Notes from last week

Placebo check in RD designs

In last week’s problem set, we had to do a placebo check in which we plot the RD estimate, \(\widehat{\tau_{RD}}\), for different values of the cut-point. The idea is that if treatment status discontinuously changes only at the cut-point \(X = 0\), then the difference in outcomes at other cut-points should be statistically indistinguishable from 0. To this end, the problem set asked us to re-estimate \(\widehat{\tau_{RD}}\) for cut-points \(c \in (-0.95, -0.90, \cdots,0, \cdots, 0.90, 0.95)\). This assumes the forcing variable, \(X_i \in [-1,1]\). Since the dataset codes DifDPct between -100 and 100, we first have to transform the forcing variable:

\[\begin{equation} X_i = \frac{\text{DifDPct}}{100} \end{equation}\]

Alternatively, redefine the sequence of cut-points to be seq(-95, 95, 5). With this transformation, the placebo check figure looks something like this:

library(tidyverse)
library(rdrobust)
library(rddensity)
library(haven)
library(estimatr)
library(ggthemes)
library(DeclareDesign)
library(knitr)
library(kableExtra)

# Download the dataset
imai <- read_dta("PSET7_RDReplication.dta")

# Subset to observations for which Use = 1, 
# and there is no missing values of X,Y, Y_pre
imai_subset <- imai %>%
  filter(Use == 1,!is.na(DifDPct),!is.na(DifDPNxt),!is.na(DifDPPrv)) %>%
  transmute(Y = DifDPNxt/100,
            X = DifDPct/100,
            Z = DifDPct > 0)

# Write a function that returns bias corrected estimate and SE
# given some cut point
rd_function = function(cutpoint){
  output = with(imai_subset, rdrobust(Y,X, c = cutpoint, all = TRUE))
  out = data.frame(output$coef[3], output$se[3])
  names(out) = c("Estimate", "SE")
  return(out)
}

# Use the map function from purrr that applies
# rd_function to every element of cutoffs
cutoffs <- seq(-0.95, 0.95, 0.05)

dat_estimates <-
  map_dfr(.x = cutoffs, .f = rd_function)

dat_estimates$Cutoff = cutoffs

# Make the plot
ggplot(dat_estimates, aes(Cutoff, Estimate)) +
  geom_point() +
  geom_errorbar(aes(ymin = Estimate - 1.96*SE, 
                    ymax = Estimate + 1.96*SE)) +
  ggtitle(label = "RD Estimate at Different Cut Points",
          subtitle = "(Data: de la Cuesta, Brandon; Imai, Kosuke (2016)") + 
  theme_economist_white(gray_bg = TRUE)

Note that the point-estimate, when the cut-point is 0, is statistically significant, but that point estimates at other specifications of the cut-point are indistinguishable from 0. This suggests that treatment status, \(D_i\), discontinously changes only at the stated cut-point.

Underpowered Density

Last week’s problem set also had a question about the McCrary density test’s power to detect sorting. To motivate this, we specified a data generating process in DeclareDesign such that units right below the cut-point (X values between -0.1 and 0) got a 0.1 “bump” to their X value with a probability \(p = 0.1\). Put another way, 10% of the units in that neighborhood sorted above the cut-point.

When we did the McCrary density test on a draw of data from this design, we were unable to detect any discontinuity in the density of \(X\) near the cut-point. The reason for this is an underpowered test - with 1,000 units and a sorting probability of 0.1, our test only detected sorting 4% of the time.

\[\begin{equation} \text{Power} = 1 - \text{Type II Error} = 1 - Pr(\text{Not Rejecting} H_0 | H_0 \text{is False}) \end{equation}\]

Where \(H_0\) implies no difference in the density of the forcing variable \(X\) near the cut-point.

Furthermore, our power calculations showed that given some sorting probability \(p\), the test was more likely to detect sorting as \(N\) increased. Here is the code and associated figure.

# Alex's McCrary density test function
density_wrapper <- 
  function(data) {
  fit <- with(data, rddensity(observed_running_variable))
  data.frame(p = fit$test$p_jk)
}

# Write a function that specifies a design with different N

design_maker = function(units){
  sorting_probability <- 0.1
  rd_design <-
  declare_population(
    N = units,
    true_running_variable = runif(N, -1, 1),
    is_just_below = as.numeric(true_running_variable < 0 & true_running_variable > -0.1),
    would_sort = rbinom(N, 1, sorting_probability),
    observed_running_variable = true_running_variable + 0.1 * is_just_below * would_sort
  ) +
  declare_estimator(handler = density_wrapper)
  return(rd_design)
}

# Calculate power for N = 1000, and 5000

design_1000 <-
  redesign(design_maker(1000), 
           sorting_probability = seq(0.0, 0.9, 0.1))

design_2000 <-
  redesign(design_maker(2000), 
           sorting_probability = seq(0.0, 0.9, 0.1))


sims1000 <- simulate_design(design_1000, sims = 100)

sims2000 <- simulate_design(design_2000, sims = 100)

results1  = sims1000 %>%
  group_by(sorting_probability) %>%
  summarise(power = mean(p < 0.05)) %>%
  mutate(N = "1000")

results2 = sims2000 %>%
  group_by(sorting_probability) %>%
  summarise(power = mean(p < 0.05)) %>%
  mutate(N = "2000")

sims = rbind(results1,results2)

# Make a figure using ggplot

ggplot(sims, aes(x = sorting_probability, y = power, color = N)) +
  geom_point(alpha = 0.3) +
  geom_smooth(se = FALSE) +
  xlab("Pr(Sorting)") +
  ylab("Statistical Power") +
  ggtitle("McCrary Density Test's Power to Detect Sorting") +
  theme_economist_white(gray_bg = T)

Fixed Effects

Fixed effects or “within” estimators are useful when some group-level invariant characteristics confound the relationship between an outcome \(Y\) and some exposure variable \(D\). In such cases, the strategy is to discard variation across units and only draw inferences based on variation within units. This way we “difference-out” the invariant characteristic.

To develop this, I will use Angrist and Pischke’s example in which we are interested in the impact of union membership on worker’s earnings.

Let \(Y_{i,t}\) be the log earnings of worker \(i\) at time \(t\), and \(D_{i,t}\) an indicator variable that captures union membership of \(i\) at time \(t\). Assume for a moment that earnings (\(Y_{i,t}\)) and union membership (\(D_{i,t}\)) are correlated with:

  1. Unobserved characteristics peculiar to each individual, that are fixed or time-invariant. Call this Ability or \(A_i\)

  2. An unobserved time shock that affects all individuals in a time period. Call this \(\lambda_t\)

  3. Some observed characteristics that vary across individuals and time periods. Call this \(X_{i,t}\)

The omitted variable bias here arises from the researcher’s inability to control for \(A_i\) and \(\lambda_t\). As I shall show, a fixed effects estimation strategy can deal with this problem.

Assumptions

Ignorability: \(D_{it}\) is “ignorable” or “as good as randomly assigned” in some time period t, conditional on \(A_i\) and \(X_{it}\) \[\begin{equation} \mathbb{E}[Y_{0it} | A_i, X_{it}, t, D_{it}] = \mathbb{E}[Y_{0it} | A_i, X_{it}, t] \end{equation}\]
Constant Additive Treatment Effect: \[\begin{equation} \mathbb{E}[Y_{1it} | A_i, X_{it}, t] = \mathbb{E}[Y_{0it} | A_i, X_{it}, t] + \rho \end{equation}\]

When potential outcomes are described in this way, observed outcomes can be written as:

\[\begin{equation} Y_{it} = \alpha + \lambda_t + \rho D_{it} + A_{i}\gamma + X_{it}\beta + \varepsilon_{it} \end{equation}\]

Where \(\alpha_i = \alpha + A_{i}\gamma\) allows a different intercept for each unit \(i\), and is the “fixed effect”. This is algebrically the same as de-meaning each variable by the individual’s average value for that variable:

\[\begin{equation} (Y_{it} - \overline{Y_{i}}) = (\alpha - \overline{\alpha}) + (\lambda_t - \overline{\lambda}) + \rho (D_{it} - \overline{D_i}) + (A_{i} - \overline{A_i})\gamma + (X_{it} - \overline{X_i}) \beta + (\varepsilon_{it} - \overline{\varepsilon_{i}}) \end{equation}\]

Now observe that \(\alpha\) and \(A_i\) are constants, therefore \(\alpha = \overline{\alpha}\) and \(A_i = \overline{A_i}\).

So we are left with an expression that is purged of \(A_i\) (time invariant individual characteristics that confound the association):

\[\begin{equation} (Y_{it} - \overline{Y_{i}}) = (\lambda_t - \overline{\lambda}) + \rho (D_{it} - \overline{D_i}) + (X_{it} - \overline{X_i}) \beta + (\varepsilon_{it} - \overline{\varepsilon_{i}}) \end{equation}\]

Example

To see this in practice, I constructed some fake data using fabricatr. I specified ability as a time-invariant individual characteristic, time_trend as a common shock that affected everyone in a time period, and time_varying_covariate as the product of these two quantities and some randomly generated noise. Finally, I defined potential outcomes and union membership as a function of these characteristics. Here is what the data looks like:

set.seed(46)

# Create some data that resembles the Union Membership-Wage case
panel_dat <- fabricate(
  individual = add_level(N = 400, ability = rnorm(N, mean = 0, sd = 0.5)),
  time = add_level(N = 4, 
                   time_trend = rnorm(N, mean = 0, sd = 0.5), 
                   nest = FALSE),
  observations = cross_levels(
    by = join(individual, time),
    time_varying_covariate = ability*time_trend + rnorm(N, 0, 0.5),
    Y0 = 10.36 + ability + time_trend + time_varying_covariate + rnorm(N, 0, 1),
    Y1 = Y0 + 0.15,
    union_membership = rbinom(N, size = 1, prob = pnorm(ability + time_trend)),
    Y = Y0*(1-union_membership) + Y1*union_membership
  )
)

panel_dat %>% arrange(individual) %>% 
  kable(format = "html", digits = 3) %>% 
  kable_styling(bootstrap_options = "striped", 
                                     full_width = FALSE, 
                                     fixed_thead = TRUE) %>% 
  scroll_box(width = "750px", height = "250px")
individual ability time time_trend observations time_varying_covariate Y0 Y1 union_membership Y
001 -0.449 1 -0.158 0001 -0.793 9.178 9.328 0 9.178
001 -0.449 2 0.369 0401 0.422 10.185 10.335 1 10.335
001 -0.449 3 1.118 0801 -0.532 12.336 12.486 1 12.486
001 -0.449 4 0.341 1201 -0.586 9.918 10.068 0 9.918
002 0.106 1 -0.158 0002 0.002 10.012 10.162 0 10.012
002 0.106 2 0.369 0402 0.159 10.040 10.190 1 10.190
002 0.106 3 1.118 0802 0.578 11.885 12.035 1 12.035
002 0.106 4 0.341 1202 0.684 11.354 11.504 1 11.504
003 -0.364 1 -0.158 0003 -0.346 9.581 9.731 1 9.731
003 -0.364 2 0.369 0403 -1.039 9.257 9.407 1 9.407
003 -0.364 3 1.118 0803 -0.188 10.978 11.128 1 11.128
003 -0.364 4 0.341 1203 1.103 11.579 11.729 1 11.729
004 0.618 1 -0.158 0004 -0.183 10.656 10.806 1 10.806
004 0.618 2 0.369 0404 0.438 12.618 12.768 0 12.618
004 0.618 3 1.118 0804 0.537 11.446 11.596 1 11.596
004 0.618 4 0.341 1204 0.154 11.612 11.762 1 11.762
005 0.584 1 -0.158 0005 -0.754 10.794 10.944 0 10.794
005 0.584 2 0.369 0405 0.139 11.380 11.530 0 11.380
005 0.584 3 1.118 0805 1.254 13.389 13.539 1 13.539
005 0.584 4 0.341 1205 -0.337 11.255 11.405 1 11.405
006 -0.311 1 -0.158 0006 0.324 9.599 9.749 0 9.599
006 -0.311 2 0.369 0406 -0.881 10.117 10.267 0 10.117
006 -0.311 3 1.118 0806 -0.269 10.011 10.161 1 10.161
006 -0.311 4 0.341 1206 0.419 9.610 9.760 0 9.610
007 0.215 1 -0.158 0007 -0.030 9.525 9.675 1 9.675
007 0.215 2 0.369 0407 -0.027 11.325 11.475 1 11.475
007 0.215 3 1.118 0807 -0.111 12.464 12.614 1 12.614
007 0.215 4 0.341 1207 -0.157 11.065 11.215 1 11.215
008 0.360 1 -0.158 0008 -0.182 11.153 11.303 0 11.153
008 0.360 2 0.369 0408 0.783 10.995 11.145 1 11.145
008 0.360 3 1.118 0808 -0.328 12.770 12.920 1 12.920
008 0.360 4 0.341 1208 -0.763 9.923 10.073 1 10.073
009 0.850 1 -0.158 0009 0.154 11.994 12.144 1 12.144
009 0.850 2 0.369 0409 -0.126 10.846 10.996 1 10.996
009 0.850 3 1.118 0809 0.198 12.052 12.202 1 12.202
009 0.850 4 0.341 1209 -0.030 12.151 12.301 1 12.301
010 0.123 1 -0.158 0010 0.230 10.004 10.154 0 10.004
010 0.123 2 0.369 0410 0.851 13.409 13.559 1 13.559
010 0.123 3 1.118 0810 0.770 13.617 13.767 1 13.767
010 0.123 4 0.341 1210 -0.067 9.422 9.572 0 9.422
011 0.370 1 -0.158 0011 -0.232 11.737 11.887 0 11.737
011 0.370 2 0.369 0411 -0.297 9.695 9.845 1 9.845
011 0.370 3 1.118 0811 1.260 12.856 13.006 1 13.006
011 0.370 4 0.341 1211 0.394 10.152 10.302 0 10.152
012 1.100 1 -0.158 0012 -0.564 8.812 8.962 0 8.812
012 1.100 2 0.369 0412 -0.035 12.271 12.421 1 12.421
012 1.100 3 1.118 0812 1.056 15.045 15.195 1 15.195
012 1.100 4 0.341 1212 0.067 12.296 12.446 1 12.446
013 -0.962 1 -0.158 0013 0.568 9.056 9.206 0 9.056
013 -0.962 2 0.369 0413 -0.381 10.552 10.702 0 10.552
013 -0.962 3 1.118 0813 -1.022 10.165 10.315 0 10.165
013 -0.962 4 0.341 1213 -1.378 7.451 7.601 0 7.451
014 -0.186 1 -0.158 0014 0.046 11.004 11.154 0 11.004
014 -0.186 2 0.369 0414 -1.240 10.796 10.946 1 10.946
014 -0.186 3 1.118 0814 0.180 12.252 12.402 1 12.402
014 -0.186 4 0.341 1214 0.043 10.772 10.922 1 10.922
015 -0.105 1 -0.158 0015 -0.411 9.581 9.731 0 9.581
015 -0.105 2 0.369 0415 0.003 11.131 11.281 0 11.131
015 -0.105 3 1.118 0815 -0.819 13.071 13.221 1 13.221
015 -0.105 4 0.341 1215 0.275 12.603 12.753 1 12.753
016 -0.460 1 -0.158 0016 -0.566 8.640 8.790 0 8.640
016 -0.460 2 0.369 0416 0.138 8.880 9.030 1 9.030
016 -0.460 3 1.118 0816 -0.553 8.141 8.291 1 8.291
016 -0.460 4 0.341 1216 -0.502 10.652 10.802 0 10.652
017 0.031 1 -0.158 0017 0.183 9.308 9.458 1 9.458
017 0.031 2 0.369 0417 -0.246 10.923 11.073 1 11.073
017 0.031 3 1.118 0817 -0.200 11.895 12.045 1 12.045
017 0.031 4 0.341 1217 0.209 11.517 11.667 0 11.517
018 0.280 1 -0.158 0018 0.428 9.672 9.822 0 9.672
018 0.280 2 0.369 0418 -0.214 10.276 10.426 0 10.276
018 0.280 3 1.118 0818 0.369 13.289 13.439 1 13.439
018 0.280 4 0.341 1218 0.378 11.893 12.043 1 12.043
019 -0.199 1 -0.158 0019 0.386 11.429 11.579 1 11.579
019 -0.199 2 0.369 0419 0.526 10.853 11.003 0 10.853
019 -0.199 3 1.118 0819 0.382 12.314 12.464 1 12.464
019 -0.199 4 0.341 1219 -0.202 11.591 11.741 0 11.591
020 0.210 1 -0.158 0020 -0.034 7.762 7.912 0 7.762
020 0.210 2 0.369 0420 -0.483 10.054 10.204 1 10.204
020 0.210 3 1.118 0820 0.572 10.136 10.286 1 10.286
020 0.210 4 0.341 1220 0.684 10.693 10.843 0 10.693
021 -0.205 1 -0.158 0021 0.634 10.580 10.730 1 10.730
021 -0.205 2 0.369 0421 0.122 9.893 10.043 0 9.893
021 -0.205 3 1.118 0821 0.160 12.328 12.478 1 12.478
021 -0.205 4 0.341 1221 -0.302 10.894 11.044 1 11.044
022 -0.391 1 -0.158 0022 -0.230 9.739 9.889 1 9.889
022 -0.391 2 0.369 0422 -0.785 10.371 10.521 0 10.371
022 -0.391 3 1.118 0822 0.901 12.661 12.811 0 12.661
022 -0.391 4 0.341 1222 -0.529 9.698 9.848 1 9.848
023 -0.006 1 -0.158 0023 -0.386 10.085 10.235 1 10.235
023 -0.006 2 0.369 0423 -0.563 10.427 10.577 1 10.577
023 -0.006 3 1.118 0823 -0.131 9.692 9.842 1 9.842
023 -0.006 4 0.341 1223 -0.833 10.831 10.981 0 10.831
024 0.811 1 -0.158 0024 -0.116 11.206 11.356 1 11.356
024 0.811 2 0.369 0424 0.180 12.151 12.301 1 12.301
024 0.811 3 1.118 0824 0.965 13.021 13.171 1 13.171
024 0.811 4 0.341 1224 0.077 10.274 10.424 1 10.424
025 -0.171 1 -0.158 0025 0.265 8.751 8.901 0 8.751
025 -0.171 2 0.369 0425 -0.696 8.755 8.905 0 8.755
025 -0.171 3 1.118 0825 -0.429 10.700 10.850 1 10.850
025 -0.171 4 0.341 1225 -0.173 10.222 10.372 1 10.372
026 -0.431 1 -0.158 0026 1.258 12.572 12.722 0 12.572
026 -0.431 2 0.369 0426 -0.342 11.618 11.768 1 11.768
026 -0.431 3 1.118 0826 0.730 9.337 9.487 1 9.487
026 -0.431 4 0.341 1226 0.508 10.719 10.869 1 10.869
027 -0.198 1 -0.158 0027 0.180 10.613 10.763 0 10.613
027 -0.198 2 0.369 0427 1.069 13.566 13.716 1 13.716
027 -0.198 3 1.118 0827 0.050 10.521 10.671 1 10.671
027 -0.198 4 0.341 1227 0.196 10.886 11.036 0 10.886
028 0.222 1 -0.158 0028 0.401 10.958 11.108 1 11.108
028 0.222 2 0.369 0428 0.194 10.765 10.915 1 10.915
028 0.222 3 1.118 0828 0.209 11.646 11.796 1 11.796
028 0.222 4 0.341 1228 1.109 11.908 12.058 1 12.058
029 -0.694 1 -0.158 0029 0.336 10.255 10.405 1 10.405
029 -0.694 2 0.369 0429 -0.151 10.191 10.341 1 10.341
029 -0.694 3 1.118 0829 -0.887 10.712 10.862 1 10.862
029 -0.694 4 0.341 1229 -0.320 8.602 8.752 1 8.752
030 0.368 1 -0.158 0030 0.292 10.613 10.763 0 10.613
030 0.368 2 0.369 0430 -0.316 9.795 9.945 1 9.945
030 0.368 3 1.118 0830 0.610 12.742 12.892 1 12.892
030 0.368 4 0.341 1230 -0.688 11.340 11.490 1 11.490
031 -0.357 1 -0.158 0031 -0.378 9.022 9.172 1 9.172
031 -0.357 2 0.369 0431 -0.032 10.670 10.820 1 10.820
031 -0.357 3 1.118 0831 0.409 9.720 9.870 1 9.870
031 -0.357 4 0.341 1231 -0.425 9.635 9.785 1 9.785
032 0.146 1 -0.158 0032 -0.626 9.979 10.129 1 10.129
032 0.146 2 0.369 0432 0.599 11.884 12.034 1 12.034
032 0.146 3 1.118 0832 -1.087 9.110 9.260 1 9.260
032 0.146 4 0.341 1232 -0.415 10.132 10.282 0 10.132
033 -0.241 1 -0.158 0033 -0.404 9.260 9.410 0 9.260
033 -0.241 2 0.369 0433 0.533 10.825 10.975 0 10.825
033 -0.241 3 1.118 0833 -0.569 10.970 11.120 1 11.120
033 -0.241 4 0.341 1233 -0.295 10.913 11.063 0 10.913
034 -0.219 1 -0.158 0034 -0.642 8.680 8.830 1 8.830
034 -0.219 2 0.369 0434 0.319 11.986 12.136 0 11.986
034 -0.219 3 1.118 0834 -0.182 10.255 10.405 1 10.405
034 -0.219 4 0.341 1234 -0.295 9.611 9.761 0 9.611
035 0.499 1 -0.158 0035 -0.072 9.877 10.027 1 10.027
035 0.499 2 0.369 0435 -0.644 12.260 12.410 1 12.410
035 0.499 3 1.118 0835 1.023 14.460 14.610 1 14.610
035 0.499 4 0.341 1235 -0.384 9.205 9.355 1 9.355
036 -1.259 1 -0.158 0036 0.226 8.713 8.863 0 8.713
036 -1.259 2 0.369 0436 -1.012 9.479 9.629 1 9.629
036 -1.259 3 1.118 0836 -0.497 11.951 12.101 0 11.951
036 -1.259 4 0.341 1236 -0.568 8.588 8.738 1 8.738
037 0.960 1 -0.158 0037 -0.203 10.522 10.672 1 10.672
037 0.960 2 0.369 0437 0.402 12.542 12.692 1 12.692
037 0.960 3 1.118 0837 1.020 12.486 12.636 1 12.636
037 0.960 4 0.341 1237 0.516 12.172 12.322 1 12.322
038 -0.151 1 -0.158 0038 0.496 12.039 12.189 0 12.039
038 -0.151 2 0.369 0438 -1.226 11.005 11.155 1 11.155
038 -0.151 3 1.118 0838 -1.073 10.417 10.567 0 10.417
038 -0.151 4 0.341 1238 -0.424 9.575 9.725 0 9.575
039 -0.337 1 -0.158 0039 -0.686 10.057 10.207 0 10.057
039 -0.337 2 0.369 0439 -0.392 11.911 12.061 1 12.061
039 -0.337 3 1.118 0839 0.312 11.171 11.321 1 11.321
039 -0.337 4 0.341 1239 -1.392 8.842 8.992 0 8.842
040 1.047 1 -0.158 0040 0.011 10.966 11.116 1 11.116
040 1.047 2 0.369 0440 -0.287 11.061 11.211 1 11.211
040 1.047 3 1.118 0840 1.527 14.175 14.325 1 14.325
040 1.047 4 0.341 1240 -0.531 11.812 11.962 1 11.962
041 0.374 1 -0.158 0041 -0.153 11.257 11.407 1 11.407
041 0.374 2 0.369 0441 0.664 11.140 11.290 1 11.290
041 0.374 3 1.118 0841 0.091 11.631 11.781 1 11.781
041 0.374 4 0.341 1241 0.094 9.864 10.014 1 10.014
042 0.340 1 -0.158 0042 -0.306 10.394 10.544 1 10.544
042 0.340 2 0.369 0442 0.194 13.222 13.372 1 13.372
042 0.340 3 1.118 0842 -0.444 10.874 11.024 1 11.024
042 0.340 4 0.341 1242 0.330 13.517 13.667 1 13.667
043 -0.671 1 -0.158 0043 0.510 11.609 11.759 0 11.609
043 -0.671 2 0.369 0443 0.583 9.776 9.926 1 9.926
043 -0.671 3 1.118 0843 0.057 10.666 10.816 1 10.816
043 -0.671 4 0.341 1243 -0.808 8.583 8.733 0 8.583
044 -0.054 1 -0.158 0044 0.902 11.346 11.496 0 11.346
044 -0.054 2 0.369 0444 -0.561 11.432 11.582 1 11.582
044 -0.054 3 1.118 0844 -0.362 11.729 11.879 1 11.879
044 -0.054 4 0.341 1244 -0.501 10.062 10.212 0 10.062
045 0.736 1 -0.158 0045 0.032 10.427 10.577 0 10.427
045 0.736 2 0.369 0445 -0.187 10.455 10.605 1 10.605
045 0.736 3 1.118 0845 0.355 12.184 12.334 1 12.334
045 0.736 4 0.341 1245 -0.389 10.287 10.437 1 10.437
046 -0.179 1 -0.158 0046 0.521 9.541 9.691 0 9.541
046 -0.179 2 0.369 0446 0.280 12.013 12.163 1 12.163
046 -0.179 3 1.118 0846 0.456 13.009 13.159 0 13.009
046 -0.179 4 0.341 1246 0.449 11.637 11.787 0 11.637
047 0.642 1 -0.158 0047 0.113 9.016 9.166 0 9.016
047 0.642 2 0.369 0447 -0.106 11.498 11.648 1 11.648
047 0.642 3 1.118 0847 1.208 13.110 13.260 1 13.260
047 0.642 4 0.341 1247 -0.124 10.260 10.410 1 10.410
048 -1.067 1 -0.158 0048 0.685 9.977 10.127 0 9.977
048 -1.067 2 0.369 0448 -0.487 7.789 7.939 0 7.789
048 -1.067 3 1.118 0848 -0.257 10.231 10.381 1 10.381
048 -1.067 4 0.341 1248 -0.389 10.333 10.483 0 10.333
049 0.086 1 -0.158 0049 -0.117 11.107 11.257 1 11.257
049 0.086 2 0.369 0449 -0.398 11.450 11.600 1 11.600
049 0.086 3 1.118 0849 0.107 11.551 11.701 1 11.701
049 0.086 4 0.341 1249 -1.138 9.910 10.060 1 10.060
050 -0.349 1 -0.158 0050 -0.138 10.097 10.247 0 10.097
050 -0.349 2 0.369 0450 -0.536 7.474 7.624 1 7.624
050 -0.349 3 1.118 0850 -0.997 7.100 7.250 0 7.100
050 -0.349 4 0.341 1250 -0.193 8.377 8.527 1 8.527
051 0.378 1 -0.158 0051 0.005 11.669 11.819 0 11.669
051 0.378 2 0.369 0451 -0.219 13.150 13.300 0 13.150
051 0.378 3 1.118 0851 0.178 10.134 10.284 1 10.284
051 0.378 4 0.341 1251 -0.236 10.797 10.947 0 10.797
052 -0.099 1 -0.158 0052 0.063 9.881 10.031 0 9.881
052 -0.099 2 0.369 0452 0.511 9.581 9.731 0 9.581
052 -0.099 3 1.118 0852 -0.721 11.451 11.601 1 11.601
052 -0.099 4 0.341 1252 0.267 10.700 10.850 1 10.850
053 -0.319 1 -0.158 0053 0.133 9.435 9.585 1 9.585
053 -0.319 2 0.369 0453 -0.458 10.234 10.384 0 10.234
053 -0.319 3 1.118 0853 0.472 11.458 11.608 1 11.608
053 -0.319 4 0.341 1253 0.366 10.802 10.952 0 10.802
054 0.221 1 -0.158 0054 0.618 9.012 9.162 1 9.162
054 0.221 2 0.369 0454 -0.259 10.613 10.763 1 10.763
054 0.221 3 1.118 0854 0.114 10.218 10.368 1 10.368
054 0.221 4 0.341 1254 -0.799 9.340 9.490 1 9.490
055 0.307 1 -0.158 0055 0.270 12.176 12.326 1 12.326
055 0.307 2 0.369 0455 0.899 11.613 11.763 1 11.763
055 0.307 3 1.118 0855 -0.079 10.992 11.142 1 11.142
055 0.307 4 0.341 1255 0.885 12.099 12.249 0 12.099
056 -0.241 1 -0.158 0056 -0.336 9.500 9.650 0 9.500
056 -0.241 2 0.369 0456 -0.176 9.730 9.880 0 9.730
056 -0.241 3 1.118 0856 0.271 11.393 11.543 1 11.543
056 -0.241 4 0.341 1256 0.145 10.744 10.894 1 10.894
057 0.319 1 -0.158 0057 -0.923 9.950 10.100 0 9.950
057 0.319 2 0.369 0457 -0.499 10.908 11.058 0 10.908
057 0.319 3 1.118 0857 0.401 13.329 13.479 1 13.479
057 0.319 4 0.341 1257 0.357 12.374 12.524 0 12.374
058 -0.250 1 -0.158 0058 0.899 10.582 10.732 1 10.732
058 -0.250 2 0.369 0458 -0.757 8.203 8.353 1 8.353
058 -0.250 3 1.118 0858 0.259 11.866 12.016 1 12.016
058 -0.250 4 0.341 1258 0.902 11.060 11.210 1 11.210
059 0.284 1 -0.158 0059 0.886 10.010 10.160 0 10.010
059 0.284 2 0.369 0459 0.578 13.852 14.002 0 13.852
059 0.284 3 1.118 0859 -0.465 9.633 9.783 1 9.783
059 0.284 4 0.341 1259 -0.601 10.257 10.407 0 10.257
060 -0.319 1 -0.158 0060 0.127 9.601 9.751 0 9.601
060 -0.319 2 0.369 0460 0.365 10.542 10.692 1 10.692
060 -0.319 3 1.118 0860 -0.577 9.981 10.131 1 10.131
060 -0.319 4 0.341 1260 -0.035 10.593 10.743 0 10.593
061 0.160 1 -0.158 0061 -0.328 9.301 9.451 1 9.451
061 0.160 2 0.369 0461 0.604 9.805 9.955 1 9.955
061 0.160 3 1.118 0861 0.137 11.073 11.223 0 11.073
061 0.160 4 0.341 1261 0.240 10.753 10.903 1 10.903
062 0.309 1 -0.158 0062 0.303 11.074 11.224 0 11.074
062 0.309 2 0.369 0462 0.639 13.768 13.918 1 13.918
062 0.309 3 1.118 0862 -0.107 9.558 9.708 1 9.708
062 0.309 4 0.341 1262 -0.310 12.031 12.181 1 12.181
063 1.114 1 -0.158 0063 0.631 9.554 9.704 1 9.704
063 1.114 2 0.369 0463 0.059 11.504 11.654 1 11.654
063 1.114 3 1.118 0863 0.962 14.420 14.570 1 14.570
063 1.114 4 0.341 1263 0.484 11.959 12.109 1 12.109
064 -0.290 1 -0.158 0064 0.286 10.551 10.701 1 10.701
064 -0.290 2 0.369 0464 -0.191 11.286 11.436 1 11.436
064 -0.290 3 1.118 0864 -0.642 9.197 9.347 1 9.347
064 -0.290 4 0.341 1264 0.087 10.795 10.945 0 10.795
065 0.088 1 -0.158 0065 0.794 12.372 12.522 1 12.522
065 0.088 2 0.369 0465 -0.507 10.811 10.961 1 10.961
065 0.088 3 1.118 0865 -0.323 11.229 11.379 1 11.379
065 0.088 4 0.341 1265 -0.953 10.814 10.964 1 10.964
066 1.134 1 -0.158 0066 -0.580 11.326 11.476 0 11.326
066 1.134 2 0.369 0466 1.588 13.017 13.167 1 13.167
066 1.134 3 1.118 0866 2.187 15.000 15.150 1 15.150
066 1.134 4 0.341 1266 0.018 12.827 12.977 1 12.977
067 0.691 1 -0.158 0067 -0.429 10.204 10.354 1 10.354
067 0.691 2 0.369 0467 -0.131 10.896 11.046 1 11.046
067 0.691 3 1.118 0867 1.244 11.674 11.824 0 11.674
067 0.691 4 0.341 1267 0.217 12.063 12.213 1 12.213
068 0.169 1 -0.158 0068 0.093 11.114 11.264 0 11.114
068 0.169 2 0.369 0468 0.531 11.935 12.085 0 11.935
068 0.169 3 1.118 0868 0.270 11.635 11.785 1 11.785
068 0.169 4 0.341 1268 -0.593 10.973 11.123 0 10.973
069 0.045 1 -0.158 0069 0.362 9.819 9.969 0 9.819
069 0.045 2 0.369 0469 0.160 11.003 11.153 1 11.153
069 0.045 3 1.118 0869 -0.140 12.076 12.226 1 12.226
069 0.045 4 0.341 1269 -0.911 10.183 10.333 1 10.333
070 0.008 1 -0.158 0070 0.152 10.976 11.126 0 10.976
070 0.008 2 0.369 0470 -0.303 10.684 10.834 1 10.834
070 0.008 3 1.118 0870 -0.394 11.863 12.013 1 12.013
070 0.008 4 0.341 1270 -0.061 9.878 10.028 1 10.028
071 -0.221 1 -0.158 0071 -0.459 10.305 10.455 0 10.305
071 -0.221 2 0.369 0471 -0.370 9.996 10.146 1 10.146
071 -0.221 3 1.118 0871 -0.314 9.839 9.989 1 9.989
071 -0.221 4 0.341 1271 -0.183 10.716 10.866 1 10.866
072 -0.353 1 -0.158 0072 0.191 9.569 9.719 0 9.569
072 -0.353 2 0.369 0472 -0.016 9.945 10.095 1 10.095
072 -0.353 3 1.118 0872 -0.943 10.096 10.246 1 10.246
072 -0.353 4 0.341 1272 0.014 10.967 11.117 1 11.117
073 0.252 1 -0.158 0073 -0.012 8.520 8.670 0 8.520
073 0.252 2 0.369 0473 0.574 10.941 11.091 0 10.941
073 0.252 3 1.118 0873 -0.597 8.139 8.289 1 8.289
073 0.252 4 0.341 1273 0.038 10.966 11.116 1 11.116
074 -0.161 1 -0.158 0074 -0.053 10.276 10.426 1 10.426
074 -0.161 2 0.369 0474 -0.609 9.282 9.432 0 9.282
074 -0.161 3 1.118 0874 0.290 12.465 12.615 0 12.465
074 -0.161 4 0.341 1274 -0.341 11.301 11.451 1 11.451
075 -0.476 1 -0.158 0075 0.280 10.316 10.466 1 10.466
075 -0.476 2 0.369 0475 -0.200 9.336 9.486 0 9.336
075 -0.476 3 1.118 0875 -1.170 11.142 11.292 0 11.142
075 -0.476 4 0.341 1275 -0.437 9.741 9.891 1 9.891
076 -0.307 1 -0.158 0076 -1.056 10.595 10.745 0 10.595
076 -0.307 2 0.369 0476 -0.688 9.390 9.540 1 9.540
076 -0.307 3 1.118 0876 -0.172 11.876 12.026 1 12.026
076 -0.307 4 0.341 1276 0.518 9.946 10.096 1 10.096
077 -0.262 1 -0.158 0077 0.076 8.504 8.654 0 8.504
077 -0.262 2 0.369 0477 -0.160 11.494 11.644 1 11.644
077 -0.262 3 1.118 0877 0.333 11.865 12.015 1 12.015
077 -0.262 4 0.341 1277 -0.416 10.399 10.549 1 10.549
078 -0.639 1 -0.158 0078 0.016 10.594 10.744 0 10.594
078 -0.639 2 0.369 0478 -0.112 10.119 10.269 0 10.119
078 -0.639 3 1.118 0878 -1.252 8.794 8.944 0 8.794
078 -0.639 4 0.341 1278 -0.099 10.893 11.043 0 10.893
079 0.264 1 -0.158 0079 -0.595 10.030 10.180 0 10.030
079 0.264 2 0.369 0479 -0.538 9.856 10.006 1 10.006
079 0.264 3 1.118 0879 -0.134 11.369 11.519 1 11.519
079 0.264 4 0.341 1279 0.282 9.088 9.238 1 9.238
080 -0.602 1 -0.158 0080 0.093 10.171 10.321 0 10.171
080 -0.602 2 0.369 0480 0.270 9.829 9.979 0 9.829
080 -0.602 3 1.118 0880 -0.037 9.962 10.112 1 10.112
080 -0.602 4 0.341 1280 -0.466 8.707 8.857 0 8.707
081 0.272 1 -0.158 0081 0.574 13.035 13.185 1 13.185
081 0.272 2 0.369 0481 0.626 11.152 11.302 1 11.302
081 0.272 3 1.118 0881 0.676 12.501 12.651 1 12.651
081 0.272 4 0.341 1281 -0.066 10.434 10.584 1 10.584
082 0.168 1 -0.158 0082 0.143 12.176 12.326 1 12.326
082 0.168 2 0.369 0482 1.088 14.758 14.908 1 14.908
082 0.168 3 1.118 0882 -0.144 10.694 10.844 1 10.844
082 0.168 4 0.341 1282 -0.121 11.646 11.796 1 11.796
083 -0.539 1 -0.158 0083 -0.128 10.130 10.280 0 10.130
083 -0.539 2 0.369 0483 -0.511 8.165 8.315 0 8.165
083 -0.539 3 1.118 0883 -0.699 12.005 12.155 1 12.155
083 -0.539 4 0.341 1283 -0.991 11.177 11.327 0 11.177
084 -0.245 1 -0.158 0084 0.386 9.507 9.657 1 9.657
084 -0.245 2 0.369 0484 0.275 13.239 13.389 1 13.389
084 -0.245 3 1.118 0884 -0.301 11.916 12.066 1 12.066
084 -0.245 4 0.341 1284 -0.372 9.852 10.002 1 10.002
085 0.317 1 -0.158 0085 -0.632 9.499 9.649 1 9.649
085 0.317 2 0.369 0485 0.103 11.151 11.301 1 11.301
085 0.317 3 1.118 0885 0.965 13.374 13.524 1 13.524
085 0.317 4 0.341 1285 -0.142 10.339 10.489 0 10.339
086 -1.028 1 -0.158 0086 0.243 10.167 10.317 0 10.167
086 -1.028 2 0.369 0486 -0.360 9.181 9.331 0 9.181
086 -1.028 3 1.118 0886 -0.582 9.511 9.661 0 9.511
086 -1.028 4 0.341 1286 -0.539 9.902 10.052 1 10.052
087 -0.653 1 -0.158 0087 -0.292 7.644 7.794 1 7.794
087 -0.653 2 0.369 0487 -0.639 9.392 9.542 1 9.542
087 -0.653 3 1.118 0887 -0.942 8.370 8.520 0 8.370
087 -0.653 4 0.341 1287 -0.104 9.411 9.561 0 9.411
088 -0.473 1 -0.158 0088 0.149 10.023 10.173 1 10.173
088 -0.473 2 0.369 0488 0.287 10.604 10.754 0 10.604
088 -0.473 3 1.118 0888 -0.084 10.671 10.821 1 10.821
088 -0.473 4 0.341 1288 -0.696 10.706 10.856 0 10.706
089 0.028 1 -0.158 0089 -0.243 9.246 9.396 1 9.396
089 0.028 2 0.369 0489 -0.756 10.441 10.591 1 10.591
089 0.028 3 1.118 0889 0.172 12.018 12.168 1 12.168
089 0.028 4 0.341 1289 0.018 8.988 9.138 0 8.988
090 0.206 1 -0.158 0090 -0.181 9.153 9.303 0 9.153
090 0.206 2 0.369 0490 -0.545 9.320 9.470 1 9.470
090 0.206 3 1.118 0890 0.279 12.919 13.069 1 13.069
090 0.206 4 0.341 1290 0.242 11.573 11.723 1 11.723
091 -0.723 1 -0.158 0091 -0.184 9.159 9.309 1 9.309
091 -0.723 2 0.369 0491 -0.126 10.669 10.819 0 10.669
091 -0.723 3 1.118 0891 -0.716 9.855 10.005 0 9.855
091 -0.723 4 0.341 1291 -1.019 8.634 8.784 1 8.784
092 0.039 1 -0.158 0092 0.330 11.179 11.329 1 11.329
092 0.039 2 0.369 0492 -0.421 9.099 9.249 1 9.249
092 0.039 3 1.118 0892 0.604 12.867 13.017 1 13.017
092 0.039 4 0.341 1292 -0.246 11.186 11.336 1 11.336
093 0.413 1 -0.158 0093 -0.154 10.720 10.870 1 10.870
093 0.413 2 0.369 0493 1.242 11.146 11.296 1 11.296
093 0.413 3 1.118 0893 -0.641 11.364 11.514 1 11.514
093 0.413 4 0.341 1293 -1.026 10.407 10.557 1 10.557
094 0.205 1 -0.158 0094 0.209 10.565 10.715 1 10.715
094 0.205 2 0.369 0494 -0.014 11.590 11.740 1 11.740
094 0.205 3 1.118 0894 0.160 12.193 12.343 0 12.193
094 0.205 4 0.341 1294 -0.010 10.896 11.046 0 10.896
095 0.212 1 -0.158 0095 0.039 11.854 12.004 1 12.004
095 0.212 2 0.369 0495 -1.393 10.127 10.277 0 10.127
095 0.212 3 1.118 0895 -0.896 11.798 11.948 1 11.948
095 0.212 4 0.341 1295 0.057 9.056 9.206 1 9.206
096 -0.212 1 -0.158 0096 -0.021 10.109 10.259 1 10.259
096 -0.212 2 0.369 0496 0.156 11.325 11.475 1 11.475
096 -0.212 3 1.118 0896 0.283 11.002 11.152 1 11.152
096 -0.212 4 0.341 1296 -0.354 10.665 10.815 1 10.815
097 0.307 1 -0.158 0097 -0.186 10.090 10.240 1 10.240
097 0.307 2 0.369 0497 -0.877 11.492 11.642 1 11.642
097 0.307 3 1.118 0897 0.798 13.318 13.468 1 13.468
097 0.307 4 0.341 1297 -0.197 10.774 10.924 0 10.774
098 0.375 1 -0.158 0098 -0.669 10.784 10.934 0 10.784
098 0.375 2 0.369 0498 0.098 12.534 12.684 1 12.684
098 0.375 3 1.118 0898 0.675 13.176 13.326 1 13.326
098 0.375 4 0.341 1298 0.253 10.653 10.803 1 10.803
099 -0.477 1 -0.158 0099 -1.092 10.100 10.250 0 10.100
099 -0.477 2 0.369 0499 0.094 10.625 10.775 0 10.625
099 -0.477 3 1.118 0899 -0.717 11.400 11.550 0 11.400
099 -0.477 4 0.341 1299 -0.386 7.815 7.965 0 7.815
100 0.212 1 -0.158 0100 0.172 10.854 11.004 0 10.854
100 0.212 2 0.369 0500 0.878 11.159 11.309 1 11.309
100 0.212 3 1.118 0900 -0.175 12.017 12.167 1 12.167
100 0.212 4 0.341 1300 -0.810 10.126 10.276 0 10.126
101 -1.542 1 -0.158 0101 0.301 9.138 9.288 0 9.138
101 -1.542 2 0.369 0501 -1.081 7.399 7.549 0 7.399
101 -1.542 3 1.118 0901 -1.086 8.003 8.153 0 8.003
101 -1.542 4 0.341 1301 -0.191 9.909 10.059 1 10.059
102 1.221 1 -0.158 0102 -0.373 11.270 11.420 1 11.420
102 1.221 2 0.369 0502 0.280 13.442 13.592 1 13.592
102 1.221 3 1.118 0902 1.532 13.214 13.364 1 13.364
102 1.221 4 0.341 1302 -0.573 10.313 10.463 1 10.463
103 0.234 1 -0.158 0103 -0.507 8.950 9.100 1 9.100
103 0.234 2 0.369 0503 0.700 11.742 11.892 1 11.892
103 0.234 3 1.118 0903 0.050 12.525 12.675 1 12.675
103 0.234 4 0.341 1303 -0.471 9.377 9.527 1 9.527
104 0.780 1 -0.158 0104 -0.220 10.603 10.753 1 10.753
104 0.780 2 0.369 0504 0.255 12.342 12.492 1 12.492
104 0.780 3 1.118 0904 1.580 14.228 14.378 1 14.378
104 0.780 4 0.341 1304 -0.117 10.985 11.135 1 11.135
105 1.155 1 -0.158 0105 -0.440 11.229 11.379 1 11.379
105 1.155 2 0.369 0505 0.261 12.207 12.357 1 12.357
105 1.155 3 1.118 0905 1.399 13.452 13.602 1 13.602
105 1.155 4 0.341 1305 -0.204 10.161 10.311 1 10.311
106 0.657 1 -0.158 0106 0.692 11.809 11.959 1 11.959
106 0.657 2 0.369 0506 0.361 12.126 12.276 1 12.276
106 0.657 3 1.118 0906 0.249 11.969 12.119 1 12.119
106 0.657 4 0.341 1306 0.437 13.065 13.215 1 13.215
107 -0.136 1 -0.158 0107 0.406 9.869 10.019 0 9.869
107 -0.136 2 0.369 0507 -0.230 8.165 8.315 1 8.315
107 -0.136 3 1.118 0907 -1.036 10.367 10.517 1 10.517
107 -0.136 4 0.341 1307 1.018 9.940 10.090 0 9.940
108 -0.242 1 -0.158 0108 0.240 10.344 10.494 1 10.494
108 -0.242 2 0.369 0508 0.332 10.307 10.457 0 10.307
108 -0.242 3 1.118 0908 -0.331 9.933 10.083 1 10.083
108 -0.242 4 0.341 1308 0.258 11.798 11.948 1 11.948
109 0.165 1 -0.158 0109 -0.342 9.014 9.164 1 9.164
109 0.165 2 0.369 0509 -0.741 8.680 8.830 1 8.830
109 0.165 3 1.118 0909 0.340 10.692 10.842 1 10.842
109 0.165 4 0.341 1309 -0.219 9.125 9.275 0 9.125
110 -0.114 1 -0.158 0110 -0.523 9.863 10.013 0 9.863
110 -0.114 2 0.369 0510 0.277 11.929 12.079 1 12.079
110 -0.114 3 1.118 0910 0.401 11.730 11.880 1 11.880
110 -0.114 4 0.341 1310 -0.276 11.609 11.759 0 11.609
111 0.700 1 -0.158 0111 0.516 11.511 11.661 1 11.661
111 0.700 2 0.369 0511 0.114 10.716 10.866 1 10.866
111 0.700 3 1.118 0911 1.041 14.101 14.251 1 14.251
111 0.700 4 0.341 1311 0.268 12.532 12.682 1 12.682
112 0.644 1 -0.158 0112 0.242 11.848 11.998 1 11.998
112 0.644 2 0.369 0512 -0.254 11.455 11.605 1 11.605
112 0.644 3 1.118 0912 1.086 14.751 14.901 1 14.901
112 0.644 4 0.341 1312 0.441 12.186 12.336 0 12.186
113 0.482 1 -0.158 0113 0.561 10.229 10.379 1 10.379
113 0.482 2 0.369 0513 -0.884 12.052 12.202 1 12.202
113 0.482 3 1.118 0913 0.052 12.770 12.920 1 12.920
113 0.482 4 0.341 1313 0.043 10.573 10.723 0 10.573
114 0.060 1 -0.158 0114 0.103 10.057 10.207 1 10.207
114 0.060 2 0.369 0514 -0.756 11.016 11.166 1 11.166
114 0.060 3 1.118 0914 -0.473 10.171 10.321 1 10.321
114 0.060 4 0.341 1314 -0.124 13.096 13.246 1 13.246
115 0.351 1 -0.158 0115 -0.487 11.466 11.616 1 11.616
115 0.351 2 0.369 0515 0.614 9.949 10.099 1 10.099
115 0.351 3 1.118 0915 0.080 13.072 13.222 1 13.222
115 0.351 4 0.341 1315 0.408 11.945 12.095 0 11.945
116 0.056 1 -0.158 0116 0.851 11.088 11.238 1 11.238
116 0.056 2 0.369 0516 0.272 10.327 10.477 0 10.327
116 0.056 3 1.118 0916 1.058 14.878 15.028 1 15.028
116 0.056 4 0.341 1316 -0.639 10.535 10.685 1 10.685
117 -0.006 1 -0.158 0117 0.478 10.527 10.677 0 10.527
117 -0.006 2 0.369 0517 -0.527 9.543 9.693 0 9.543
117 -0.006 3 1.118 0917 -0.204 10.870 11.020 1 11.020
117 -0.006 4 0.341 1317 -0.877 8.721 8.871 1 8.871
118 -0.103 1 -0.158 0118 0.519 9.665 9.815 0 9.665
118 -0.103 2 0.369 0518 -0.812 9.538 9.688 0 9.538
118 -0.103 3 1.118 0918 0.496 11.890 12.040 1 12.040
118 -0.103 4 0.341 1318 -0.170 12.233 12.383 1 12.383
119 -0.814 1 -0.158 0119 0.355 10.268 10.418 0 10.268
119 -0.814 2 0.369 0519 -0.174 10.058 10.208 0 10.058
119 -0.814 3 1.118 0919 -0.432 9.920 10.070 1 10.070
119 -0.814 4 0.341 1319 -0.277 9.757 9.907 0 9.757
120 -1.064 1 -0.158 0120 0.914 8.456 8.606 0 8.456
120 -1.064 2 0.369 0520 -1.276 7.113 7.263 0 7.113
120 -1.064 3 1.118 0920 -1.104 9.017 9.167 0 9.017
120 -1.064 4 0.341 1320 -0.959 9.659 9.809 1 9.809
121 0.287 1 -0.158 0121 -0.220 12.202 12.352 1 12.352
121 0.287 2 0.369 0521 0.197 12.542 12.692 1 12.692
121 0.287 3 1.118 0921 1.001 12.467 12.617 1 12.617
121 0.287 4 0.341 1321 -0.472 9.555 9.705 0 9.555
122 0.006 1 -0.158 0122 -0.553 10.034 10.184 0 10.034
122 0.006 2 0.369 0522 0.533 11.601 11.751 1 11.751
122 0.006 3 1.118 0922 0.458 12.558 12.708 1 12.708
122 0.006 4 0.341 1322 -0.010 10.728 10.878 0 10.728
123 -0.777 1 -0.158 0123 0.694 9.231 9.381 0 9.231
123 -0.777 2 0.369 0523 -0.216 9.566 9.716 0 9.566
123 -0.777 3 1.118 0923 -0.705 10.508 10.658 1 10.658
123 -0.777 4 0.341 1323 -0.504 7.901 8.051 0 7.901
124 0.070 1 -0.158 0124 -0.092 10.829 10.979 0 10.829
124 0.070 2 0.369 0524 0.157 11.140 11.290 1 11.290
124 0.070 3 1.118 0924 -0.365 11.116 11.266 1 11.266
124 0.070 4 0.341 1324 -0.498 12.396 12.546 0 12.396
125 -0.193 1 -0.158 0125 -0.125 9.292 9.442 1 9.442
125 -0.193 2 0.369 0525 -0.352 10.628 10.778 1 10.778
125 -0.193 3 1.118 0925 -0.036 9.953 10.103 1 10.103
125 -0.193 4 0.341 1325 -0.199 10.923 11.073 0 10.923
126 0.228 1 -0.158 0126 0.326 11.818 11.968 0 11.818
126 0.228 2 0.369 0526 -0.107 11.540 11.690 0 11.540
126 0.228 3 1.118 0926 0.095 12.887 13.037 1 13.037
126 0.228 4 0.341 1326 -0.038 11.425 11.575 1 11.575
127 -0.789 1 -0.158 0127 0.263 9.934 10.084 0 9.934
127 -0.789 2 0.369 0527 -0.331 9.676 9.826 1 9.826
127 -0.789 3 1.118 0927 -0.843 9.163 9.313 0 9.163
127 -0.789 4 0.341 1327 -0.441 10.756 10.906 0 10.756
128 -0.632 1 -0.158 0128 -0.602 9.866 10.016 0 9.866
128 -0.632 2 0.369 0528 -0.101 10.069 10.219 0 10.069
128 -0.632 3 1.118 0928 -0.287 9.970 10.120 1 10.120
128 -0.632 4 0.341 1328 -0.364 9.138 9.288 0 9.138
129 0.370 1 -0.158 0129 0.147 12.777 12.927 1 12.927
129 0.370 2 0.369 0529 -0.528 9.562 9.712 1 9.712
129 0.370 3 1.118 0929 0.626 14.432 14.582 1 14.582
129 0.370 4 0.341 1329 0.160 9.806 9.956 0 9.806
130 0.587 1 -0.158 0130 0.168 11.207 11.357 0 11.207
130 0.587 2 0.369 0530 0.282 10.369 10.519 1 10.519
130 0.587 3 1.118 0930 0.806 13.650 13.800 1 13.800
130 0.587 4 0.341 1330 -0.651 10.989 11.139 1 11.139
131 -0.689 1 -0.158 0131 0.329 8.032 8.182 0 8.032
131 -0.689 2 0.369 0531 -0.084 9.851 10.001 0 9.851
131 -0.689 3 1.118 0931 -0.797 9.923 10.073 1 10.073
131 -0.689 4 0.341 1331 -0.244 7.602 7.752 1 7.752
132 1.077 1 -0.158 0132 -0.657 11.253 11.403 1 11.403
132 1.077 2 0.369 0532 -0.243 12.774 12.924 1 12.924
132 1.077 3 1.118 0932 1.102 12.835 12.985 1 12.985
132 1.077 4 0.341 1332 0.590 11.517 11.667 1 11.667
133 -0.505 1 -0.158 0133 -1.044 10.299 10.449 0 10.299
133 -0.505 2 0.369 0533 -0.078 9.923 10.073 1 10.073
133 -0.505 3 1.118 0933 -0.890 11.090 11.240 1 11.240
133 -0.505 4 0.341 1333 -0.532 8.916 9.066 1 9.066
134 0.294 1 -0.158 0134 0.208 10.024 10.174 0 10.024
134 0.294 2 0.369 0534 0.308 10.953 11.103 0 10.953
134 0.294 3 1.118 0934 -0.552 12.252 12.402 1 12.402
134 0.294 4 0.341 1334 1.004 10.982 11.132 1 11.132
135 0.572 1 -0.158 0135 0.355 8.852 9.002 1 9.002
135 0.572 2 0.369 0535 -0.376 9.962 10.112 1 10.112
135 0.572 3 1.118 0935 0.530 11.989 12.139 1 12.139
135 0.572 4 0.341 1335 -0.246 10.324 10.474 1 10.474
136 0.286 1 -0.158 0136 0.386 9.837 9.987 0 9.837
136 0.286 2 0.369 0536 0.995 12.955 13.105 1 13.105
136 0.286 3 1.118 0936 -0.319 11.288 11.438 1 11.438
136 0.286 4 0.341 1336 -0.403 9.980 10.130 1 10.130
137 -0.576 1 -0.158 0137 -0.334 10.179 10.329 0 10.179
137 -0.576 2 0.369 0537 -1.081 9.209 9.359 1 9.359
137 -0.576 3 1.118 0937 -0.822 11.438 11.588 0 11.438
137 -0.576 4 0.341 1337 0.363 11.815 11.965 0 11.815
138 0.750 1 -0.158 0138 -0.585 11.945 12.095 0 11.945
138 0.750 2 0.369 0538 -0.008 10.870 11.020 1 11.020
138 0.750 3 1.118 0938 0.105 10.609 10.759 1 10.759
138 0.750 4 0.341 1338 0.130 12.262 12.412 1 12.412
139 -0.469 1 -0.158 0139 0.333 9.838 9.988 0 9.838
139 -0.469 2 0.369 0539 0.242 9.398 9.548 0 9.398
139 -0.469 3 1.118 0939 0.593 10.696 10.846 0 10.696
139 -0.469 4 0.341 1339 0.418 9.921 10.071 0 9.921
140 0.074 1 -0.158 0140 -0.020 11.683 11.833 1 11.833
140 0.074 2 0.369 0540 -0.140 10.823 10.973 1 10.973
140 0.074 3 1.118 0940 1.883 12.704 12.854 1 12.854
140 0.074 4 0.341 1340 0.658 13.232 13.382 1 13.382
141 -0.024 1 -0.158 0141 0.396 9.607 9.757 0 9.607
141 -0.024 2 0.369 0541 0.433 11.774 11.924 0 11.774
141 -0.024 3 1.118 0941 -0.403 10.362 10.512 1 10.512
141 -0.024 4 0.341 1341 -0.073 12.987 13.137 1 13.137
142 -0.512 1 -0.158 0142 0.025 10.924 11.074 0 10.924
142 -0.512 2 0.369 0542 0.626 10.407 10.557 1 10.557
142 -0.512 3 1.118 0942 0.001 10.466 10.616 1 10.616
142 -0.512 4 0.341 1342 0.443 11.783 11.933 1 11.933
143 0.279 1 -0.158 0143 -0.209 8.594 8.744 0 8.594
143 0.279 2 0.369 0543 0.292 11.476 11.626 1 11.626
143 0.279 3 1.118 0943 0.156 11.560 11.710 1 11.710
143 0.279 4 0.341 1343 -0.370 10.185 10.335 0 10.185
144 -0.017 1 -0.158 0144 -0.142 7.890 8.040 0 7.890
144 -0.017 2 0.369 0544 0.094 12.207 12.357 1 12.357
144 -0.017 3 1.118 0944 -1.008 8.673 8.823 1 8.823
144 -0.017 4 0.341 1344 0.317 12.467 12.617 0 12.467
145 -0.638 1 -0.158 0145 0.753 10.015 10.165 0 10.015
145 -0.638 2 0.369 0545 -0.369 10.287 10.437 0 10.287
145 -0.638 3 1.118 0945 -0.361 10.256 10.406 0 10.256
145 -0.638 4 0.341 1345 -0.207 10.131 10.281 1 10.281
146 -0.313 1 -0.158 0146 -1.226 6.822 6.972 0 6.822
146 -0.313 2 0.369 0546 0.302 11.103 11.253 1 11.253
146 -0.313 3 1.118 0946 0.011 10.006 10.156 0 10.006
146 -0.313 4 0.341 1346 -0.173 10.861 11.011 0 10.861
147 0.671 1 -0.158 0147 -0.405 10.769 10.919 1 10.919
147 0.671 2 0.369 0547 0.804 13.724 13.874 1 13.874
147 0.671 3 1.118 0947 -0.030 11.892 12.042 1 12.042
147 0.671 4 0.341 1347 0.403 10.700 10.850 1 10.850
148 0.334 1 -0.158 0148 -0.433 8.484 8.634 1 8.634
148 0.334 2 0.369 0548 0.033 11.227 11.377 0 11.227
148 0.334 3 1.118 0948 0.762 11.946 12.096 1 12.096
148 0.334 4 0.341 1348 -0.002 10.470 10.620 1 10.620
149 0.299 1 -0.158 0149 0.224 9.871 10.021 1 10.021
149 0.299 2 0.369 0549 -0.316 10.466 10.616 1 10.616
149 0.299 3 1.118 0949 1.212 12.714 12.864 1 12.864
149 0.299 4 0.341 1349 -0.011 11.362 11.512 0 11.362
150 0.961 1 -0.158 0150 -0.486 10.681 10.831 1 10.831
150 0.961 2 0.369 0550 0.197 11.290 11.440 1 11.440
150 0.961 3 1.118 0950 1.027 14.011 14.161 1 14.161
150 0.961 4 0.341 1350 0.134 10.656 10.806 1 10.806
151 0.688 1 -0.158 0151 0.631 11.987 12.137 1 12.137
151 0.688 2 0.369 0551 -0.265 12.138 12.288 1 12.288
151 0.688 3 1.118 0951 0.878 14.785 14.935 1 14.935
151 0.688 4 0.341 1351 0.008 10.035 10.185 1 10.185
152 -0.161 1 -0.158 0152 0.415 10.416 10.566 1 10.566
152 -0.161 2 0.369 0552 -0.161 11.188 11.338 1 11.338
152 -0.161 3 1.118 0952 -0.738 10.572 10.722 1 10.722
152 -0.161 4 0.341 1352 -0.938 9.541 9.691 0 9.541
153 0.085 1 -0.158 0153 -0.218 10.025 10.175 0 10.025
153 0.085 2 0.369 0553 -0.051 12.020 12.170 1 12.170
153 0.085 3 1.118 0953 -0.015 10.578 10.728 1 10.728
153 0.085 4 0.341 1353 -0.388 11.247 11.397 0 11.247
154 0.251 1 -0.158 0154 -0.750 12.200 12.350 0 12.200
154 0.251 2 0.369 0554 0.534 11.995 12.145 0 11.995
154 0.251 3 1.118 0954 0.226 11.820 11.970 1 11.970
154 0.251 4 0.341 1354 0.474 10.511 10.661 0 10.511
155 -0.376 1 -0.158 0155 0.208 8.952 9.102 1 9.102
155 -0.376 2 0.369 0555 -0.196 10.701 10.851 0 10.701
155 -0.376 3 1.118 0955 0.694 11.175 11.325 1 11.325
155 -0.376 4 0.341 1355 -0.732 10.937 11.087 1 11.087
156 0.127 1 -0.158 0156 -0.334 9.937 10.087 1 10.087
156 0.127 2 0.369 0556 0.002 11.207 11.357 1 11.357
156 0.127 3 1.118 0956 -0.476 10.870 11.020 1 11.020
156 0.127 4 0.341 1356 0.490 10.591 10.741 1 10.741
157 -0.491 1 -0.158 0157 -0.149 11.017 11.167 0 11.017
157 -0.491 2 0.369 0557 -0.109 9.095 9.245 1 9.245
157 -0.491 3 1.118 0957 -0.523 10.960 11.110 1 11.110
157 -0.491 4 0.341 1357 -0.682 8.858 9.008 0 8.858
158 0.254 1 -0.158 0158 -0.204 9.354 9.504 1 9.504
158 0.254 2 0.369 0558 -0.497 10.175 10.325 1 10.325
158 0.254 3 1.118 0958 -0.311 13.539 13.689 1 13.689
158 0.254 4 0.341 1358 -0.278 10.206 10.356 0 10.206
159 0.673 1 -0.158 0159 0.445 10.133 10.283 1 10.283
159 0.673 2 0.369 0559 0.336 12.214 12.364 1 12.364
159 0.673 3 1.118 0959 -0.697 11.463 11.613 1 11.613
159 0.673 4 0.341 1359 0.186 9.785 9.935 1 9.935
160 -0.588 1 -0.158 0160 -0.897 9.109 9.259 0 9.109
160 -0.588 2 0.369 0560 0.065 11.076 11.226 0 11.076
160 -0.588 3 1.118 0960 -0.702 9.729 9.879 1 9.879
160 -0.588 4 0.341 1360 -0.724 10.721 10.871 0 10.721
161 0.425 1 -0.158 0161 -0.826 11.452 11.602 0 11.452
161 0.425 2 0.369 0561 0.104 13.060 13.210 0 13.060
161 0.425 3 1.118 0961 -0.794 11.539 11.689 0 11.539
161 0.425 4 0.341 1361 0.312 11.432 11.582 0 11.432
162 0.869 1 -0.158 0162 -0.334 12.062 12.212 0 12.062
162 0.869 2 0.369 0562 0.023 11.170 11.320 1 11.320
162 0.869 3 1.118 0962 1.672 14.172 14.322 1 14.322
162 0.869 4 0.341 1362 0.258 12.780 12.930 1 12.930
163 0.500 1 -0.158 0163 -0.082 9.967 10.117 1 10.117
163 0.500 2 0.369 0563 0.206 12.503 12.653 1 12.653
163 0.500 3 1.118 0963 0.914 13.527 13.677 1 13.677
163 0.500 4 0.341 1363 0.079 11.466 11.616 1 11.616
164 0.043 1 -0.158 0164 0.237 10.002 10.152 0 10.002
164 0.043 2 0.369 0564 0.267 10.003 10.153 1 10.153
164 0.043 3 1.118 0964 0.301 11.518 11.668 1 11.668
164 0.043 4 0.341 1364 0.304 12.173 12.323 0 12.173
165 0.628 1 -0.158 0165 0.220 12.027 12.177 1 12.177
165 0.628 2 0.369 0565 0.118 11.299 11.449 1 11.449
165 0.628 3 1.118 0965 0.584 12.667 12.817 1 12.817
165 0.628 4 0.341 1365 0.193 12.858 13.008 1 13.008
166 -0.550 1 -0.158 0166 0.635 8.393 8.543 0 8.393
166 -0.550 2 0.369 0566 0.456 10.358 10.508 0 10.358
166 -0.550 3 1.118 0966 -0.728 9.740 9.890 1 9.890
166 -0.550 4 0.341 1366 -0.329 10.048 10.198 1 10.198
167 0.638 1 -0.158 0167 -0.577 10.633 10.783 0 10.633
167 0.638 2 0.369 0567 0.076 11.985 12.135 1 12.135
167 0.638 3 1.118 0967 0.584 11.554 11.704 1 11.704
167 0.638 4 0.341 1367 0.335 12.350 12.500 1 12.500
168 0.575 1 -0.158 0168 -0.417 9.222 9.372 1 9.372
168 0.575 2 0.369 0568 -0.370 11.484 11.634 0 11.484
168 0.575 3 1.118 0968 0.503 13.541 13.691 1 13.691
168 0.575 4 0.341 1368 -0.254 12.203 12.353 1 12.353
169 -0.525 1 -0.158 0169 0.785 10.881 11.031 0 10.881
169 -0.525 2 0.369 0569 -0.369 9.948 10.098 0 9.948
169 -0.525 3 1.118 0969 -0.718 9.832 9.982 0 9.832
169 -0.525 4 0.341 1369 -0.988 8.223 8.373 1 8.373
170 0.276 1 -0.158 0170 -0.142 10.120 10.270 0 10.120
170 0.276 2 0.369 0570 -0.272 10.915 11.065 1 11.065
170 0.276 3 1.118 0970 1.222 13.037 13.187 1 13.187
170 0.276 4 0.341 1370 0.655 13.320 13.470 0 13.320
171 1.434 1 -0.158 0171 0.571 12.685 12.835 1 12.835
171 1.434 2 0.369 0571 1.961 14.698 14.848 1 14.848
171 1.434 3 1.118 0971 2.026 15.598 15.748 1 15.748
171 1.434 4 0.341 1371 1.230 12.768 12.918 1 12.918
172 -1.272 1 -0.158 0172 0.376 9.660 9.810 0 9.660
172 -1.272 2 0.369 0572 0.529 10.516 10.666 0 10.516
172 -1.272 3 1.118 0972 -0.752 9.749 9.899 1 9.899
172 -1.272 4 0.341 1372 -0.279 10.292 10.442 1 10.442
173 -0.030 1 -0.158 0173 -0.467 10.362 10.512 1 10.512
173 -0.030 2 0.369 0573 0.458 10.440 10.590 0 10.440
173 -0.030 3 1.118 0973 0.782 12.828 12.978 1 12.978
173 -0.030 4 0.341 1373 -0.178 12.656 12.806 1 12.806
174 0.852 1 -0.158 0174 -0.895 9.930 10.080 1 10.080
174 0.852 2 0.369 0574 0.800 12.303 12.453 1 12.453
174 0.852 3 1.118 0974 1.658 12.159 12.309 1 12.309
174 0.852 4 0.341 1374 0.020 10.458 10.608 1 10.608
175 -0.087 1 -0.158 0175 1.009 11.633 11.783 1 11.783
175 -0.087 2 0.369 0575 0.255 11.177 11.327 1 11.327
175 -0.087 3 1.118 0975 0.696 14.561 14.711 0 14.561
175 -0.087 4 0.341 1375 -0.681 8.937 9.087 1 9.087
176 -0.509 1 -0.158 0176 -0.351 11.809 11.959 0 11.809
176 -0.509 2 0.369 0576 -1.161 6.838 6.988 0 6.838
176 -0.509 3 1.118 0976 -1.231 10.882 11.032 0 10.882
176 -0.509 4 0.341 1376 -0.379 8.559 8.709 0 8.559
177 0.151 1 -0.158 0177 0.204 11.165 11.315 0 11.165
177 0.151 2 0.369 0577 -0.553 11.582 11.732 0 11.582
177 0.151 3 1.118 0977 -0.557 13.287 13.437 1 13.437
177 0.151 4 0.341 1377 0.874 10.490 10.640 1 10.640
178 -0.358 1 -0.158 0178 0.464 12.345 12.495 0 12.345
178 -0.358 2 0.369 0578 -0.122 9.579 9.729 0 9.579
178 -0.358 3 1.118 0978 -0.386 9.371 9.521 1 9.521
178 -0.358 4 0.341 1378 -0.869 7.614 7.764 0 7.614
179 -0.851 1 -0.158 0179 -0.781 7.308 7.458 0 7.308
179 -0.851 2 0.369 0579 -0.701 12.270 12.420 1 12.420
179 -0.851 3 1.118 0979 -0.909 10.531 10.681 0 10.531
179 -0.851 4 0.341 1379 0.125 9.288 9.438 1 9.438
180 0.264 1 -0.158 0180 0.643 10.314 10.464 0 10.314
180 0.264 2 0.369 0580 1.185 12.390 12.540 1 12.540
180 0.264 3 1.118 0980 0.067 13.493 13.643 1 13.643
180 0.264 4 0.341 1380 0.278 10.757 10.907 0 10.757
181 0.145 1 -0.158 0181 0.075 10.255 10.405 1 10.405
181 0.145 2 0.369 0581 0.308 10.986 11.136 1 11.136
181 0.145 3 1.118 0981 1.223 12.759 12.909 1 12.909
181 0.145 4 0.341 1381 0.453 9.468 9.618 1 9.618
182 -0.625 1 -0.158 0182 -0.275 9.579 9.729 0 9.579
182 -0.625 2 0.369 0582 -0.556 9.040 9.190 0 9.040
182 -0.625 3 1.118 0982 -0.486 11.458 11.608 1 11.608
182 -0.625 4 0.341 1382 -0.687 9.534 9.684 0 9.534
183 -0.888 1 -0.158 0183 1.195 9.924 10.074 0 9.924
183 -0.888 2 0.369 0583 -0.181 9.501 9.651 0 9.501
183 -0.888 3 1.118 0983 -1.127 8.801 8.951 1 8.951
183 -0.888 4 0.341 1383 -0.071 9.858 10.008 0 9.858
184 0.306 1 -0.158 0184 -0.327 10.744 10.894 1 10.894
184 0.306 2 0.369 0584 0.818 12.460 12.610 1 12.610
184 0.306 3 1.118 0984 0.053 12.257 12.407 1 12.407
184 0.306 4 0.341 1384 0.889 11.704 11.854 1 11.854
185 -0.459 1 -0.158 0185 0.475 10.766 10.916 1 10.916
185 -0.459 2 0.369 0585 0.264 10.918 11.068 1 11.068
185 -0.459 3 1.118 0985 -1.215 11.502 11.652 1 11.652
185 -0.459 4 0.341 1385 0.124 9.613 9.763 1 9.763
186 0.845 1 -0.158 0186 -0.127 10.284 10.434 1 10.434
186 0.845 2 0.369 0586 0.925 12.824 12.974 1 12.974
186 0.845 3 1.118 0986 1.175 12.601 12.751 1 12.751
186 0.845 4 0.341 1386 0.075 13.123 13.273 1 13.273
187 -0.250 1 -0.158 0187 0.163 10.843 10.993 1 10.993
187 -0.250 2 0.369 0587 0.697 10.514 10.664 0 10.514
187 -0.250 3 1.118 0987 1.030 12.472 12.622 0 12.472
187 -0.250 4 0.341 1387 -0.387 8.716 8.866 1 8.866
188 0.027 1 -0.158 0188 0.109 8.922 9.072 1 9.072
188 0.027 2 0.369 0588 0.370 9.623 9.773 0 9.623
188 0.027 3 1.118 0988 0.024 11.053 11.203 1 11.203
188 0.027 4 0.341 1388 -0.305 7.792 7.942 1 7.942
189 0.272 1 -0.158 0189 0.895 11.708 11.858 1 11.858
189 0.272 2 0.369 0589 -0.192 10.356 10.506 1 10.506
189 0.272 3 1.118 0989 0.262 13.693 13.843 1 13.843
189 0.272 4 0.341 1389 -0.433 11.566 11.716 0 11.566
190 -0.270 1 -0.158 0190 -0.407 12.165 12.315 1 12.315
190 -0.270 2 0.369 0590 -0.272 9.695 9.845 1 9.845
190 -0.270 3 1.118 0990 -0.153 10.712 10.862 1 10.862
190 -0.270 4 0.341 1390 -1.297 10.887 11.037 1 11.037
191 0.324 1 -0.158 0191 -0.804 9.378 9.528 1 9.528
191 0.324 2 0.369 0591 0.194 11.272 11.422 1 11.422
191 0.324 3 1.118 0991 0.031 10.476 10.626 1 10.626
191 0.324 4 0.341 1391 -0.123 11.538 11.688 1 11.688
192 1.077 1 -0.158 0192 -1.328 9.671 9.821 1 9.821
192 1.077 2 0.369 0592 0.859 12.369 12.519 1 12.519
192 1.077 3 1.118 0992 1.066 13.955 14.105 1 14.105
192 1.077 4 0.341 1392 0.626 11.711 11.861 1 11.861
193 1.203 1 -0.158 0193 -0.006 9.679 9.829 0 9.679
193 1.203 2 0.369 0593 -0.401 12.302 12.452 1 12.452
193 1.203 3 1.118 0993 2.011 14.735 14.885 1 14.885
193 1.203 4 0.341 1393 0.238 12.728 12.878 1 12.878
194 -0.331 1 -0.158 0194 0.208 7.768 7.918 0 7.768
194 -0.331 2 0.369 0594 -0.398 8.536 8.686 1 8.686
194 -0.331 3 1.118 0994 -1.121 11.186 11.336 1 11.336
194 -0.331 4 0.341 1394 -0.224 10.711 10.861 1 10.861
195 -0.117 1 -0.158 0195 -0.483 8.834 8.984 0 8.834
195 -0.117 2 0.369 0595 -0.343 10.658 10.808 0 10.658
195 -0.117 3 1.118 0995 -0.317 9.867 10.017 1 10.017
195 -0.117 4 0.341 1395 -0.008 11.122 11.272 0 11.122
196 -0.544 1 -0.158 0196 0.187 9.946 10.096 0 9.946
196 -0.544 2 0.369 0596 -0.728 9.815 9.965 1 9.965
196 -0.544 3 1.118 0996 -0.161 9.564 9.714 1 9.714
196 -0.544 4 0.341 1396 0.347 10.093 10.243 0 10.093
197 -0.110 1 -0.158 0197 0.271 8.257 8.407 0 8.257
197 -0.110 2 0.369 0597 0.539 12.078 12.228 0 12.078
197 -0.110 3 1.118 0997 -0.451 11.481 11.631 1 11.631
197 -0.110 4 0.341 1397 1.024 10.639 10.789 0 10.639
198 -0.448 1 -0.158 0198 -0.170 8.960 9.110 0 8.960
198 -0.448 2 0.369 0598 0.096 8.970 9.120 0 8.970
198 -0.448 3 1.118 0998 -0.739 12.011 12.161 1 12.161
198 -0.448 4 0.341 1398 -0.370 10.969 11.119 1 11.119
199 0.724 1 -0.158 0199 -1.425 9.828 9.978 1 9.978
199 0.724 2 0.369 0599 0.779 12.112 12.262 1 12.262
199 0.724 3 1.118 0999 1.842 14.157 14.307 1 14.307
199 0.724 4 0.341 1399 0.203 9.710 9.860 1 9.860
200 -0.439 1 -0.158 0200 0.531 9.383 9.533 0 9.383
200 -0.439 2 0.369 0600 -0.172 9.946 10.096 0 9.946
200 -0.439 3 1.118 1000 -0.930 9.765 9.915 1 9.915
200 -0.439 4 0.341 1400 0.067 10.152 10.302 1 10.302
201 -0.363 1 -0.158 0201 -0.093 10.315 10.465 0 10.315
201 -0.363 2 0.369 0601 0.138 9.752 9.902 0 9.752
201 -0.363 3 1.118 1001 -0.703 9.931 10.081 1 10.081
201 -0.363 4 0.341 1401 -0.343 9.126 9.276 0 9.126
202 -0.346 1 -0.158 0202 -0.211 7.826 7.976 1 7.976
202 -0.346 2 0.369 0602 -0.788 8.734 8.884 0 8.734
202 -0.346 3 1.118 1002 -0.296 10.142 10.292 1 10.292
202 -0.346 4 0.341 1402 -0.740 10.074 10.224 0 10.074
203 -0.603 1 -0.158 0203 -1.269 8.505 8.655 0 8.505
203 -0.603 2 0.369 0603 0.173 10.855 11.005 0 10.855
203 -0.603 3 1.118 1003 -0.563 10.894 11.044 0 10.894
203 -0.603 4 0.341 1403 -0.425 11.524 11.674 0 11.524
204 -0.270 1 -0.158 0204 0.096 9.533 9.683 0 9.533
204 -0.270 2 0.369 0604 -0.088 11.376 11.526 1 11.526
204 -0.270 3 1.118 1004 -0.331 11.136 11.286 0 11.136
204 -0.270 4 0.341 1404 0.493 12.292 12.442 1 12.442
205 -0.200 1 -0.158 0205 -0.117 10.246 10.396 0 10.246
205 -0.200 2 0.369 0605 0.053 9.007 9.157 0 9.007
205 -0.200 3 1.118 1005 -0.353 10.070 10.220 1 10.220
205 -0.200 4 0.341 1405 -0.489 10.025 10.175 1 10.175
206 -0.322 1 -0.158 0206 -0.406 10.098 10.248 1 10.248
206 -0.322 2 0.369 0606 0.038 10.124 10.274 1 10.274
206 -0.322 3 1.118 1006 -0.875 9.548 9.698 1 9.698
206 -0.322 4 0.341 1406 -0.169 10.914 11.064 1 11.064
207 0.257 1 -0.158 0207 -0.306 9.883 10.033 1 10.033
207 0.257 2 0.369 0607 -0.200 11.400 11.550 1 11.550
207 0.257 3 1.118 1007 0.469 12.082 12.232 1 12.232
207 0.257 4 0.341 1407 0.089 10.838 10.988 1 10.988
208 0.201 1 -0.158 0208 0.111 12.369 12.519 1 12.519
208 0.201 2 0.369 0608 -0.449 10.488 10.638 0 10.488
208 0.201 3 1.118 1008 -0.293 10.165 10.315 1 10.315
208 0.201 4 0.341 1408 -0.620 10.680 10.830 1 10.830
209 0.398 1 -0.158 0209 0.070 11.090 11.240 1 11.240
209 0.398 2 0.369 0609 -0.746 10.642 10.792 1 10.792
209 0.398 3 1.118 1009 -0.186 12.467 12.617 1 12.617
209 0.398 4 0.341 1409 0.422 11.763 11.913 1 11.913
210 0.130 1 -0.158 0210 0.063 11.019 11.169 0 11.019
210 0.130 2 0.369 0610 0.461 9.788 9.938 1 9.938
210 0.130 3 1.118 1010 0.396 12.894 13.044 1 13.044
210 0.130 4 0.341 1410 -0.087 10.687 10.837 1 10.837
211 -0.243 1 -0.158 0211 0.181 9.743 9.893 0 9.743
211 -0.243 2 0.369 0611 -0.223 9.761 9.911 1 9.911
211 -0.243 3 1.118 1011 -0.039 11.594 11.744 1 11.744
211 -0.243 4 0.341 1411 0.210 10.521 10.671 0 10.521
212 -0.538 1 -0.158 0212 0.135 9.237 9.387 1 9.387
212 -0.538 2 0.369 0612 0.242 9.334 9.484 0 9.334
212 -0.538 3 1.118 1012 -0.349 9.009 9.159 0 9.009
212 -0.538 4 0.341 1412 0.258 11.009 11.159 1 11.159
213 0.580 1 -0.158 0213 0.011 12.032 12.182 1 12.182
213 0.580 2 0.369 0613 0.434 11.755 11.905 1 11.905
213 0.580 3 1.118 1013 0.121 12.152 12.302 1 12.302
213 0.580 4 0.341 1413 0.612 11.830 11.980 1 11.980
214 0.017 1 -0.158 0214 0.226 11.144 11.294 1 11.294
214 0.017 2 0.369 0614 -0.867 10.911 11.061 1 11.061
214 0.017 3 1.118 1014 -0.162 11.411 11.561 1 11.561
214 0.017 4 0.341 1414 0.068 9.839 9.989 0 9.839
215 0.549 1 -0.158 0215 -0.273 10.469 10.619 0 10.469
215 0.549 2 0.369 0615 0.453 10.363 10.513 1 10.513
215 0.549 3 1.118 1015 -0.332 13.745 13.895 1 13.895
215 0.549 4 0.341 1415 0.941 12.856 13.006 1 13.006
216 -0.532 1 -0.158 0216 -0.315 8.413 8.563 0 8.413
216 -0.532 2 0.369 0616 -0.368 9.273 9.423 0 9.273
216 -0.532 3 1.118 1016 0.156 12.795 12.945 1 12.945
216 -0.532 4 0.341 1416 0.058 8.837 8.987 1 8.987
217 -0.292 1 -0.158 0217 0.396 11.193 11.343 0 11.193
217 -0.292 2 0.369 0617 -0.443 10.377 10.527 0 10.377
217 -0.292 3 1.118 1017 -1.123 10.882 11.032 1 11.032
217 -0.292 4 0.341 1417 -0.122 10.334 10.484 1 10.484
218 0.108 1 -0.158 0218 -0.290 11.575 11.725 1 11.725
218 0.108 2 0.369 0618 -0.235 10.008 10.158 0 10.008
218 0.108 3 1.118 1018 -0.294 10.059 10.209 1 10.209
218 0.108 4 0.341 1418 0.608 9.807 9.957 0 9.807
219 -0.278 1 -0.158 0219 0.315 9.399 9.549 1 9.549
219 -0.278 2 0.369 0619 -1.081 8.323 8.473 1 8.473
219 -0.278 3 1.118 1019 -1.346 12.577 12.727 1 12.727
219 -0.278 4 0.341 1419 0.734 11.083 11.233 0 11.083
220 -0.362 1 -0.158 0220 0.090 10.821 10.971 1 10.971
220 -0.362 2 0.369 0620 0.439 12.108 12.258 0 12.108
220 -0.362 3 1.118 1020 -0.346 12.833 12.983 1 12.983
220 -0.362 4 0.341 1420 0.688 10.894 11.044 1 11.044
221 0.459 1 -0.158 0221 -0.344 10.024 10.174 1 10.174
221 0.459 2 0.369 0621 0.478 12.391 12.541 0 12.391
221 0.459 3 1.118 1021 0.613 12.427 12.577 1 12.577
221 0.459 4 0.341 1421 0.388 11.993 12.143 1 12.143
222 -0.598 1 -0.158 0222 -0.139 9.251 9.401 0 9.251
222 -0.598 2 0.369 0622 -0.293 8.721 8.871 1 8.871
222 -0.598 3 1.118 1022 -0.820 8.744 8.894 1 8.894
222 -0.598 4 0.341 1422 0.721 10.516 10.666 0 10.516
223 -0.216 1 -0.158 0223 -0.477 9.127 9.277 0 9.127
223 -0.216 2 0.369 0623 -0.616 8.291 8.441 1 8.441
223 -0.216 3 1.118 1023 -0.527 9.961 10.111 1 10.111
223 -0.216 4 0.341 1423 0.433 12.510 12.660 0 12.510
224 -0.117 1 -0.158 0224 0.737 11.699 11.849 1 11.849
224 -0.117 2 0.369 0624 -0.083 10.897 11.047 1 11.047
224 -0.117 3 1.118 1024 -0.305 10.416 10.566 1 10.566
224 -0.117 4 0.341 1424 0.053 11.130 11.280 1 11.280
225 -0.277 1 -0.158 0225 -0.330 7.977 8.127 0 7.977
225 -0.277 2 0.369 0625 -0.142 10.834 10.984 1 10.984
225 -0.277 3 1.118 1025 -0.339 10.704 10.854 1 10.854
225 -0.277 4 0.341 1425 -0.610 10.724 10.874 1 10.874
226 0.348 1 -0.158 0226 -0.399 10.275 10.425 1 10.425
226 0.348 2 0.369 0626 -0.332 9.355 9.505 1 9.505
226 0.348 3 1.118 1026 0.340 13.691 13.841 1 13.841
226 0.348 4 0.341 1426 0.220 11.926 12.076 1 12.076
227 0.737 1 -0.158 0227 0.319 10.860 11.010 1 11.010
227 0.737 2 0.369 0627 1.029 11.795 11.945 1 11.945
227 0.737 3 1.118 1027 -0.102 11.224 11.374 1 11.374
227 0.737 4 0.341 1427 0.480 11.636 11.786 0 11.636
228 0.497 1 -0.158 0228 -0.158 11.497 11.647 1 11.647
228 0.497 2 0.369 0628 0.929 11.335 11.485 1 11.485
228 0.497 3 1.118 1028 0.566 12.915 13.065 1 13.065
228 0.497 4 0.341 1428 -0.076 12.664 12.814 1 12.814
229 -0.992 1 -0.158 0229 0.411 10.976 11.126 1 11.126
229 -0.992 2 0.369 0629 0.509 11.471 11.621 0 11.471
229 -0.992 3 1.118 1029 -1.124 7.977 8.127 1 8.127
229 -0.992 4 0.341 1429 -0.347 8.422 8.572 1 8.572
230 0.271 1 -0.158 0230 0.352 11.629 11.779 1 11.779
230 0.271 2 0.369 0630 0.759 11.931 12.081 1 12.081
230 0.271 3 1.118 1030 0.314 13.653 13.803 0 13.653
230 0.271 4 0.341 1430 0.645 11.998 12.148 1 12.148
231 0.052 1 -0.158 0231 0.183 9.441 9.591 1 9.591
231 0.052 2 0.369 0631 0.320 12.370 12.520 0 12.370
231 0.052 3 1.118 1031 0.232 12.767 12.917 1 12.917
231 0.052 4 0.341 1431 -0.829 8.448 8.598 0 8.448
232 -0.671 1 -0.158 0232 -0.554 8.669 8.819 0 8.669
232 -0.671 2 0.369 0632 -0.077 12.210 12.360 0 12.210
232 -0.671 3 1.118 1032 -0.292 9.765 9.915 0 9.765
232 -0.671 4 0.341 1432 -0.451 10.125 10.275 0 10.125
233 0.717 1 -0.158 0233 0.159 11.459 11.609 1 11.609
233 0.717 2 0.369 0633 0.639 13.575 13.725 1 13.725
233 0.717 3 1.118 1033 0.654 12.194 12.344 1 12.344
233 0.717 4 0.341 1433 1.285 12.727 12.877 1 12.877
234 -0.089 1 -0.158 0234 0.736 10.774 10.924 0 10.774
234 -0.089 2 0.369 0634 -0.202 9.732 9.882 1 9.882
234 -0.089 3 1.118 1034 -0.195 12.344 12.494 1 12.494
234 -0.089 4 0.341 1434 0.283 11.875 12.025 0 11.875
235 0.196 1 -0.158 0235 -0.531 9.800 9.950 0 9.800
235 0.196 2 0.369 0635 0.412 11.257 11.407 0 11.257
235 0.196 3 1.118 1035 -0.298 11.299 11.449 1 11.449
235 0.196 4 0.341 1435 -0.050 11.189 11.339 1 11.339
236 -0.131 1 -0.158 0236 -0.225 8.201 8.351 1 8.351
236 -0.131 2 0.369 0636 0.659 11.648 11.798 1 11.798
236 -0.131 3 1.118 1036 0.245 12.244 12.394 1 12.394
236 -0.131 4 0.341 1436 0.450 9.266 9.416 1 9.416
237 0.132 1 -0.158 0237 -0.482 10.440 10.590 0 10.440
237 0.132 2 0.369 0637 0.273 12.967 13.117 1 13.117
237 0.132 3 1.118 1037 0.470 12.006 12.156 1 12.156
237 0.132 4 0.341 1437 -0.295 9.892 10.042 1 10.042
238 -0.047 1 -0.158 0238 -0.051 10.030 10.180 0 10.030
238 -0.047 2 0.369 0638 -0.237 11.155 11.305 0 11.155
238 -0.047 3 1.118 1038 1.079 12.272 12.422 0 12.272
238 -0.047 4 0.341 1438 1.276 11.651 11.801 1 11.801
239 -0.813 1 -0.158 0239 -0.120 9.494 9.644 1 9.644
239 -0.813 2 0.369 0639 -0.754 10.148 10.298 1 10.298
239 -0.813 3 1.118 1039 -0.516 10.908 11.058 1 11.058
239 -0.813 4 0.341 1439 -0.386 8.883 9.033 0 8.883
240 -0.123 1 -0.158 0240 -0.370 11.558 11.708 0 11.558
240 -0.123 2 0.369 0640 0.074 10.165 10.315 0 10.165
240 -0.123 3 1.118 1040 -0.161 10.337 10.487 1 10.487
240 -0.123 4 0.341 1440 -0.423 11.388 11.538 0 11.388
241 -0.561 1 -0.158 0241 -0.627 9.163 9.313 1 9.313
241 -0.561 2 0.369 0641 -0.500 9.834 9.984 0 9.834
241 -0.561 3 1.118 1041 -0.437 11.957 12.107 0 11.957
241 -0.561 4 0.341 1441 -0.076 8.648 8.798 1 8.798
242 -0.607 1 -0.158 0242 0.624 11.065 11.215 1 11.215
242 -0.607 2 0.369 0642 -0.274 10.792 10.942 1 10.942
242 -0.607 3 1.118 1042 -0.548 10.528 10.678 1 10.678
242 -0.607 4 0.341 1442 -0.145 10.158 10.308 1 10.308
243 1.507 1 -0.158 0243 -0.365 10.333 10.483 1 10.483
243 1.507 2 0.369 0643 1.026 13.513 13.663 1 13.663
243 1.507 3 1.118 1043 2.363 15.290 15.440 1 15.440
243 1.507 4 0.341 1443 0.366 14.098 14.248 1 14.248
244 0.318 1 -0.158 0244 0.915 10.204 10.354 1 10.354
244 0.318 2 0.369 0644 0.166 11.370 11.520 1 11.520
244 0.318 3 1.118 1044 0.821 11.328 11.478 1 11.478
244 0.318 4 0.341 1444 0.005 11.817 11.967 1 11.967
245 0.495 1 -0.158 0245 -1.037 10.651 10.801 0 10.651
245 0.495 2 0.369 0645 0.089 12.302 12.452 1 12.452
245 0.495 3 1.118 1045 0.334 13.037 13.187 1 13.187
245 0.495 4 0.341 1445 0.493 12.275 12.425 1 12.425
246 -0.563 1 -0.158 0246 -0.781 8.320 8.470 0 8.320
246 -0.563 2 0.369 0646 0.478 10.773 10.923 1 10.923
246 -0.563 3 1.118 1046 -0.477 10.021 10.171 1 10.171
246 -0.563 4 0.341 1446 -0.241 9.164 9.314 0 9.164
247 -0.337 1 -0.158 0247 0.243 10.137 10.287 1 10.287
247 -0.337 2 0.369 0647 0.034 9.559 9.709 1 9.709
247 -0.337 3 1.118 1047 -0.890 10.598 10.748 1 10.748
247 -0.337 4 0.341 1447 0.411 12.519 12.669 0 12.519
248 0.159 1 -0.158 0248 0.830 10.496 10.646 0 10.496
248 0.159 2 0.369 0648 0.509 11.357 11.507 1 11.507
248 0.159 3 1.118 1048 0.025 12.032 12.182 1 12.182
248 0.159 4 0.341 1448 0.388 9.772 9.922 0 9.772
249 -0.388 1 -0.158 0249 -0.945 8.261 8.411 0 8.261
249 -0.388 2 0.369 0649 -0.251 10.893 11.043 0 10.893
249 -0.388 3 1.118 1049 -0.722 10.463 10.613 1 10.613
249 -0.388 4 0.341 1449 -0.595 10.513 10.663 0 10.513
250 0.086 1 -0.158 0250 0.340 10.291 10.441 1 10.441
250 0.086 2 0.369 0650 -0.119 10.546 10.696 1 10.696
250 0.086 3 1.118 1050 0.007 13.053 13.203 1 13.203
250 0.086 4 0.341 1450 -0.467 10.207 10.357 1 10.357
251 0.167 1 -0.158 0251 0.066 12.087 12.237 0 12.087
251 0.167 2 0.369 0651 -0.523 10.524 10.674 1 10.674
251 0.167 3 1.118 1051 -0.339 12.091 12.241 1 12.241
251 0.167 4 0.341 1451 0.317 11.264 11.414 1 11.414
252 -0.799 1 -0.158 0252 -0.040 6.881 7.031 1 7.031
252 -0.799 2 0.369 0652 -0.353 9.298 9.448 1 9.448
252 -0.799 3 1.118 1052 -1.369 9.003 9.153 1 9.153
252 -0.799 4 0.341 1452 0.185 10.463 10.613 1 10.613
253 -0.424 1 -0.158 0253 -0.692 8.124 8.274 0 8.124
253 -0.424 2 0.369 0653 -0.255 11.798 11.948 0 11.798
253 -0.424 3 1.118 1053 -0.212 9.206 9.356 1 9.356
253 -0.424 4 0.341 1453 -0.179 10.366 10.516 0 10.366
254 1.124 1 -0.158 0254 0.323 12.658 12.808 1 12.808
254 1.124 2 0.369 0654 -0.165 10.822 10.972 1 10.972
254 1.124 3 1.118 1054 1.075 12.993 13.143 1 13.143
254 1.124 4 0.341 1454 0.992 11.499 11.649 1 11.649
255 -0.783 1 -0.158 0255 0.342 8.326 8.476 0 8.326
255 -0.783 2 0.369 0655 0.038 10.886 11.036 0 10.886
255 -0.783 3 1.118 1055 -0.976 10.310 10.460 0 10.310
255 -0.783 4 0.341 1455 -0.061 8.356 8.506 0 8.356
256 0.592 1 -0.158 0256 -0.369 10.778 10.928 1 10.928
256 0.592 2 0.369 0656 1.267 12.050 12.200 1 12.200
256 0.592 3 1.118 1056 0.506 12.738 12.888 1 12.888
256 0.592 4 0.341 1456 -0.640 9.543 9.693 0 9.543
257 -0.147 1 -0.158 0257 0.309 9.637 9.787 0 9.637
257 -0.147 2 0.369 0657 0.232 11.497 11.647 1 11.647
257 -0.147 3 1.118 1057 0.284 13.197 13.347 1 13.347
257 -0.147 4 0.341 1457 -0.182 10.094 10.244 1 10.244
258 -0.139 1 -0.158 0258 0.300 9.422 9.572 0 9.422
258 -0.139 2 0.369 0658 0.875 12.862 13.012 1 13.012
258 -0.139 3 1.118 1058 -0.004 12.650 12.800 1 12.800
258 -0.139 4 0.341 1458 -0.116 9.184 9.334 0 9.184
259 -0.314 1 -0.158 0259 -1.006 7.467 7.617 0 7.467
259 -0.314 2 0.369 0659 -0.279 10.641 10.791 1 10.791
259 -0.314 3 1.118 1059 -0.311 9.741 9.891 1 9.891
259 -0.314 4 0.341 1459 0.327 9.778 9.928 0 9.778
260 1.076 1 -0.158 0260 -0.157 12.392 12.542 1 12.542
260 1.076 2 0.369 0660 0.779 12.741 12.891 1 12.891
260 1.076 3 1.118 1060 1.294 14.146 14.296 1 14.296
260 1.076 4 0.341 1460 0.343 13.190 13.340 1 13.340
261 0.240 1 -0.158 0261 -0.169 9.622 9.772 0 9.622
261 0.240 2 0.369 0661 0.092 10.273 10.423 1 10.423
261 0.240 3 1.118 1061 -0.343 13.149 13.299 1 13.299
261 0.240 4 0.341 1461 0.754 12.163 12.313 1 12.313
262 0.113 1 -0.158 0262 -0.885 9.837 9.987 1 9.987
262 0.113 2 0.369 0662 0.159 10.722 10.872 1 10.872
262 0.113 3 1.118 1062 0.345 12.342 12.492 1 12.492
262 0.113 4 0.341 1462 -0.597 12.013 12.163 1 12.163
263 1.207 1 -0.158 0263 0.228 12.279 12.429 1 12.429
263 1.207 2 0.369 0663 0.239 11.805 11.955 1 11.955
263 1.207 3 1.118 1063 1.172 12.490 12.640 1 12.640
263 1.207 4 0.341 1463 0.042 13.014 13.164 0 13.014
264 -0.932 1 -0.158 0264 0.065 8.562 8.712 1 8.712
264 -0.932 2 0.369 0664 -0.034 10.121 10.271 0 10.121
264 -0.932 3 1.118 1064 -1.504 9.346 9.496 1 9.496
264 -0.932 4 0.341 1464 -0.262 9.410 9.560 0 9.410
265 0.024 1 -0.158 0265 1.056 11.893 12.043 1 12.043
265 0.024 2 0.369 0665 1.093 14.025 14.175 0 14.025
265 0.024 3 1.118 1065 0.152 12.043 12.193 1 12.193
265 0.024 4 0.341 1465 0.253 10.153 10.303 1 10.303
266 0.601 1 -0.158 0266 0.172 13.820 13.970 0 13.820
266 0.601 2 0.369 0666 -0.189 11.711 11.861 1 11.861
266 0.601 3 1.118 1066 0.607 13.392 13.542 1 13.542
266 0.601 4 0.341 1466 0.211 10.852 11.002 0 10.852
267 -0.384 1 -0.158 0267 -0.582 9.150 9.300 0 9.150
267 -0.384 2 0.369 0667 0.336 9.123 9.273 1 9.273
267 -0.384 3 1.118 1067 -0.495 12.254 12.404 1 12.404
267 -0.384 4 0.341 1467 -0.236 11.094 11.244 1 11.244
268 -0.343 1 -0.158 0268 -0.245 10.753 10.903 0 10.753
268 -0.343 2 0.369 0668 -0.016 9.681 9.831 1 9.831
268 -0.343 3 1.118 1068 -0.658 10.082 10.232 1 10.232
268 -0.343 4 0.341 1468 0.305 9.199 9.349 1 9.349
269 -0.040 1 -0.158 0269 0.079 9.550 9.700 0 9.550
269 -0.040 2 0.369 0669 -0.015 9.350 9.500 0 9.350
269 -0.040 3 1.118 1069 -0.134 11.423 11.573 1 11.573
269 -0.040 4 0.341 1469 0.262 11.379 11.529 1 11.529
270 0.612 1 -0.158 0270 0.503 11.818 11.968 0 11.818
270 0.612 2 0.369 0670 0.707 12.232 12.382 0 12.232
270 0.612 3 1.118 1070 0.535 13.442 13.592 1 13.592
270 0.612 4 0.341 1470 0.405 12.181 12.331 1 12.331
271 -0.238 1 -0.158 0271 -0.876 9.978 10.128 0 9.978
271 -0.238 2 0.369 0671 -0.115 10.869 11.019 0 10.869
271 -0.238 3 1.118 1071 -0.382 11.350 11.500 1 11.500
271 -0.238 4 0.341 1471 -0.380 10.060 10.210 0 10.060
272 0.033 1 -0.158 0272 -0.653 9.976 10.126 0 9.976
272 0.033 2 0.369 0672 0.703 10.244 10.394 1 10.394
272 0.033 3 1.118 1072 0.225 11.504 11.654 1 11.654
272 0.033 4 0.341 1472 0.155 9.269 9.419 1 9.419
273 0.048 1 -0.158 0273 0.315 10.608 10.758 0 10.608
273 0.048 2 0.369 0673 0.608 9.643 9.793 0 9.643
273 0.048 3 1.118 1073 0.006 10.056 10.206 1 10.206
273 0.048 4 0.341 1473 -0.123 10.618 10.768 1 10.768
274 -0.520 1 -0.158 0274 -0.451 8.490 8.640 0 8.490
274 -0.520 2 0.369 0674 -0.370 8.859 9.009 1 9.009
274 -0.520 3 1.118 1074 -0.932 11.964 12.114 0 11.964
274 -0.520 4 0.341 1474 -0.608 9.991 10.141 0 9.991
275 0.475 1 -0.158 0275 -0.789 10.547 10.697 0 10.547
275 0.475 2 0.369 0675 0.003 10.412 10.562 1 10.562
275 0.475 3 1.118 1075 0.793 11.700 11.850 1 11.850
275 0.475 4 0.341 1475 -0.216 11.083 11.233 1 11.233
276 -0.356 1 -0.158 0276 0.225 10.652 10.802 0 10.652
276 -0.356 2 0.369 0676 0.144 9.342 9.492 1 9.492
276 -0.356 3 1.118 1076 -1.070 10.132 10.282 1 10.282
276 -0.356 4 0.341 1476 -0.316 11.339 11.489 0 11.339
277 -0.735 1 -0.158 0277 -0.515 9.853 10.003 0 9.853
277 -0.735 2 0.369 0677 -0.225 10.175 10.325 1 10.325
277 -0.735 3 1.118 1077 -1.148 8.853 9.003 0 8.853
277 -0.735 4 0.341 1477 0.136 9.782 9.932 0 9.782
278 -0.607 1 -0.158 0278 0.532 10.688 10.838 0 10.688
278 -0.607 2 0.369 0678 0.039 10.350 10.500 1 10.500
278 -0.607 3 1.118 1078 0.085 11.588 11.738 1 11.738
278 -0.607 4 0.341 1478 -0.164 10.527 10.677 0 10.527
279 -0.240 1 -0.158 0279 0.051 8.978 9.128 1 9.128
279 -0.240 2 0.369 0679 -0.125 9.347 9.497 0 9.347
279 -0.240 3 1.118 1079 -0.062 9.998 10.148 1 10.148
279 -0.240 4 0.341 1479 -0.223 10.392 10.542 1 10.542
280 -0.143 1 -0.158 0280 0.239 9.270 9.420 1 9.420
280 -0.143 2 0.369 0680 0.133 10.320 10.470 0 10.320
280 -0.143 3 1.118 1080 0.927 13.060 13.210 1 13.210
280 -0.143 4 0.341 1480 0.517 12.273 12.423 0 12.273
281 -0.009 1 -0.158 0281 0.566 10.493 10.643 1 10.643
281 -0.009 2 0.369 0681 0.009 10.921 11.071 1 11.071
281 -0.009 3 1.118 1081 -0.047 10.240 10.390 1 10.390
281 -0.009 4 0.341 1481 0.710 12.500 12.650 1 12.650
282 0.086 1 -0.158 0282 0.012 7.560 7.710 1 7.710
282 0.086 2 0.369 0682 -0.150 8.970 9.120 0 8.970
282 0.086 3 1.118 1082 -0.501 10.973 11.123 1 11.123
282 0.086 4 0.341 1482 -0.001 10.653 10.803 0 10.653
283 0.117 1 -0.158 0283 -0.048 10.967 11.117 1 11.117
283 0.117 2 0.369 0683 0.728 12.057 12.207 1 12.207
283 0.117 3 1.118 1083 0.885 13.311 13.461 1 13.461
283 0.117 4 0.341 1483 0.512 10.461 10.611 1 10.611
284 0.360 1 -0.158 0284 0.552 9.398 9.548 0 9.398
284 0.360 2 0.369 0684 -0.103 13.031 13.181 1 13.181
284 0.360 3 1.118 1084 -0.335 10.926 11.076 1 11.076
284 0.360 4 0.341 1484 -0.250 10.756 10.906 1 10.906
285 -0.310 1 -0.158 0285 -0.287 11.065 11.215 0 11.065
285 -0.310 2 0.369 0685 -0.018 11.166 11.316 0 11.166
285 -0.310 3 1.118 1085 -0.171 10.077 10.227 1 10.227
285 -0.310 4 0.341 1485 -0.170 10.297 10.447 1 10.447
286 0.188 1 -0.158 0286 0.197 11.148 11.298 0 11.148
286 0.188 2 0.369 0686 0.697 12.059 12.209 1 12.209
286 0.188 3 1.118 1086 0.172 10.194 10.344 1 10.344
286 0.188 4 0.341 1486 -0.114 8.855 9.005 1 9.005
287 0.366 1 -0.158 0287 0.061 10.826 10.976 1 10.976
287 0.366 2 0.369 0687 0.633 10.300 10.450 1 10.450
287 0.366 3 1.118 1087 0.549 13.176 13.326 1 13.326
287 0.366 4 0.341 1487 0.763 11.723 11.873 0 11.723
288 0.204 1 -0.158 0288 0.973 9.976 10.126 0 9.976
288 0.204 2 0.369 0688 0.725 11.749 11.899 1 11.899
288 0.204 3 1.118 1088 -1.103 10.904 11.054 1 11.054
288 0.204 4 0.341 1488 0.375 12.479 12.629 1 12.629
289 0.560 1 -0.158 0289 0.431 10.236 10.386 0 10.236
289 0.560 2 0.369 0689 0.612 12.669 12.819 0 12.669
289 0.560 3 1.118 1089 1.505 13.239 13.389 1 13.389
289 0.560 4 0.341 1489 -0.616 10.578 10.728 1 10.728
290 0.072 1 -0.158 0290 -0.696 9.815 9.965 1 9.965
290 0.072 2 0.369 0690 0.067 11.584 11.734 1 11.734
290 0.072 3 1.118 1090 0.291 13.155 13.305 1 13.305
290 0.072 4 0.341 1490 0.492 13.074 13.224 1 13.224
291 0.269 1 -0.158 0291 0.046 8.173 8.323 1 8.323
291 0.269 2 0.369 0691 0.091 11.648 11.798 1 11.798
291 0.269 3 1.118 1091 0.463 13.030 13.180 1 13.180
291 0.269 4 0.341 1491 0.130 9.963 10.113 0 9.963
292 -0.370 1 -0.158 0292 0.522 9.432 9.582 0 9.432
292 -0.370 2 0.369 0692 0.553 12.581 12.731 0 12.581
292 -0.370 3 1.118 1092 -1.344 10.684 10.834 0 10.684
292 -0.370 4 0.341 1492 -0.259 9.825 9.975 0 9.825
293 0.253 1 -0.158 0293 0.117 10.673 10.823 0 10.673
293 0.253 2 0.369 0693 0.298 10.958 11.108 1 11.108
293 0.253 3 1.118 1093 0.505 12.415 12.565 1 12.565
293 0.253 4 0.341 1493 -0.439 11.815 11.965 1 11.965
294 -0.103 1 -0.158 0294 -0.237 11.196 11.346 1 11.346
294 -0.103 2 0.369 0694 0.412 11.009 11.159 0 11.009
294 -0.103 3 1.118 1094 -0.057 12.057 12.207 1 12.207
294 -0.103 4 0.341 1494 0.393 10.286 10.436 0 10.286
295 0.159 1 -0.158 0295 -0.003 10.872 11.022 0 10.872
295 0.159 2 0.369 0695 0.276 9.396 9.546 0 9.396
295 0.159 3 1.118 1095 -0.958 11.045 11.195 1 11.195
295 0.159 4 0.341 1495 0.261 10.579 10.729 1 10.729
296 -0.516 1 -0.158 0296 -0.664 10.413 10.563 0 10.413
296 -0.516 2 0.369 0696 0.466 9.189 9.339 1 9.339
296 -0.516 3 1.118 1096 -0.131 9.979 10.129 1 10.129
296 -0.516 4 0.341 1496 -0.530 8.871 9.021 1 9.021
297 -1.135 1 -0.158 0297 0.066 8.039 8.189 0 8.039
297 -1.135 2 0.369 0697 -0.377 10.156 10.306 1 10.306
297 -1.135 3 1.118 1097 -1.827 8.782 8.932 1 8.932
297 -1.135 4 0.341 1497 0.400 10.877 11.027 0 10.877
298 0.544 1 -0.158 0298 0.258 11.235 11.385 1 11.385
298 0.544 2 0.369 0698 0.954 11.966 12.116 1 12.116
298 0.544 3 1.118 1098 1.047 12.178 12.328 1 12.328
298 0.544 4 0.341 1498 0.630 12.482 12.632 0 12.482
299 -0.498 1 -0.158 0299 0.737 12.568 12.718 0 12.568
299 -0.498 2 0.369 0699 -0.512 10.614 10.764 1 10.764
299 -0.498 3 1.118 1099 0.280 10.541 10.691 1 10.691
299 -0.498 4 0.341 1499 0.247 9.724 9.874 0 9.724
300 -0.748 1 -0.158 0300 0.657 10.139 10.289 0 10.139
300 -0.748 2 0.369 0700 -0.256 9.647 9.797 1 9.797
300 -0.748 3 1.118 1100 -0.781 8.703 8.853 1 8.853
300 -0.748 4 0.341 1500 -0.295 9.968 10.118 0 9.968
301 0.200 1 -0.158 0301 0.263 10.497 10.647 0 10.497
301 0.200 2 0.369 0701 0.293 11.812 11.962 0 11.812
301 0.200 3 1.118 1101 0.138 12.942 13.092 1 13.092
301 0.200 4 0.341 1501 0.086 11.938 12.088 1 12.088
302 -0.573 1 -0.158 0302 0.730 10.124 10.274 0 10.124
302 -0.573 2 0.369 0702 -0.291 8.611 8.761 0 8.611
302 -0.573 3 1.118 1102 -0.079 10.771 10.921 1 10.921
302 -0.573 4 0.341 1502 -0.191 10.486 10.636 1 10.636
303 1.225 1 -0.158 0303 -0.455 8.828 8.978 1 8.978
303 1.225 2 0.369 0703 1.307 11.406 11.556 1 11.556
303 1.225 3 1.118 1103 0.666 14.779 14.929 1 14.929
303 1.225 4 0.341 1503 1.484 13.686 13.836 1 13.836
304 -0.198 1 -0.158 0304 0.034 9.491 9.641 0 9.491
304 -0.198 2 0.369 0704 -0.600 9.978 10.128 1 10.128
304 -0.198 3 1.118 1104 -0.334 11.336 11.486 1 11.486
304 -0.198 4 0.341 1504 0.401 11.880 12.030 1 12.030
305 0.742 1 -0.158 0305 -0.205 11.616 11.766 1 11.766
305 0.742 2 0.369 0705 0.821 12.081 12.231 1 12.231
305 0.742 3 1.118 1105 1.018 13.299 13.449 1 13.449
305 0.742 4 0.341 1505 -0.574 11.513 11.663 1 11.663
306 0.395 1 -0.158 0306 -0.039 8.807 8.957 1 8.957
306 0.395 2 0.369 0706 -0.616 10.941 11.091 1 11.091
306 0.395 3 1.118 1106 0.129 10.094 10.244 1 10.244
306 0.395 4 0.341 1506 -0.668 11.595 11.745 1 11.745
307 -0.564 1 -0.158 0307 0.677 9.246 9.396 1 9.396
307 -0.564 2 0.369 0707 0.122 11.725 11.875 1 11.875
307 -0.564 3 1.118 1107 -0.055 10.520 10.670 1 10.670
307 -0.564 4 0.341 1507 -0.883 8.658 8.808 1 8.808
308 0.890 1 -0.158 0308 -0.161 10.932 11.082 1 11.082
308 0.890 2 0.369 0708 1.493 14.307 14.457 1 14.457
308 0.890 3 1.118 1108 2.160 14.502 14.652 1 14.652
308 0.890 4 0.341 1508 0.126 12.424 12.574 1 12.574
309 -0.447 1 -0.158 0309 -0.097 10.037 10.187 0 10.037
309 -0.447 2 0.369 0709 1.263 13.391 13.541 1 13.541
309 -0.447 3 1.118 1109 -0.521 10.026 10.176 1 10.176
309 -0.447 4 0.341 1509 -0.803 9.460 9.610 0 9.460
310 0.192 1 -0.158 0310 0.161 10.024 10.174 0 10.024
310 0.192 2 0.369 0710 0.218 10.960 11.110 0 10.960
310 0.192 3 1.118 1110 0.095 12.172 12.322 1 12.322
310 0.192 4 0.341 1510 0.226 10.011 10.161 0 10.011
311 0.757 1 -0.158 0311 -0.469 11.211 11.361 1 11.361
311 0.757 2 0.369 0711 0.423 11.103 11.253 1 11.253
311 0.757 3 1.118 1111 0.643 12.095 12.245 1 12.245
311 0.757 4 0.341 1511 0.371 12.969 13.119 0 12.969
312 -0.632 1 -0.158 0312 0.646 9.070 9.220 0 9.070
312 -0.632 2 0.369 0712 -0.947 9.897 10.047 0 9.897
312 -0.632 3 1.118 1112 -0.867 10.850 11.000 1 11.000
312 -0.632 4 0.341 1512 0.104 11.546 11.696 0 11.546
313 0.920 1 -0.158 0313 -0.951 10.097 10.247 1 10.247
313 0.920 2 0.369 0713 0.323 12.810 12.960 1 12.960
313 0.920 3 1.118 1113 0.221 13.421 13.571 1 13.571
313 0.920 4 0.341 1513 0.010 12.938 13.088 1 13.088
314 -1.058 1 -0.158 0314 0.101 9.680 9.830 0 9.680
314 -1.058 2 0.369 0714 -0.184 8.255 8.405 0 8.255
314 -1.058 3 1.118 1114 -0.851 10.159 10.309 1 10.309
314 -1.058 4 0.341 1514 -0.187 9.927 10.077 0 9.927
315 0.197 1 -0.158 0315 -0.130 10.415 10.565 1 10.565
315 0.197 2 0.369 0715 0.203 11.933 12.083 0 11.933
315 0.197 3 1.118 1115 -0.162 12.033 12.183 1 12.183
315 0.197 4 0.341 1515 0.203 9.993 10.143 1 10.143
316 -0.325 1 -0.158 0316 0.041 8.722 8.872 1 8.872
316 -0.325 2 0.369 0716 -0.540 9.643 9.793 0 9.643
316 -0.325 3 1.118 1116 -0.212 12.107 12.257 1 12.257
316 -0.325 4 0.341 1516 -0.602 7.894 8.044 0 7.894
317 -0.169 1 -0.158 0317 -0.084 11.359 11.509 0 11.359
317 -0.169 2 0.369 0717 -0.075 10.866 11.016 0 10.866
317 -0.169 3 1.118 1117 -0.034 13.162 13.312 1 13.312
317 -0.169 4 0.341 1517 0.373 9.335 9.485 0 9.335
318 -0.422 1 -0.158 0318 -0.314 12.244 12.394 1 12.394
318 -0.422 2 0.369 0718 0.583 12.078 12.228 1 12.228
318 -0.422 3 1.118 1118 -1.158 9.059 9.209 1 9.209
318 -0.422 4 0.341 1518 -0.118 10.333 10.483 0 10.333
319 0.716 1 -0.158 0319 -0.224 8.728 8.878 1 8.878
319 0.716 2 0.369 0719 0.615 11.672 11.822 1 11.822
319 0.716 3 1.118 1119 0.040 11.468 11.618 1 11.618
319 0.716 4 0.341 1519 0.132 12.858 13.008 1 13.008
320 -0.008 1 -0.158 0320 -0.356 9.898 10.048 0 9.898
320 -0.008 2 0.369 0720 0.386 12.702 12.852 0 12.702
320 -0.008 3 1.118 1120 -0.647 10.939 11.089 1 11.089
320 -0.008 4 0.341 1520 0.726 10.681 10.831 1 10.831
321 -0.768 1 -0.158 0321 0.271 9.776 9.926 0 9.776
321 -0.768 2 0.369 0721 -0.163 11.085 11.235 0 11.085
321 -0.768 3 1.118 1121 -0.674 10.445 10.595 0 10.445
321 -0.768 4 0.341 1521 -0.160 6.848 6.998 0 6.848
322 -0.613 1 -0.158 0322 0.194 10.749 10.899 1 10.899
322 -0.613 2 0.369 0722 0.001 9.396 9.546 0 9.396
322 -0.613 3 1.118 1122 -0.466 9.006 9.156 0 9.006
322 -0.613 4 0.341 1522 -1.356 6.388 6.538 0 6.388
323 0.044 1 -0.158 0323 0.124 10.245 10.395 1 10.395
323 0.044 2 0.369 0723 0.120 11.057 11.207 1 11.207
323 0.044 3 1.118 1123 0.423 10.299 10.449 1 10.449
323 0.044 4 0.341 1523 0.119 9.757 9.907 1 9.907
324 0.728 1 -0.158 0324 0.034 11.084 11.234 1 11.234
324 0.728 2 0.369 0724 0.142 9.824 9.974 1 9.974
324 0.728 3 1.118 1124 0.698 12.433 12.583 1 12.583
324 0.728 4 0.341 1524 0.714 13.755 13.905 1 13.905
325 -1.043 1 -0.158 0325 1.183 10.252 10.402 0 10.252
325 -1.043 2 0.369 0725 -0.272 9.904 10.054 0 9.904
325 -1.043 3 1.118 1125 -1.380 10.207 10.357 1 10.357
325 -1.043 4 0.341 1525 -0.569 9.537 9.687 1 9.687
326 -0.361 1 -0.158 0326 0.388 11.055 11.205 1 11.205
326 -0.361 2 0.369 0726 -0.368 8.905 9.055 0 8.905
326 -0.361 3 1.118 1126 -0.321 11.113 11.263 1 11.263
326 -0.361 4 0.341 1526 -0.393 10.048 10.198 1 10.198
327 0.128 1 -0.158 0327 0.958 11.653 11.803 1 11.803
327 0.128 2 0.369 0727 0.521 12.663 12.813 0 12.663
327 0.128 3 1.118 1127 0.674 12.700 12.850 1 12.850
327 0.128 4 0.341 1527 -1.071 8.821 8.971 1 8.971
328 0.556 1 -0.158 0328 0.172 11.229 11.379 1 11.379
328 0.556 2 0.369 0728 0.449 11.945 12.095 1 12.095
328 0.556 3 1.118 1128 0.525 11.358 11.508 1 11.508
328 0.556 4 0.341 1528 0.911 13.642 13.792 1 13.792
329 -0.175 1 -0.158 0329 -0.307 8.761 8.911 0 8.761
329 -0.175 2 0.369 0729 -0.779 8.967 9.117 0 8.967
329 -0.175 3 1.118 1129 -0.183 11.459 11.609 1 11.609
329 -0.175 4 0.341 1529 -0.282 9.184 9.334 1 9.334
330 -0.228 1 -0.158 0330 0.734 10.226 10.376 0 10.226
330 -0.228 2 0.369 0730 0.469 13.049 13.199 1 13.199
330 -0.228 3 1.118 1130 0.503 12.058 12.208 1 12.208
330 -0.228 4 0.341 1530 0.367 12.994 13.144 0 12.994
331 -0.017 1 -0.158 0331 -0.844 9.284 9.434 1 9.434
331 -0.017 2 0.369 0731 0.659 9.768 9.918 1 9.918
331 -0.017 3 1.118 1131 -0.516 9.517 9.667 1 9.667
331 -0.017 4 0.341 1531 -0.278 11.264 11.414 0 11.264
332 0.122 1 -0.158 0332 -0.143 9.818 9.968 1 9.968
332 0.122 2 0.369 0732 0.903 10.882 11.032 1 11.032
332 0.122 3 1.118 1132 0.060 10.908 11.058 0 10.908
332 0.122 4 0.341 1532 -0.703 9.635 9.785 0 9.635
333 0.587 1 -0.158 0333 -0.643 9.223 9.373 1 9.373
333 0.587 2 0.369 0733 0.546 10.932 11.082 1 11.082
333 0.587 3 1.118 1133 1.654 12.807 12.957 1 12.957
333 0.587 4 0.341 1533 0.581 12.668 12.818 1 12.818
334 0.438 1 -0.158 0334 0.101 10.999 11.149 1 11.149
334 0.438 2 0.369 0734 0.456 11.856 12.006 0 11.856
334 0.438 3 1.118 1134 1.298 12.575 12.725 1 12.725
334 0.438 4 0.341 1534 -0.201 12.067 12.217 1 12.217
335 -0.388 1 -0.158 0335 0.386 10.915 11.065 1 11.065
335 -0.388 2 0.369 0735 0.303 9.129 9.279 0 9.129
335 -0.388 3 1.118 1135 0.335 10.803 10.953 1 10.953
335 -0.388 4 0.341 1535 0.414 11.649 11.799 1 11.799
336 -0.152 1 -0.158 0336 -0.921 9.111 9.261 0 9.111
336 -0.152 2 0.369 0736 -0.035 9.788 9.938 1 9.938
336 -0.152 3 1.118 1136 0.222 12.985 13.135 1 13.135
336 -0.152 4 0.341 1536 -0.412 12.244 12.394 0 12.244
337 0.058 1 -0.158 0337 0.554 12.443 12.593 1 12.593
337 0.058 2 0.369 0737 0.132 10.533 10.683 1 10.683
337 0.058 3 1.118 1137 0.670 12.176 12.326 1 12.326
337 0.058 4 0.341 1537 -0.070 9.759 9.909 0 9.759
338 -0.262 1 -0.158 0338 0.850 11.901 12.051 0 11.901
338 -0.262 2 0.369 0738 -0.316 9.342 9.492 1 9.492
338 -0.262 3 1.118 1138 -0.369 11.659 11.809 1 11.809
338 -0.262 4 0.341 1538 -0.420 10.297 10.447 1 10.447
339 0.450 1 -0.158 0339 0.054 11.594 11.744 1 11.744
339 0.450 2 0.369 0739 0.142 12.438 12.588 1 12.588
339 0.450 3 1.118 1139 0.650 12.380 12.530 1 12.530
339 0.450 4 0.341 1539 0.586 12.120 12.270 0 12.120
340 -0.118 1 -0.158 0340 0.734 8.559 8.709 1 8.709
340 -0.118 2 0.369 0740 -0.131 9.113 9.263 1 9.263
340 -0.118 3 1.118 1140 -0.046 11.558 11.708 1 11.708
340 -0.118 4 0.341 1540 0.086 11.321 11.471 0 11.321
341 -0.530 1 -0.158 0341 -0.945 8.190 8.340 1 8.340
341 -0.530 2 0.369 0741 -0.072 10.735 10.885 1 10.885
341 -0.530 3 1.118 1141 0.237 10.155 10.305 1 10.305
341 -0.530 4 0.341 1541 0.408 10.347 10.497 1 10.497
342 -0.003 1 -0.158 0342 0.093 10.475 10.625 1 10.625
342 -0.003 2 0.369 0742 0.385 10.841 10.991 1 10.991
342 -0.003 3 1.118 1142 0.786 11.210 11.360 1 11.360
342 -0.003 4 0.341 1542 -0.209 11.329 11.479 1 11.479
343 -0.334 1 -0.158 0343 0.188 11.324 11.474 0 11.324
343 -0.334 2 0.369 0743 -0.380 9.535 9.685 0 9.535
343 -0.334 3 1.118 1143 -0.853 10.947 11.097 1 11.097
343 -0.334 4 0.341 1543 0.201 11.732 11.882 1 11.882
344 0.517 1 -0.158 0344 -0.001 8.842 8.992 0 8.842
344 0.517 2 0.369 0744 0.057 11.120 11.270 1 11.270
344 0.517 3 1.118 1144 -0.071 11.272 11.422 1 11.422
344 0.517 4 0.341 1544 0.610 11.184 11.334 1 11.334
345 0.199 1 -0.158 0345 0.171 10.225 10.375 0 10.225
345 0.199 2 0.369 0745 0.127 11.613 11.763 1 11.763
345 0.199 3 1.118 1145 0.583 12.484 12.634 0 12.484
345 0.199 4 0.341 1545 0.164 9.916 10.066 1 10.066
346 0.255 1 -0.158 0346 0.117 11.200 11.350 1 11.350
346 0.255 2 0.369 0746 -0.207 11.308 11.458 1 11.458
346 0.255 3 1.118 1146 0.477 11.818 11.968 1 11.968
346 0.255 4 0.341 1546 -0.019 8.971 9.121 1 9.121
347 0.370 1 -0.158 0347 -0.629 10.948 11.098 1 11.098
347 0.370 2 0.369 0747 0.312 13.253 13.403 1 13.403
347 0.370 3 1.118 1147 0.880 12.811 12.961 1 12.961
347 0.370 4 0.341 1547 -0.199 10.300 10.450 1 10.450
348 -1.283 1 -0.158 0348 0.856 10.239 10.389 0 10.239
348 -1.283 2 0.369 0748 0.299 10.107 10.257 0 10.107
348 -1.283 3 1.118 1148 -1.752 7.946 8.096 1 8.096
348 -1.283 4 0.341 1548 -0.768 8.797 8.947 0 8.797
349 -1.159 1 -0.158 0349 0.481 8.347 8.497 0 8.347
349 -1.159 2 0.369 0749 -1.260 9.987 10.137 0 9.987
349 -1.159 3 1.118 1149 -1.307 9.137 9.287 0 9.137
349 -1.159 4 0.341 1549 -0.354 7.742 7.892 1 7.892
350 -0.150 1 -0.158 0350 -0.267 7.585 7.735 0 7.585
350 -0.150 2 0.369 0750 0.183 11.602 11.752 1 11.752
350 -0.150 3 1.118 1150 0.512 9.610 9.760 0 9.610
350 -0.150 4 0.341 1550 0.190 9.622 9.772 1 9.772
351 0.700 1 -0.158 0351 0.516 9.987 10.137 1 10.137
351 0.700 2 0.369 0751 -0.550 10.451 10.601 1 10.601
351 0.700 3 1.118 1151 0.955 11.903 12.053 1 12.053
351 0.700 4 0.341 1551 -0.098 11.653 11.803 1 11.803
352 0.765 1 -0.158 0352 0.293 11.722 11.872 1 11.872
352 0.765 2 0.369 0752 -0.124 11.705 11.855 1 11.855
352 0.765 3 1.118 1152 1.398 12.689 12.839 1 12.839
352 0.765 4 0.341 1552 -0.524 10.208 10.358 1 10.358
353 -0.264 1 -0.158 0353 0.069 10.225 10.375 0 10.225
353 -0.264 2 0.369 0753 -0.022 9.614 9.764 1 9.764
353 -0.264 3 1.118 1153 -0.716 10.328 10.478 1 10.478
353 -0.264 4 0.341 1553 -0.585 10.243 10.393 1 10.393
354 0.067 1 -0.158 0354 -0.619 9.972 10.122 0 9.972
354 0.067 2 0.369 0754 -0.836 10.941 11.091 1 11.091
354 0.067 3 1.118 1154 0.128 11.186 11.336 0 11.186
354 0.067 4 0.341 1554 0.137 10.718 10.868 0 10.718
355 1.056 1 -0.158 0355 0.323 11.434 11.584 1 11.584
355 1.056 2 0.369 0755 0.396 12.514 12.664 1 12.664
355 1.056 3 1.118 1155 1.584 13.967 14.117 0 13.967
355 1.056 4 0.341 1555 -0.032 12.552 12.702 1 12.702
356 0.008 1 -0.158 0356 0.445 11.303 11.453 1 11.453
356 0.008 2 0.369 0756 1.516 12.384 12.534 0 12.384
356 0.008 3 1.118 1156 0.435 11.141 11.291 1 11.291
356 0.008 4 0.341 1556 0.051 11.849 11.999 1 11.999
357 0.451 1 -0.158 0357 1.219 12.654 12.804 1 12.804
357 0.451 2 0.369 0757 -0.512 10.106 10.256 0 10.106
357 0.451 3 1.118 1157 0.373 12.138 12.288 1 12.288
357 0.451 4 0.341 1557 0.161 11.383 11.533 0 11.383
358 -0.065 1 -0.158 0358 -0.191 11.312 11.462 1 11.462
358 -0.065 2 0.369 0758 -0.045 10.324 10.474 0 10.324
358 -0.065 3 1.118 1158 0.859 11.721 11.871 0 11.721
358 -0.065 4 0.341 1558 -0.536 11.075 11.225 1 11.225
359 -0.305 1 -0.158 0359 0.028 12.229 12.379 0 12.229
359 -0.305 2 0.369 0759 0.108 11.982 12.132 1 12.132
359 -0.305 3 1.118 1159 -0.547 10.936 11.086 1 11.086
359 -0.305 4 0.341 1559 0.052 12.430 12.580 0 12.430
360 -0.479 1 -0.158 0360 -0.726 8.120 8.270 0 8.120
360 -0.479 2 0.369 0760 -0.402 9.842 9.992 0 9.842
360 -0.479 3 1.118 1160 -1.208 10.780 10.930 0 10.780
360 -0.479 4 0.341 1560 -0.107 12.222 12.372 0 12.222
361 -0.345 1 -0.158 0361 -0.862 7.156 7.306 1 7.306
361 -0.345 2 0.369 0761 -0.497 8.675 8.825 0 8.675
361 -0.345 3 1.118 1161 -0.328 8.783 8.933 1 8.933
361 -0.345 4 0.341 1561 -0.393 8.720 8.870 1 8.870
362 -0.483 1 -0.158 0362 0.832 10.849 10.999 1 10.999
362 -0.483 2 0.369 0762 -0.093 10.891 11.041 0 10.891
362 -0.483 3 1.118 1162 -0.569 9.931 10.081 1 10.081
362 -0.483 4 0.341 1562 -0.120 9.617 9.767 1 9.767
363 -0.484 1 -0.158 0363 0.813 12.017 12.167 0 12.017
363 -0.484 2 0.369 0763 -0.978 9.935 10.085 0 9.935
363 -0.484 3 1.118 1163 -0.548 10.254 10.404 0 10.254
363 -0.484 4 0.341 1563 -0.101 7.858 8.008 1 8.008
364 -0.782 1 -0.158 0364 -0.546 9.664 9.814 0 9.664
364 -0.782 2 0.369 0764 0.617 12.060 12.210 0 12.060
364 -0.782 3 1.118 1164 -0.843 10.809 10.959 1 10.959
364 -0.782 4 0.341 1564 -0.490 8.420 8.570 1 8.570
365 0.065 1 -0.158 0365 -0.212 8.347 8.497 0 8.347
365 0.065 2 0.369 0765 -0.723 10.461 10.611 0 10.461
365 0.065 3 1.118 1165 0.012 10.687 10.837 1 10.837
365 0.065 4 0.341 1565 0.051 10.449 10.599 0 10.449
366 -1.416 1 -0.158 0366 0.852 9.065 9.215 0 9.065
366 -1.416 2 0.369 0766 -0.586 8.359 8.509 0 8.359
366 -1.416 3 1.118 1166 -1.907 8.092 8.242 1 8.242
366 -1.416 4 0.341 1566 -0.395 9.129 9.279 0 9.129
367 0.331 1 -0.158 0367 0.262 9.084 9.234 1 9.234
367 0.331 2 0.369 0767 -0.082 11.022 11.172 1 11.172
367 0.331 3 1.118 1167 0.372 11.891 12.041 1 12.041
367 0.331 4 0.341 1567 0.164 11.198 11.348 0 11.198
368 -0.491 1 -0.158 0368 -0.500 9.877 10.027 1 10.027
368 -0.491 2 0.369 0768 -0.005 9.935 10.085 0 9.935
368 -0.491 3 1.118 1168 -0.148 10.247 10.397 0 10.247
368 -0.491 4 0.341 1568 0.419 9.618 9.768 0 9.618
369 1.197 1 -0.158 0369 -1.048 10.219 10.369 1 10.369
369 1.197 2 0.369 0769 -0.438 10.090 10.240 1 10.240
369 1.197 3 1.118 1169 1.515 15.434 15.584 1 15.584
369 1.197 4 0.341 1569 0.340 10.262 10.412 1 10.412
370 -0.316 1 -0.158 0370 0.669 10.285 10.435 1 10.435
370 -0.316 2 0.369 0770 0.661 9.651 9.801 1 9.801
370 -0.316 3 1.118 1170 -0.255 11.577 11.727 1 11.727
370 -0.316 4 0.341 1570 0.030 9.220 9.370 1 9.370
371 -0.076 1 -0.158 0371 -0.116 9.655 9.805 0 9.655
371 -0.076 2 0.369 0771 -0.145 12.378 12.528 0 12.378
371 -0.076 3 1.118 1171 0.368 10.034 10.184 1 10.184
371 -0.076 4 0.341 1571 -0.442 8.479 8.629 1 8.629
372 -0.206 1 -0.158 0372 0.042 10.747 10.897 0 10.747
372 -0.206 2 0.369 0772 0.126 10.549 10.699 1 10.699
372 -0.206 3 1.118 1172 -0.506 11.037 11.187 1 11.187
372 -0.206 4 0.341 1572 -0.598 8.800 8.950 1 8.950
373 0.159 1 -0.158 0373 -0.336 8.433 8.583 1 8.583
373 0.159 2 0.369 0773 0.796 11.083 11.233 0 11.083
373 0.159 3 1.118 1173 -0.720 12.400 12.550 1 12.550
373 0.159 4 0.341 1573 -0.181 11.123 11.273 1 11.273
374 -0.216 1 -0.158 0374 -0.084 9.904 10.054 0 9.904
374 -0.216 2 0.369 0774 0.164 9.607 9.757 1 9.757
374 -0.216 3 1.118 1174 0.134 11.107 11.257 1 11.257
374 -0.216 4 0.341 1574 -0.040 11.307 11.457 0 11.307
375 -0.659 1 -0.158 0375 0.549 9.754 9.904 0 9.754
375 -0.659 2 0.369 0775 0.113 8.519 8.669 1 8.669
375 -0.659 3 1.118 1175 -0.385 9.866 10.016 0 9.866
375 -0.659 4 0.341 1575 0.003 9.787 9.937 1 9.937
376 1.067 1 -0.158 0376 0.396 10.569 10.719 1 10.719
376 1.067 2 0.369 0776 0.814 12.940 13.090 1 13.090
376 1.067 3 1.118 1176 0.519 12.654 12.804 1 12.804
376 1.067 4 0.341 1576 0.572 10.957 11.107 1 11.107
377 -0.138 1 -0.158 0377 0.600 11.627 11.777 0 11.627
377 -0.138 2 0.369 0777 -0.481 9.192 9.342 1 9.342
377 -0.138 3 1.118 1177 -0.066 12.268 12.418 1 12.418
377 -0.138 4 0.341 1577 -0.170 10.517 10.667 1 10.667
378 0.594 1 -0.158 0378 0.196 11.301 11.451 0 11.301
378 0.594 2 0.369 0778 0.071 11.483 11.633 1 11.633
378 0.594 3 1.118 1178 0.643 15.535 15.685 1 15.685
378 0.594 4 0.341 1578 0.564 12.141 12.291 0 12.141
379 -0.977 1 -0.158 0379 -0.472 8.591 8.741 1 8.741
379 -0.977 2 0.369 0779 -0.582 7.241 7.391 1 7.391
379 -0.977 3 1.118 1179 -0.798 10.200 10.350 0 10.200
379 -0.977 4 0.341 1579 0.302 11.036 11.186 0 11.036
380 -1.037 1 -0.158 0380 -0.431 6.429 6.579 0 6.429
380 -1.037 2 0.369 0780 -1.187 11.433 11.583 0 11.433
380 -1.037 3 1.118 1180 -0.317 10.166 10.316 1 10.316
380 -1.037 4 0.341 1580 -0.553 10.995 11.145 0 10.995
381 -0.615 1 -0.158 0381 -0.237 8.615 8.765 0 8.615
381 -0.615 2 0.369 0781 -0.733 9.677 9.827 0 9.677
381 -0.615 3 1.118 1181 -0.562 9.452 9.602 1 9.602
381 -0.615 4 0.341 1581 0.338 10.671 10.821 0 10.671
382 0.066 1 -0.158 0382 0.609 10.982 11.132 0 10.982
382 0.066 2 0.369 0782 0.791 11.148 11.298 0 11.148
382 0.066 3 1.118 1182 0.297 11.737 11.887 1 11.887
382 0.066 4 0.341 1582 0.201 11.322 11.472 1 11.472
383 -0.568 1 -0.158 0383 0.925 7.761 7.911 0 7.761
383 -0.568 2 0.369 0783 -0.428 7.911 8.061 0 7.911
383 -0.568 3 1.118 1183 -0.757 10.718 10.868 0 10.718
383 -0.568 4 0.341 1583 0.657 12.198 12.348 1 12.348
384 0.356 1 -0.158 0384 -0.779 8.373 8.523 0 8.373
384 0.356 2 0.369 0784 0.035 11.672 11.822 1 11.822
384 0.356 3 1.118 1184 0.500 9.685 9.835 1 9.835
384 0.356 4 0.341 1584 0.612 10.249 10.399 0 10.249
385 0.167 1 -0.158 0385 0.171 10.694 10.844 0 10.694
385 0.167 2 0.369 0785 -0.116 10.196 10.346 1 10.346
385 0.167 3 1.118 1185 0.248 12.313 12.463 0 12.313
385 0.167 4 0.341 1585 -0.046 11.892 12.042 1 12.042
386 0.144 1 -0.158 0386 -0.410 10.103 10.253 0 10.103
386 0.144 2 0.369 0786 0.840 10.594 10.744 1 10.744
386 0.144 3 1.118 1186 0.904 10.420 10.570 1 10.570
386 0.144 4 0.341 1586 -0.228 10.762 10.912 1 10.912
387 0.685 1 -0.158 0387 -0.841 11.748 11.898 0 11.748
387 0.685 2 0.369 0787 0.581 11.174 11.324 0 11.174
387 0.685 3 1.118 1187 1.293 14.553 14.703 1 14.703
387 0.685 4 0.341 1587 0.011 11.768 11.918 1 11.918
388 -0.318 1 -0.158 0388 -0.355 9.684 9.834 0 9.684
388 -0.318 2 0.369 0788 0.760 10.507 10.657 1 10.657
388 -0.318 3 1.118 1188 0.325 13.413 13.563 1 13.563
388 -0.318 4 0.341 1588 -0.390 10.437 10.587 1 10.587
389 0.213 1 -0.158 0389 0.164 9.904 10.054 1 10.054
389 0.213 2 0.369 0789 0.207 11.713 11.863 1 11.863
389 0.213 3 1.118 1189 0.089 11.284 11.434 1 11.434
389 0.213 4 0.341 1589 -0.718 9.950 10.100 1 10.100
390 0.190 1 -0.158 0390 -0.696 9.564 9.714 0 9.564
390 0.190 2 0.369 0790 0.216 10.987 11.137 1 11.137
390 0.190 3 1.118 1190 0.883 13.171 13.321 1 13.321
390 0.190 4 0.341 1590 0.596 10.688 10.838 1 10.838
391 -0.093 1 -0.158 0391 0.230 12.514 12.664 0 12.514
391 -0.093 2 0.369 0791 0.023 9.143 9.293 1 9.293
391 -0.093 3 1.118 1191 -0.103 11.512 11.662 0 11.512
391 -0.093 4 0.341 1591 0.180 11.254 11.404 1 11.404
392 0.090 1 -0.158 0392 0.362 11.274 11.424 1 11.424
392 0.090 2 0.369 0792 1.300 10.966 11.116 1 11.116
392 0.090 3 1.118 1192 0.319 12.574 12.724 1 12.724
392 0.090 4 0.341 1592 -0.128 11.251 11.401 0 11.251
393 0.024 1 -0.158 0393 -1.500 10.446 10.596 1 10.596
393 0.024 2 0.369 0793 -0.480 8.489 8.639 1 8.639
393 0.024 3 1.118 1193 0.046 10.369 10.519 1 10.519
393 0.024 4 0.341 1593 0.542 11.813 11.963 1 11.963
394 0.730 1 -0.158 0394 0.105 10.473 10.623 1 10.623
394 0.730 2 0.369 0794 0.517 11.659 11.809 0 11.659
394 0.730 3 1.118 1194 0.849 12.531 12.681 1 12.681
394 0.730 4 0.341 1594 -0.063 12.115 12.265 1 12.265
395 -0.142 1 -0.158 0395 0.275 9.925 10.075 0 9.925
395 -0.142 2 0.369 0795 0.347 11.612 11.762 0 11.612
395 -0.142 3 1.118 1195 -0.868 10.816 10.966 1 10.966
395 -0.142 4 0.341 1595 -0.063 11.255 11.405 1 11.405
396 -0.216 1 -0.158 0396 -0.318 8.610 8.760 0 8.610
396 -0.216 2 0.369 0796 -0.780 9.106 9.256 0 9.106
396 -0.216 3 1.118 1196 0.676 13.224 13.374 1 13.374
396 -0.216 4 0.341 1596 -0.267 9.933 10.083 0 9.933
397 -0.238 1 -0.158 0397 -0.628 10.902 11.052 0 10.902
397 -0.238 2 0.369 0797 0.187 10.491 10.641 1 10.641
397 -0.238 3 1.118 1197 -0.143 10.114 10.264 1 10.264
397 -0.238 4 0.341 1597 -0.183 8.604 8.754 1 8.754
398 0.190 1 -0.158 0398 -0.319 9.686 9.836 0 9.686
398 0.190 2 0.369 0798 0.153 10.168 10.318 1 10.318
398 0.190 3 1.118 1198 0.020 11.360 11.510 1 11.510
398 0.190 4 0.341 1598 -0.498 10.454 10.604 1 10.604
399 -0.450 1 -0.158 0399 0.663 10.261 10.411 0 10.261
399 -0.450 2 0.369 0799 0.551 11.374 11.524 1 11.524
399 -0.450 3 1.118 1199 -0.160 10.271 10.421 1 10.421
399 -0.450 4 0.341 1599 -0.172 10.346 10.496 1 10.496
400 0.192 1 -0.158 0400 0.251 10.830 10.980 0 10.830
400 0.192 2 0.369 0800 -0.622 11.105 11.255 1 11.255
400 0.192 3 1.118 1200 0.838 11.686 11.836 1 11.836
400 0.192 4 0.341 1600 0.310 12.402 12.552 1 12.552

Note that I build in constant, additive effects:

# Check to see the true effect is 0.15

panel_dat %>% group_by(individual) %>% 
  summarise(within_person = mean(Y1) - mean(Y0)) %>%
  summarise(Overall = mean(within_person))
## # A tibble: 1 x 1
##   Overall
##     <dbl>
## 1    0.15

Exercise I

We will now assess different estimation strategies. Please set.seed(46) and create the data using my code. Next, estimate the following models:

  • A naive regression of Y on union_membership

  • A regression of Y on union_membership that controls for time_varying_covariate

  • A “within-person” or fixed-effects model that allows a separate intercept for each individual (i.e. \(N-1\) dummy variables)

  • A “two-way fixed effects” model that restricts the comparison to “within-person” and “within-time period”.

How do these estimation strategies fare? Which strategy comes closest to the true effect, \(\rho = 0.15\)?

Answer:

Here is the regression output from my code:

# Different estimation strategies

model1 = lm_robust(Y ~ union_membership,
                   data = panel_dat) %>%
                   extract.lm_robust(include.ci = FALSE)

model2 = lm_robust(Y ~ union_membership + time_varying_covariate,
                   data = panel_dat) %>%
                   extract.lm_robust(include.ci = FALSE)

model3 = lm_robust(
  Y ~ union_membership + time_varying_covariate,
  fixed_effects = ~ individual,
  data = panel_dat
  ) %>%
  extract.lm_robust(include.ci = FALSE)

model4 = lm_robust(
  Y ~ union_membership + time_varying_covariate,
  fixed_effects = ~ individual + time,
  data = panel_dat
  ) %>%
  extract.lm_robust(include.ci = FALSE)

model5 = lm_robust(
  Y ~ union_membership + time_varying_covariate + ability + time_trend,
  data = panel_dat
  ) %>%
  extract.lm_robust(include.ci = FALSE)


texreg::htmlreg(
  list(model1, model2, model3, model4, model5),
  stars = c(0.001, 0.01, 0.05),
  custom.coef.names = c("(Intercept)", "Union Membership", "Time Varying Covariate", "Ability", "Time Trend"),
  custom.model.names = c("Bivariate", "Control for X", "Individual FEs", "Time+Indv FEs", "Control for X + Unobservables"),
  digits = 3,
  caption = "Fixed Effects Estimation"
  )
Fixed Effects Estimation
Bivariate Control for X Individual FEs Time+Indv FEs Control for X + Unobservables
(Intercept) 10.308*** 10.398*** 10.360***
(0.054) (0.050) (0.047)
Union Membership 0.909*** 0.751*** 0.577*** 0.165* 0.156**
(0.070) (0.062) (0.071) (0.069) (0.059)
Time Varying Covariate 1.236*** 0.954*** 0.909*** 0.938***
(0.054) (0.060) (0.052) (0.047)
Ability 1.007***
(0.052)
Time Trend 1.016***
(0.059)
R2 0.091 0.331 0.543 0.633 0.512
Adj. R2 0.090 0.330 0.390 0.508 0.511
Num. obs. 1600 1600 1600 1600 1600
RMSE 1.378 1.182 1.128 1.013 1.010
p < 0.001, p < 0.01, p < 0.05

Partial Regression Framework

Claim: Including fixed effects is algebrically the same as de-meaning each variable.

To show this, I go through the regression anatomy of a fixed effects model. I will show that:

  • When we residualize an outcome, or exposure variable, we remove all the variation due to time-invariant characteristics (\(A_i\)), common shocks in a time period (\(\lambda_t\)), and time-variant characteristics (\(X_{it}\)). The remaining variation in earnings and union_membership is entirely within-person. We leverage this variation to see how changes in union_membership affect earnings.

  • The estimate from a “res-res” regression is exactly equal to the two-way fixed effects coefficient \(\rho\).

Here is what the residualized values look like:

# Partial Regression Approach 

# Residualize Y and Union Membership
fit1 = lm_robust(Y ~ time_varying_covariate +
                   as.factor(individual) +
                   as.factor(time),
                   data = panel_dat)

fit2 = lm_robust(
  union_membership ~ time_varying_covariate +
  as.factor(individual) +
  as.factor(time),
  data = panel_dat
  )

panel_dat$Y_resid = panel_dat$Y - fit1$fitted.values

panel_dat$D_resid = panel_dat$union_membership - fit2$fitted.values

# Visualize the residualized data

ggplot(panel_dat, aes(x = D_resid, y = Y_resid)) +
  geom_point(color = "red", alpha = 0.05) +
  geom_smooth(method = "lm_robust") +
  xlab("Residualized Union Membership") +
  ylab("Residualized Earnings") +
  ggtitle("FE Model: Residualizing Y and D") +
  theme_economist_white(gray_bg = TRUE)

And here are the results from a regression of \(\tilde{Y_{it}} \sim \tilde{D_{it}}\):

# Running the regression

model5 = lm_robust(Y_resid ~ D_resid, panel_dat) %>% 
  extract.lm_robust(include.ci = FALSE)

texreg::htmlreg(
  model5,
  stars = c(0.001, 0.01, 0.05),
  custom.coef.names = c("(Intercept)", "Union Membership"),
  custom.model.names = c("2 Way Fixed Effects + Controls"),
  digits = 3,
  caption = "Recovering FE Estimates using FWL Theorem"
  )
Recovering FE Estimates using FWL Theorem
2 Way Fixed Effects + Controls
(Intercept) -0.000
(0.022)
Union Membership 0.165**
(0.060)
R2 0.005
Adj. R2 0.004
Num. obs. 1600
RMSE 0.876
p < 0.001, p < 0.01, p < 0.05

Regression Weights

We know from Aronow and Samii (2016) that an ordinary least squares regression weights observations by the conditional variance of treatment. This has the following implication for a fixed effects setup:

OLS drops all individuals whose union membership status, \(D_{it}\), does not change in the study period. This is because \(w_{ols} \propto Var(D_{it})\) and these individuals have \(Var(D_{it}) = 0\)

By implication, the treatment effect estimate (\(\rho\)) is the same when we use the full dataset, and when we subset to only those individuals with \(Var(D_{it}) \neq 0\).

However, there is a caveat to this. If we include covariates in the fixed effects model, individuals with \(Var(D_{it}) = 0\) may not be entirely dropped from the estimation procedure. This is because the residualized values of \(D_{it}\) for an individual may not all be the same. Consequently, \(Var(\tilde{D_{it}})\) is not exactly zero. I show this in two ways:

  1. Using the residuals, \(\tilde{D_{it}}\), from the regression anatomy case (above), I estimate \(Var(\tilde{D_{it}})\) for each individual. I then compare this to the \(Var(D_{it})\).

  2. I plot these two quantities separately for those who might be considered Dropped (\(Var(D_{it}) = 0\)), and those Included in the fixed effects estimation:

# REGRESSION WEIGHTS

cond_variance_dat = panel_dat %>% group_by(individual) %>%
  summarise(
  var_unionmembership = var(union_membership),
  var_Dresid = var(D_resid)
  )

cond_variance_dat %>% 
  kable(format = "html", 
        col.names = c("Individual", "Var(Union Membership)", "Var(D_resid)"),
        digits = 3
        ) %>% 
  kable_styling(bootstrap_options = "striped", 
                                     full_width = FALSE, 
                                     fixed_thead = TRUE) %>% 
  scroll_box(width = "750px", height = "250px")
Individual Var(Union Membership) Var(D_resid)
001 0.333 0.236
002 0.250 0.164
003 0.000 0.028
004 0.250 0.265
005 0.333 0.251
006 0.250 0.134
007 0.000 0.027
008 0.250 0.160
009 0.000 0.026
010 0.333 0.236
011 0.333 0.222
012 0.250 0.168
013 0.000 0.035
014 0.250 0.149
015 0.333 0.241
016 0.333 0.228
017 0.250 0.250
018 0.333 0.247
019 0.333 0.342
020 0.333 0.208
021 0.250 0.278
022 0.333 0.482
023 0.250 0.263
024 0.000 0.020
025 0.333 0.241
026 0.250 0.140
027 0.333 0.230
028 0.000 0.028
029 0.000 0.033
030 0.250 0.146
031 0.000 0.022
032 0.250 0.259
033 0.250 0.125
034 0.333 0.322
035 0.000 0.020
036 0.333 0.368
037 0.000 0.020
038 0.250 0.268
039 0.333 0.241
040 0.000 0.018
041 0.000 0.025
042 0.000 0.028
043 0.333 0.234
044 0.333 0.207
045 0.250 0.153
046 0.250 0.273
047 0.250 0.155
048 0.250 0.135
049 0.000 0.024
050 0.333 0.394
051 0.250 0.140
052 0.333 0.230
053 0.333 0.345
054 0.000 0.028
055 0.250 0.246
056 0.333 0.249
057 0.250 0.144
058 0.000 0.031
059 0.250 0.126
060 0.333 0.219
061 0.250 0.417
062 0.250 0.154
063 0.000 0.024
064 0.250 0.254
065 0.000 0.032
066 0.250 0.183
067 0.250 0.381
068 0.250 0.138
069 0.250 0.145
070 0.250 0.152
071 0.250 0.160
072 0.250 0.153
073 0.333 0.227
074 0.333 0.497
075 0.333 0.526
076 0.250 0.174
077 0.250 0.152
078 0.000 0.034
079 0.250 0.165
080 0.250 0.134
081 0.000 0.025
082 0.000 0.028
083 0.250 0.134
084 0.000 0.030
085 0.250 0.252
086 0.250 0.296
087 0.333 0.486
088 0.333 0.344
089 0.250 0.246
090 0.250 0.159
091 0.333 0.498
092 0.000 0.024
093 0.000 0.030
094 0.333 0.480
095 0.250 0.305
096 0.000 0.024
097 0.250 0.251
098 0.250 0.172
099 0.000 0.025
100 0.333 0.240
101 0.250 0.316
102 0.000 0.016
103 0.000 0.023
104 0.000 0.017
105 0.000 0.016
106 0.000 0.029
107 0.333 0.191
108 0.250 0.273
109 0.250 0.250
110 0.333 0.235
111 0.000 0.023
112 0.250 0.248
113 0.250 0.255
114 0.000 0.030
115 0.250 0.245
116 0.250 0.277
117 0.333 0.228
118 0.333 0.248
119 0.250 0.130
120 0.250 0.300
121 0.250 0.264
122 0.333 0.235
123 0.250 0.127
124 0.333 0.225
125 0.250 0.254
126 0.333 0.238
127 0.250 0.284
128 0.250 0.134
129 0.250 0.250
130 0.250 0.153
131 0.333 0.228
132 0.000 0.018
133 0.250 0.169
134 0.333 0.244
135 0.000 0.025
136 0.250 0.155
137 0.250 0.264
138 0.250 0.168
139 0.000 0.025
140 0.000 0.017
141 0.333 0.225
142 0.250 0.165
143 0.333 0.231
144 0.333 0.209
145 0.250 0.300
146 0.250 0.288
147 0.000 0.025
148 0.250 0.270
149 0.250 0.257
150 0.000 0.018
151 0.000 0.025
152 0.250 0.277
153 0.333 0.225
154 0.250 0.133
155 0.250 0.277
156 0.000 0.028
157 0.333 0.223
158 0.250 0.254
159 0.000 0.033
160 0.250 0.130
161 0.000 0.028
162 0.250 0.168
163 0.000 0.021
164 0.333 0.219
165 0.000 0.024
166 0.333 0.217
167 0.250 0.171
168 0.250 0.277
169 0.250 0.288
170 0.333 0.218
171 0.000 0.019
172 0.333 0.219
173 0.250 0.260
174 0.000 0.014
175 0.250 0.409
176 0.000 0.032
177 0.333 0.255
178 0.250 0.134
179 0.333 0.400
180 0.333 0.227
181 0.000 0.020
182 0.250 0.135
183 0.250 0.118
184 0.000 0.026
185 0.000 0.037
186 0.000 0.019
187 0.333 0.468
188 0.250 0.266
189 0.250 0.273
190 0.000 0.024
191 0.000 0.022
192 0.000 0.016
193 0.250 0.160
194 0.250 0.148
195 0.250 0.133
196 0.333 0.201
197 0.250 0.117
198 0.333 0.228
199 0.000 0.011
200 0.333 0.231
201 0.250 0.125
202 0.333 0.351
203 0.000 0.023
204 0.333 0.395
205 0.333 0.229
206 0.000 0.029
207 0.000 0.022
208 0.250 0.281
209 0.000 0.028
210 0.250 0.159
211 0.333 0.211
212 0.333 0.511
213 0.000 0.026
214 0.250 0.250
215 0.250 0.173
216 0.333 0.250
217 0.333 0.232
218 0.333 0.324
219 0.250 0.236
220 0.250 0.272
221 0.250 0.265
222 0.333 0.200
223 0.333 0.205
224 0.000 0.032
225 0.250 0.156
226 0.000 0.023
227 0.250 0.256
228 0.000 0.022
229 0.250 0.266
230 0.250 0.425
231 0.333 0.349
232 0.000 0.025
233 0.000 0.025
234 0.333 0.204
235 0.333 0.235
236 0.000 0.024
237 0.250 0.165
238 0.250 0.315
239 0.250 0.255
240 0.250 0.135
241 0.333 0.503
242 0.000 0.033
243 0.000 0.013
244 0.000 0.026
245 0.250 0.180
246 0.333 0.232
247 0.250 0.249
248 0.333 0.214
249 0.250 0.131
250 0.000 0.028
251 0.250 0.155
252 0.000 0.035
253 0.250 0.135
254 0.000 0.023
255 0.000 0.034
256 0.250 0.274
257 0.250 0.153
258 0.333 0.230
259 0.333 0.217
260 0.000 0.019
261 0.250 0.167
262 0.000 0.020
263 0.250 0.260
264 0.333 0.330
265 0.250 0.267
266 0.333 0.217
267 0.250 0.167
268 0.250 0.163
269 0.333 0.242
270 0.333 0.235
271 0.250 0.133
272 0.250 0.175
273 0.333 0.226
274 0.250 0.286
275 0.250 0.170
276 0.333 0.215
277 0.250 0.288
278 0.333 0.217
279 0.250 0.276
280 0.333 0.340
281 0.000 0.030
282 0.333 0.335
283 0.000 0.021
284 0.250 0.145
285 0.333 0.238
286 0.250 0.158
287 0.250 0.245
288 0.250 0.150
289 0.333 0.231
290 0.000 0.022
291 0.250 0.253
292 0.000 0.038
293 0.250 0.155
294 0.333 0.321
295 0.333 0.232
296 0.250 0.167
297 0.333 0.194
298 0.250 0.253
299 0.333 0.204
300 0.333 0.207
301 0.333 0.235
302 0.333 0.234
303 0.000 0.023
304 0.250 0.155
305 0.000 0.020
306 0.000 0.025
307 0.000 0.030
308 0.000 0.015
309 0.333 0.245
310 0.250 0.132
311 0.250 0.245
312 0.250 0.127
313 0.000 0.021
314 0.250 0.124
315 0.250 0.271
316 0.333 0.351
317 0.250 0.132
318 0.250 0.257
319 0.000 0.025
320 0.333 0.243
321 0.000 0.032
322 0.250 0.416
323 0.000 0.024
324 0.000 0.023
325 0.333 0.215
326 0.250 0.284
327 0.250 0.269
328 0.000 0.025
329 0.333 0.247
330 0.333 0.218
331 0.250 0.255
332 0.333 0.499
333 0.000 0.015
334 0.250 0.267
335 0.250 0.277
336 0.333 0.234
337 0.250 0.264
338 0.250 0.138
339 0.250 0.245
340 0.250 0.261
341 0.000 0.021
342 0.000 0.022
343 0.333 0.239
344 0.250 0.162
345 0.333 0.373
346 0.000 0.024
347 0.000 0.018
348 0.250 0.111
349 0.250 0.316
350 0.333 0.380
351 0.000 0.024
352 0.000 0.020
353 0.250 0.151
354 0.250 0.259
355 0.250 0.382
356 0.250 0.250
357 0.333 0.369
358 0.333 0.473
359 0.333 0.215
360 0.000 0.030
361 0.250 0.272
362 0.250 0.286
363 0.250 0.309
364 0.333 0.222
365 0.250 0.140
366 0.250 0.112
367 0.250 0.254
368 0.250 0.382
369 0.000 0.014
370 0.000 0.031
371 0.333 0.239
372 0.250 0.152
373 0.250 0.253
374 0.333 0.223
375 0.333 0.387
376 0.000 0.026
377 0.250 0.143
378 0.333 0.216
379 0.333 0.475
380 0.250 0.142
381 0.250 0.129
382 0.333 0.228
383 0.250 0.325
384 0.333 0.223
385 0.333 0.374
386 0.250 0.170
387 0.333 0.251
388 0.250 0.166
389 0.000 0.026
390 0.250 0.176
391 0.333 0.386
392 0.250 0.272
393 0.000 0.020
394 0.250 0.266
395 0.333 0.224
396 0.250 0.153
397 0.250 0.167
398 0.250 0.159
399 0.250 0.149
400 0.250 0.153 
# Plots

cond_variance_dat = cond_variance_dat %>%
  mutate(
    Dropped = if_else(var_unionmembership == 0, "Dropped", "Included")
  )

ggplot(cond_variance_dat, aes(x = var_unionmembership, y = var_Dresid)) +
  geom_point() +
  xlab("Var(Union Membership)") +
  ylab("Var(D_resid)") +
  theme_economist_white(gray_bg = TRUE) + 
  facet_wrap(~Dropped, scales = "free")

Difference in Difference Estimator

The fixed effects strategy requires panel data, that is, repeated observations on the same individual (or firms, or whatever the unit of observation might be). Often, however, the regressor of interest varies only at a more aggregate or group level … The source of OVB [omitted variable bias in such cases] must be unobserved variables at the [aggregate level]. This leads to the difference-in-difference strategy… DD is a version of fixed effects estimation using aggregate data. (Angrist and Pischke 2009:227-8)

Figure 1: Difference in Difference Setup

Figure 1: Difference in Difference Setup

Estimand

The quantity of interest is the average treatment effect in the treated group (ATT) \[\begin{equation} \mathbb{E}[Y_i(1) - Y_i(0) | Z = 1, \text{Time = After}] \end{equation}\]

Assumption

Parallel Trends is the identification assumption necessary to estimate this quantity. Formally: \[\begin{equation} \mathbb{E}[Y_i(0) | Z = 1, \text{Time = After}] - \mathbb{E}[Y_i(0) | Z = 1, \text{Time = Before}] \\\ = \mathbb{E}[Y_i(0) | Z = 0, \text{Time = After}] - \mathbb{E}[Y_i(0) | Z = 0, \text{Time = Before}] \end{equation}\] If we re-arrange the terms, another way of saying this is \[\begin{equation} \mathbb{E}[Y_i(0) | Z = 1, \text{Time = After}] - \mathbb{E}[Y_i(0) | Z = 0, \text{Time = After}] \\\ = \mathbb{E}[Y_i(0) | Z = 1, \text{Time = Before}] - \mathbb{E}[Y_i(0) | Z = 0, \text{Time = Before}] \\\ = k \end{equation}\]

In terms of Figure 1, this assumption says that the difference in average untreated potential outcomes (E[\(Y_0\)]) of the treatment and control groups is the same “before” and “after” (i.e. \(k\)).

Note that this assumption is not empirically verifiable. Two groups that are trending pre-intervention, may or may not satisfy this criterion. It is highly likely that if their outcomes trend, the assumption might hold. However, the absence of parallel trends in the pre-intervention period is not evidence against this assumption.

Estimation and Visualization

I will motivate this section with the Card and Kreuger (1994) study that looks at the impact of minimum wages on youth employment. In this study, there are two groups — New Jersey (\(Z=1\)) and Pennsylvania (\(Z=0\)) — and two time periods — \(T_0\) when both states had similar minimum wage laws, and \(T_1\) when New Jersey raised the minimum wage. We can estimate difference-in-differences using an ordinary least squares regression of the type:

\[\begin{equation} \text{Employment} = \beta_0 + \beta_1 (I_{\text{New Jersey}}) + \beta_2 (I_{\text{After}}) + \beta_3 (I_{\text{New Jersey}} \times I_{\text{After}}) + \varepsilon \end{equation}\]

Here is the data from the replication files. I used tidyverse to “tidy” the data.

card = read_dta("card_krueger.dta")

# Select the relevant columns for the analysis

card = card %>% select(
  emptot, emptot2, nj, pa,
  bk, kfc, roys, wendys
) %>%
  mutate(
    ID = row_number(),
    State = if_else(nj == 1, "New Jersey", "PA")
  ) %>%
  rename(
    Time1 = emptot,
    Time2 = emptot2
  )

# Tidying the data

card = card %>% 
  gather(key = "Time", value = "Outcome", -c(ID,State, nj,pa,bk,kfc,roys,wendys))

card = card %>%
  mutate(Time = recode(Time, `Time1` = 0, `Time2` = 1))

card %>% arrange(ID) %>% kable(format = "html") %>% 
  kable_styling(bootstrap_options = "striped", 
                                     full_width = FALSE, 
                                     fixed_thead = TRUE) %>% 
  scroll_box(width = "750px", height = "250px")
nj pa bk kfc roys wendys ID State Time Outcome
0 1 1 0 0 0 1 PA 0 40.50
0 1 1 0 0 0 1 PA 1 24.00
0 1 0 1 0 0 2 PA 0 13.75
0 1 0 1 0 0 2 PA 1 11.50
0 1 0 1 0 0 3 PA 0 8.50
0 1 0 1 0 0 3 PA 1 10.50
0 1 0 0 0 1 4 PA 0 34.00
0 1 0 0 0 1 4 PA 1 20.00
0 1 0 0 0 1 5 PA 0 24.00
0 1 0 0 0 1 5 PA 1 35.50
0 1 0 0 0 1 6 PA 0 20.50
0 1 0 0 0 1 6 PA 1 NA
0 1 1 0 0 0 7 PA 0 70.50
0 1 1 0 0 0 7 PA 1 29.00
0 1 1 0 0 0 8 PA 0 23.50
0 1 1 0 0 0 8 PA 1 36.50
0 1 0 1 0 0 9 PA 0 11.00
0 1 0 1 0 0 9 PA 1 11.00
0 1 0 1 0 0 10 PA 0 9.00
0 1 0 1 0 0 10 PA 1 8.50
0 1 0 0 1 0 11 PA 0 15.50
0 1 0 0 1 0 11 PA 1 17.50
0 1 1 0 0 0 12 PA 0 58.00
0 1 1 0 0 0 12 PA 1 29.00
0 1 1 0 0 0 13 PA 0 26.50
0 1 1 0 0 0 13 PA 1 30.50
0 1 1 0 0 0 14 PA 0 28.50
0 1 1 0 0 0 14 PA 1 26.00
0 1 1 0 0 0 15 PA 0 13.50
0 1 1 0 0 0 15 PA 1 18.00
0 1 0 1 0 0 16 PA 0 16.75
0 1 0 1 0 0 16 PA 1 20.00
0 1 0 1 0 0 17 PA 0 9.50
0 1 0 1 0 0 17 PA 1 13.50
0 1 0 0 1 0 18 PA 0 15.00
0 1 0 0 1 0 18 PA 1 19.00
0 1 0 0 1 0 19 PA 0 29.50
0 1 0 0 1 0 19 PA 1 15.50
0 1 0 0 1 0 20 PA 0 18.50
0 1 0 0 1 0 20 PA 1 14.50
0 1 0 0 1 0 21 PA 0 20.25
0 1 0 0 1 0 21 PA 1 14.00
0 1 0 0 1 0 22 PA 0 36.00
0 1 0 0 1 0 22 PA 1 19.00
0 1 0 0 1 0 23 PA 0 21.00
0 1 0 0 1 0 23 PA 1 21.00
0 1 0 0 1 0 24 PA 0 20.50
0 1 0 0 1 0 24 PA 1 18.00
0 1 0 0 0 1 25 PA 0 19.00
0 1 0 0 0 1 25 PA 1 20.50
0 1 0 0 1 0 26 PA 0 14.00
0 1 0 0 1 0 26 PA 1 13.50
0 1 0 0 1 0 27 PA 0 19.00
0 1 0 0 1 0 27 PA 1 17.50
0 1 1 0 0 0 28 PA 0 32.50
0 1 1 0 0 0 28 PA 1 25.50
0 1 1 0 0 0 29 PA 0 19.00
0 1 1 0 0 0 29 PA 1 15.50
0 1 1 0 0 0 30 PA 0 27.00
0 1 1 0 0 0 30 PA 1 25.50
0 1 1 0 0 0 31 PA 0 19.50
0 1 1 0 0 0 31 PA 1 27.00
0 1 1 0 0 0 32 PA 0 24.00
0 1 1 0 0 0 32 PA 1 16.00
0 1 1 0 0 0 33 PA 0 18.00
0 1 1 0 0 0 33 PA 1 19.00
0 1 1 0 0 0 34 PA 0 NA
0 1 1 0 0 0 34 PA 1 24.50
0 1 1 0 0 0 35 PA 0 19.00
0 1 1 0 0 0 35 PA 1 25.00
0 1 0 1 0 0 36 PA 0 14.00
0 1 0 1 0 0 36 PA 1 17.00
0 1 0 1 0 0 37 PA 0 10.50
0 1 0 1 0 0 37 PA 1 16.50
0 1 0 0 0 1 38 PA 0 38.00
0 1 0 0 0 1 38 PA 1 20.00
0 1 0 0 0 1 39 PA 0 23.50
0 1 0 0 0 1 39 PA 1 18.00
0 1 0 0 0 1 40 PA 0 10.50
0 1 0 0 0 1 40 PA 1 12.00
0 1 0 0 0 1 41 PA 0 25.00
0 1 0 0 0 1 41 PA 1 25.00
0 1 0 1 0 0 42 PA 0 11.00
0 1 0 1 0 0 42 PA 1 11.00
0 1 1 0 0 0 43 PA 0 36.50
0 1 1 0 0 0 43 PA 1 27.00
0 1 1 0 0 0 44 PA 0 52.50
0 1 1 0 0 0 44 PA 1 34.00
0 1 1 0 0 0 45 PA 0 29.00
0 1 1 0 0 0 45 PA 1 25.00
0 1 1 0 0 0 46 PA 0 45.00
0 1 1 0 0 0 46 PA 1 37.50
0 1 1 0 0 0 47 PA 0 22.75
0 1 1 0 0 0 47 PA 1 21.00
0 1 0 1 0 0 48 PA 0 7.50
0 1 0 1 0 0 48 PA 1 9.50
0 1 0 0 0 1 49 PA 0 15.50
0 1 0 0 0 1 49 PA 1 18.00
0 1 0 0 0 1 50 PA 0 38.00
0 1 0 0 0 1 50 PA 1 18.00
0 1 0 0 0 1 51 PA 0 17.50
0 1 0 0 0 1 51 PA 1 19.00
0 1 0 0 0 1 52 PA 0 25.00
0 1 0 0 0 1 52 PA 1 31.50
0 1 1 0 0 0 53 PA 0 NA
0 1 1 0 0 0 53 PA 1 23.00
0 1 1 0 0 0 54 PA 0 21.00
0 1 1 0 0 0 54 PA 1 24.50
0 1 1 0 0 0 55 PA 0 15.50
0 1 1 0 0 0 55 PA 1 27.50
0 1 1 0 0 0 56 PA 0 15.50
0 1 1 0 0 0 56 PA 1 34.50
0 1 0 1 0 0 57 PA 0 8.50
0 1 0 1 0 0 57 PA 1 14.00
0 1 0 0 1 0 58 PA 0 18.00
0 1 0 0 1 0 58 PA 1 15.00
0 1 0 0 0 1 59 PA 0 20.75
0 1 0 0 0 1 59 PA 1 23.00
0 1 1 0 0 0 60 PA 0 39.00
0 1 1 0 0 0 60 PA 1 26.50
0 1 1 0 0 0 61 PA 0 41.50
0 1 1 0 0 0 61 PA 1 23.50
0 1 1 0 0 0 62 PA 0 22.50
0 1 1 0 0 0 62 PA 1 35.50
0 1 1 0 0 0 63 PA 0 32.00
0 1 1 0 0 0 63 PA 1 21.00
0 1 1 0 0 0 64 PA 0 24.00
0 1 1 0 0 0 64 PA 1 23.00
0 1 1 0 0 0 65 PA 0 26.00
0 1 1 0 0 0 65 PA 1 20.00
0 1 1 0 0 0 66 PA 0 48.50
0 1 1 0 0 0 66 PA 1 27.00
0 1 0 1 0 0 67 PA 0 8.50
0 1 0 1 0 0 67 PA 1 13.00
0 1 0 0 1 0 68 PA 0 18.50
0 1 0 0 1 0 68 PA 1 0.00
0 1 0 0 1 0 69 PA 0 13.00
0 1 0 0 1 0 69 PA 1 13.50
0 1 0 0 0 1 70 PA 0 22.50
0 1 0 0 0 1 70 PA 1 NA
0 1 0 0 0 1 71 PA 0 28.00
0 1 0 0 0 1 71 PA 1 26.75
0 1 1 0 0 0 72 PA 0 24.50
0 1 1 0 0 0 72 PA 1 43.50
0 1 1 0 0 0 73 PA 0 18.50
0 1 1 0 0 0 73 PA 1 41.25
0 1 0 0 1 0 74 PA 0 23.50
0 1 0 0 1 0 74 PA 1 14.25
0 1 0 0 1 0 75 PA 0 15.00
0 1 0 0 1 0 75 PA 1 12.50
0 1 0 0 1 0 76 PA 0 18.00
0 1 0 0 1 0 76 PA 1 34.00
0 1 0 0 1 0 77 PA 0 20.25
0 1 0 0 1 0 77 PA 1 10.00
0 1 1 0 0 0 78 PA 0 15.50
0 1 1 0 0 0 78 PA 1 14.00
0 1 1 0 0 0 79 PA 0 21.00
0 1 1 0 0 0 79 PA 1 17.50
1 0 1 0 0 0 80 New Jersey 0 15.00
1 0 1 0 0 0 80 New Jersey 1 27.00
1 0 1 0 0 0 81 New Jersey 0 15.00
1 0 1 0 0 0 81 New Jersey 1 21.50
1 0 0 0 1 0 82 New Jersey 0 24.00
1 0 0 0 1 0 82 New Jersey 1 23.00
1 0 0 0 1 0 83 New Jersey 0 19.25
1 0 0 0 1 0 83 New Jersey 1 21.50
1 0 1 0 0 0 84 New Jersey 0 21.50
1 0 1 0 0 0 84 New Jersey 1 34.50
1 0 0 1 0 0 85 New Jersey 0 9.50
1 0 0 1 0 0 85 New Jersey 1 10.50
1 0 0 0 0 1 86 New Jersey 0 44.00
1 0 0 0 0 1 86 New Jersey 1 16.50
1 0 1 0 0 0 87 New Jersey 0 NA
1 0 1 0 0 0 87 New Jersey 1 23.00
1 0 1 0 0 0 88 New Jersey 0 21.00
1 0 1 0 0 0 88 New Jersey 1 38.00
1 0 0 0 0 1 89 New Jersey 0 28.00
1 0 0 0 0 1 89 New Jersey 1 36.00
1 0 0 0 0 1 90 New Jersey 0 10.00
1 0 0 0 0 1 90 New Jersey 1 16.00
1 0 1 0 0 0 91 New Jersey 0 20.50
1 0 1 0 0 0 91 New Jersey 1 20.50
1 0 0 0 1 0 92 New Jersey 0 25.50
1 0 0 0 1 0 92 New Jersey 1 19.00
1 0 0 0 0 1 93 New Jersey 0 19.00
1 0 0 0 0 1 93 New Jersey 1 53.00
1 0 1 0 0 0 94 New Jersey 0 16.50
1 0 1 0 0 0 94 New Jersey 1 16.50
1 0 1 0 0 0 95 New Jersey 0 17.00
1 0 1 0 0 0 95 New Jersey 1 16.50
1 0 1 0 0 0 96 New Jersey 0 27.50
1 0 1 0 0 0 96 New Jersey 1 45.00
1 0 0 0 1 0 97 New Jersey 0 5.00
1 0 0 0 1 0 97 New Jersey 1 8.00
1 0 0 1 0 0 98 New Jersey 0 16.00
1 0 0 1 0 0 98 New Jersey 1 15.50
1 0 1 0 0 0 99 New Jersey 0 41.50
1 0 1 0 0 0 99 New Jersey 1 24.00
1 0 0 1 0 0 100 New Jersey 0 13.00
1 0 0 1 0 0 100 New Jersey 1 7.50
1 0 0 1 0 0 101 New Jersey 0 10.00
1 0 0 1 0 0 101 New Jersey 1 7.50
1 0 0 1 0 0 102 New Jersey 0 15.00
1 0 0 1 0 0 102 New Jersey 1 18.50
1 0 0 0 1 0 103 New Jersey 0 26.00
1 0 0 0 1 0 103 New Jersey 1 16.50
1 0 0 0 0 1 104 New Jersey 0 18.00
1 0 0 0 0 1 104 New Jersey 1 33.50
1 0 0 0 1 0 105 New Jersey 0 35.00
1 0 0 0 1 0 105 New Jersey 1 37.00
1 0 0 0 1 0 106 New Jersey 0 21.00
1 0 0 0 1 0 106 New Jersey 1 18.00
1 0 0 0 0 1 107 New Jersey 0 34.50
1 0 0 0 0 1 107 New Jersey 1 26.50
1 0 0 0 0 1 108 New Jersey 0 18.25
1 0 0 0 0 1 108 New Jersey 1 33.00
1 0 0 0 0 1 109 New Jersey 0 24.50
1 0 0 0 0 1 109 New Jersey 1 NA
1 0 1 0 0 0 110 New Jersey 0 20.00
1 0 1 0 0 0 110 New Jersey 1 20.50
1 0 1 0 0 0 111 New Jersey 0 16.00
1 0 1 0 0 0 111 New Jersey 1 24.50
1 0 0 1 0 0 112 New Jersey 0 17.50
1 0 0 1 0 0 112 New Jersey 1 16.00
1 0 0 0 0 1 113 New Jersey 0 20.50
1 0 0 0 0 1 113 New Jersey 1 NA
1 0 0 0 0 1 114 New Jersey 0 20.00
1 0 0 0 0 1 114 New Jersey 1 23.50
1 0 0 1 0 0 115 New Jersey 0 12.25
1 0 0 1 0 0 115 New Jersey 1 13.00
1 0 0 0 1 0 116 New Jersey 0 33.00
1 0 0 0 1 0 116 New Jersey 1 20.00
1 0 0 0 1 0 117 New Jersey 0 31.50
1 0 0 0 1 0 117 New Jersey 1 22.00
1 0 0 0 0 1 118 New Jersey 0 NA
1 0 0 0 0 1 118 New Jersey 1 28.50
1 0 0 0 0 1 119 New Jersey 0 18.00
1 0 0 0 0 1 119 New Jersey 1 NA
1 0 0 0 0 1 120 New Jersey 0 28.00
1 0 0 0 0 1 120 New Jersey 1 25.50
1 0 1 0 0 0 121 New Jersey 0 16.50
1 0 1 0 0 0 121 New Jersey 1 18.00
1 0 1 0 0 0 122 New Jersey 0 14.75
1 0 1 0 0 0 122 New Jersey 1 9.00
1 0 1 0 0 0 123 New Jersey 0 23.50
1 0 1 0 0 0 123 New Jersey 1 26.50
1 0 1 0 0 0 124 New Jersey 0 10.00
1 0 1 0 0 0 124 New Jersey 1 0.00
1 0 1 0 0 0 125 New Jersey 0 46.50
1 0 1 0 0 0 125 New Jersey 1 23.75
1 0 0 1 0 0 126 New Jersey 0 15.50
1 0 0 1 0 0 126 New Jersey 1 18.00
1 0 0 0 1 0 127 New Jersey 0 24.50
1 0 0 0 1 0 127 New Jersey 1 36.00
1 0 0 0 1 0 128 New Jersey 0 24.00
1 0 0 0 1 0 128 New Jersey 1 21.00
1 0 0 0 1 0 129 New Jersey 0 23.00
1 0 0 0 1 0 129 New Jersey 1 15.50
1 0 0 0 0 1 130 New Jersey 0 28.00
1 0 0 0 0 1 130 New Jersey 1 22.50
1 0 1 0 0 0 131 New Jersey 0 32.50
1 0 1 0 0 0 131 New Jersey 1 27.00
1 0 1 0 0 0 132 New Jersey 0 23.00
1 0 1 0 0 0 132 New Jersey 1 34.00
1 0 0 0 1 0 133 New Jersey 0 17.00
1 0 0 0 1 0 133 New Jersey 1 16.75
1 0 0 0 1 0 134 New Jersey 0 30.00
1 0 0 0 1 0 134 New Jersey 1 26.00
1 0 0 0 0 1 135 New Jersey 0 21.50
1 0 0 0 0 1 135 New Jersey 1 18.00
1 0 1 0 0 0 136 New Jersey 0 32.00
1 0 1 0 0 0 136 New Jersey 1 20.50
1 0 1 0 0 0 137 New Jersey 0 8.00
1 0 1 0 0 0 137 New Jersey 1 15.00
1 0 0 0 1 0 138 New Jersey 0 35.50
1 0 0 0 1 0 138 New Jersey 1 25.50
1 0 0 0 1 0 139 New Jersey 0 31.50
1 0 0 0 1 0 139 New Jersey 1 NA
1 0 0 0 1 0 140 New Jersey 0 33.00
1 0 0 0 1 0 140 New Jersey 1 20.00
1 0 0 0 1 0 141 New Jersey 0 22.00
1 0 0 0 1 0 141 New Jersey 1 18.00
1 0 1 0 0 0 142 New Jersey 0 23.00
1 0 1 0 0 0 142 New Jersey 1 26.50
1 0 1 0 0 0 143 New Jersey 0 23.00
1 0 1 0 0 0 143 New Jersey 1 23.25
1 0 0 1 0 0 144 New Jersey 0 8.50
1 0 0 1 0 0 144 New Jersey 1 10.50
1 0 0 1 0 0 145 New Jersey 0 12.50
1 0 0 1 0 0 145 New Jersey 1 12.00
1 0 0 1 0 0 146 New Jersey 0 9.50
1 0 0 1 0 0 146 New Jersey 1 9.50
1 0 0 1 0 0 147 New Jersey 0 13.00
1 0 0 1 0 0 147 New Jersey 1 15.50
1 0 1 0 0 0 148 New Jersey 0 17.00
1 0 1 0 0 0 148 New Jersey 1 14.50
1 0 0 1 0 0 149 New Jersey 0 9.00
1 0 0 1 0 0 149 New Jersey 1 14.00
1 0 0 1 0 0 150 New Jersey 0 19.00
1 0 0 1 0 0 150 New Jersey 1 6.50
1 0 0 1 0 0 151 New Jersey 0 11.50
1 0 0 1 0 0 151 New Jersey 1 10.00
1 0 0 1 0 0 152 New Jersey 0 13.00
1 0 0 1 0 0 152 New Jersey 1 11.50
1 0 0 0 1 0 153 New Jersey 0 48.00
1 0 0 0 1 0 153 New Jersey 1 46.50
1 0 0 0 1 0 154 New Jersey 0 45.50
1 0 0 0 1 0 154 New Jersey 1 26.50
1 0 0 0 0 1 155 New Jersey 0 15.00
1 0 0 0 0 1 155 New Jersey 1 16.00
1 0 1 0 0 0 156 New Jersey 0 18.00
1 0 1 0 0 0 156 New Jersey 1 13.00
1 0 0 0 1 0 157 New Jersey 0 21.00
1 0 0 0 1 0 157 New Jersey 1 14.00
1 0 0 0 0 1 158 New Jersey 0 20.50
1 0 0 0 0 1 158 New Jersey 1 15.50
1 0 0 0 0 1 159 New Jersey 0 20.50
1 0 0 0 0 1 159 New Jersey 1 18.00
1 0 0 0 0 1 160 New Jersey 0 17.50
1 0 0 0 0 1 160 New Jersey 1 27.00
1 0 1 0 0 0 161 New Jersey 0 27.50
1 0 1 0 0 0 161 New Jersey 1 27.00
1 0 1 0 0 0 162 New Jersey 0 24.00
1 0 1 0 0 0 162 New Jersey 1 29.50
1 0 1 0 0 0 163 New Jersey 0 34.00
1 0 1 0 0 0 163 New Jersey 1 16.50
1 0 1 0 0 0 164 New Jersey 0 18.00
1 0 1 0 0 0 164 New Jersey 1 31.50
1 0 1 0 0 0 165 New Jersey 0 24.00
1 0 1 0 0 0 165 New Jersey 1 27.50
1 0 0 0 1 0 166 New Jersey 0 NA
1 0 0 0 1 0 166 New Jersey 1 25.50
1 0 0 0 0 1 167 New Jersey 0 33.50
1 0 0 0 0 1 167 New Jersey 1 32.00
1 0 0 0 0 1 168 New Jersey 0 12.00
1 0 0 0 0 1 168 New Jersey 1 14.50
1 0 1 0 0 0 169 New Jersey 0 35.00
1 0 1 0 0 0 169 New Jersey 1 10.50
1 0 1 0 0 0 170 New Jersey 0 35.00
1 0 1 0 0 0 170 New Jersey 1 44.00
1 0 1 0 0 0 171 New Jersey 0 16.00
1 0 1 0 0 0 171 New Jersey 1 15.50
1 0 0 1 0 0 172 New Jersey 0 15.50
1 0 0 1 0 0 172 New Jersey 1 10.50
1 0 0 0 1 0 173 New Jersey 0 19.00
1 0 0 0 1 0 173 New Jersey 1 24.00
1 0 0 0 1 0 174 New Jersey 0 24.00
1 0 0 0 1 0 174 New Jersey 1 29.00
1 0 0 0 0 1 175 New Jersey 0 NA
1 0 0 0 0 1 175 New Jersey 1 30.50
1 0 1 0 0 0 176 New Jersey 0 36.00
1 0 1 0 0 0 176 New Jersey 1 20.00
1 0 1 0 0 0 177 New Jersey 0 29.00
1 0 1 0 0 0 177 New Jersey 1 26.50
1 0 1 0 0 0 178 New Jersey 0 29.00
1 0 1 0 0 0 178 New Jersey 1 27.00
1 0 1 0 0 0 179 New Jersey 0 21.25
1 0 1 0 0 0 179 New Jersey 1 26.50
1 0 0 1 0 0 180 New Jersey 0 15.50
1 0 0 1 0 0 180 New Jersey 1 10.50
1 0 0 0 1 0 181 New Jersey 0 19.50
1 0 0 0 1 0 181 New Jersey 1 18.00
1 0 0 0 1 0 182 New Jersey 0 21.00
1 0 0 0 1 0 182 New Jersey 1 18.50
1 0 0 0 1 0 183 New Jersey 0 14.00
1 0 0 0 1 0 183 New Jersey 1 25.50
1 0 0 0 1 0 184 New Jersey 0 30.50
1 0 0 0 1 0 184 New Jersey 1 32.00
1 0 1 0 0 0 185 New Jersey 0 23.50
1 0 1 0 0 0 185 New Jersey 1 33.00
1 0 0 1 0 0 186 New Jersey 0 14.00
1 0 0 1 0 0 186 New Jersey 1 7.50
1 0 0 0 1 0 187 New Jersey 0 14.50
1 0 0 0 1 0 187 New Jersey 1 12.50
1 0 1 0 0 0 188 New Jersey 0 15.50
1 0 1 0 0 0 188 New Jersey 1 24.00
1 0 1 0 0 0 189 New Jersey 0 26.50
1 0 1 0 0 0 189 New Jersey 1 29.00
1 0 0 1 0 0 190 New Jersey 0 8.00
1 0 0 1 0 0 190 New Jersey 1 10.00
1 0 0 1 0 0 191 New Jersey 0 5.50
1 0 0 1 0 0 191 New Jersey 1 9.50
1 0 0 0 1 0 192 New Jersey 0 23.25
1 0 0 0 1 0 192 New Jersey 1 17.50
1 0 0 0 1 0 193 New Jersey 0 15.50
1 0 0 0 1 0 193 New Jersey 1 17.50
1 0 0 0 0 1 194 New Jersey 0 32.25
1 0 0 0 0 1 194 New Jersey 1 25.00
1 0 1 0 0 0 195 New Jersey 0 30.00
1 0 1 0 0 0 195 New Jersey 1 26.00
1 0 1 0 0 0 196 New Jersey 0 NA
1 0 1 0 0 0 196 New Jersey 1 24.00
1 0 1 0 0 0 197 New Jersey 0 30.50
1 0 1 0 0 0 197 New Jersey 1 22.50
1 0 1 0 0 0 198 New Jersey 0 14.00
1 0 1 0 0 0 198 New Jersey 1 NA
1 0 1 0 0 0 199 New Jersey 0 27.00
1 0 1 0 0 0 199 New Jersey 1 35.00
1 0 1 0 0 0 200 New Jersey 0 18.50
1 0 1 0 0 0 200 New Jersey 1 17.75
1 0 1 0 0 0 201 New Jersey 0 38.00
1 0 1 0 0 0 201 New Jersey 1 24.50
1 0 1 0 0 0 202 New Jersey 0 16.50
1 0 1 0 0 0 202 New Jersey 1 16.50
1 0 1 0 0 0 203 New Jersey 0 50.00
1 0 1 0 0 0 203 New Jersey 1 30.00
1 0 1 0 0 0 204 New Jersey 0 20.50
1 0 1 0 0 0 204 New Jersey 1 39.00
1 0 0 1 0 0 205 New Jersey 0 6.50
1 0 0 1 0 0 205 New Jersey 1 17.00
1 0 0 1 0 0 206 New Jersey 0 13.50
1 0 0 1 0 0 206 New Jersey 1 12.50
1 0 0 0 1 0 207 New Jersey 0 28.00
1 0 0 0 1 0 207 New Jersey 1 38.50
1 0 0 0 1 0 208 New Jersey 0 21.50
1 0 0 0 1 0 208 New Jersey 1 20.00
1 0 0 0 0 1 209 New Jersey 0 NA
1 0 0 0 0 1 209 New Jersey 1 21.00
1 0 1 0 0 0 210 New Jersey 0 24.00
1 0 1 0 0 0 210 New Jersey 1 32.00
1 0 1 0 0 0 211 New Jersey 0 20.50
1 0 1 0 0 0 211 New Jersey 1 19.00
1 0 1 0 0 0 212 New Jersey 0 NA
1 0 1 0 0 0 212 New Jersey 1 20.00
1 0 1 0 0 0 213 New Jersey 0 23.50
1 0 1 0 0 0 213 New Jersey 1 26.50
1 0 1 0 0 0 214 New Jersey 0 23.00
1 0 1 0 0 0 214 New Jersey 1 19.00
1 0 1 0 0 0 215 New Jersey 0 17.00
1 0 1 0 0 0 215 New Jersey 1 20.00
1 0 1 0 0 0 216 New Jersey 0 20.50
1 0 1 0 0 0 216 New Jersey 1 22.50
1 0 0 1 0 0 217 New Jersey 0 23.50
1 0 0 1 0 0 217 New Jersey 1 10.00
1 0 0 1 0 0 218 New Jersey 0 9.50
1 0 0 1 0 0 218 New Jersey 1 12.50
1 0 0 1 0 0 219 New Jersey 0 10.00
1 0 0 1 0 0 219 New Jersey 1 11.00
1 0 0 1 0 0 220 New Jersey 0 14.00
1 0 0 1 0 0 220 New Jersey 1 12.50
1 0 0 0 1 0 221 New Jersey 0 85.00
1 0 0 0 1 0 221 New Jersey 1 59.00
1 0 0 0 1 0 222 New Jersey 0 15.00
1 0 0 0 1 0 222 New Jersey 1 19.00
1 0 0 0 1 0 223 New Jersey 0 21.50
1 0 0 0 1 0 223 New Jersey 1 15.50
1 0 0 0 0 1 224 New Jersey 0 53.00
1 0 0 0 0 1 224 New Jersey 1 19.00
1 0 1 0 0 0 225 New Jersey 0 24.00
1 0 1 0 0 0 225 New Jersey 1 26.50
1 0 1 0 0 0 226 New Jersey 0 20.50
1 0 1 0 0 0 226 New Jersey 1 17.50
1 0 1 0 0 0 227 New Jersey 0 14.50
1 0 1 0 0 0 227 New Jersey 1 20.50
1 0 1 0 0 0 228 New Jersey 0 25.00
1 0 1 0 0 0 228 New Jersey 1 28.00
1 0 0 0 1 0 229 New Jersey 0 27.00
1 0 0 0 1 0 229 New Jersey 1 30.25
1 0 1 0 0 0 230 New Jersey 0 22.50
1 0 1 0 0 0 230 New Jersey 1 44.00
1 0 1 0 0 0 231 New Jersey 0 36.50
1 0 1 0 0 0 231 New Jersey 1 17.50
1 0 1 0 0 0 232 New Jersey 0 17.25
1 0 1 0 0 0 232 New Jersey 1 31.00
1 0 1 0 0 0 233 New Jersey 0 21.50
1 0 1 0 0 0 233 New Jersey 1 31.00
1 0 1 0 0 0 234 New Jersey 0 24.50
1 0 1 0 0 0 234 New Jersey 1 28.50
1 0 1 0 0 0 235 New Jersey 0 19.00
1 0 1 0 0 0 235 New Jersey 1 19.00
1 0 1 0 0 0 236 New Jersey 0 25.00
1 0 1 0 0 0 236 New Jersey 1 41.00
1 0 1 0 0 0 237 New Jersey 0 16.00
1 0 1 0 0 0 237 New Jersey 1 28.00
1 0 0 1 0 0 238 New Jersey 0 19.00
1 0 0 1 0 0 238 New Jersey 1 9.50
1 0 0 0 1 0 239 New Jersey 0 22.75
1 0 0 0 1 0 239 New Jersey 1 25.00
1 0 0 0 1 0 240 New Jersey 0 35.00
1 0 0 0 1 0 240 New Jersey 1 28.00
1 0 0 0 1 0 241 New Jersey 0 16.00
1 0 0 0 1 0 241 New Jersey 1 16.00
1 0 0 0 1 0 242 New Jersey 0 21.00
1 0 0 0 1 0 242 New Jersey 1 22.75
1 0 0 0 1 0 243 New Jersey 0 26.75
1 0 0 0 1 0 243 New Jersey 1 NA
1 0 0 0 1 0 244 New Jersey 0 30.50
1 0 0 0 1 0 244 New Jersey 1 32.00
1 0 0 0 0 1 245 New Jersey 0 23.00
1 0 0 0 0 1 245 New Jersey 1 27.50
1 0 0 0 0 1 246 New Jersey 0 21.50
1 0 0 0 0 1 246 New Jersey 1 25.50
1 0 0 0 0 1 247 New Jersey 0 36.50
1 0 0 0 0 1 247 New Jersey 1 60.50
1 0 1 0 0 0 248 New Jersey 0 23.00
1 0 1 0 0 0 248 New Jersey 1 20.50
1 0 1 0 0 0 249 New Jersey 0 14.00
1 0 1 0 0 0 249 New Jersey 1 16.00
1 0 1 0 0 0 250 New Jersey 0 17.25
1 0 1 0 0 0 250 New Jersey 1 25.00
1 0 1 0 0 0 251 New Jersey 0 18.50
1 0 1 0 0 0 251 New Jersey 1 13.00
1 0 0 1 0 0 252 New Jersey 0 8.50
1 0 0 1 0 0 252 New Jersey 1 9.00
1 0 0 1 0 0 253 New Jersey 0 14.00
1 0 0 1 0 0 253 New Jersey 1 16.50
1 0 0 0 1 0 254 New Jersey 0 23.00
1 0 0 0 1 0 254 New Jersey 1 21.00
1 0 0 0 0 1 255 New Jersey 0 29.00
1 0 0 0 0 1 255 New Jersey 1 21.50
1 0 1 0 0 0 256 New Jersey 0 19.00
1 0 1 0 0 0 256 New Jersey 1 20.00
1 0 1 0 0 0 257 New Jersey 0 21.00
1 0 1 0 0 0 257 New Jersey 1 21.25
1 0 1 0 0 0 258 New Jersey 0 17.00
1 0 1 0 0 0 258 New Jersey 1 21.50
1 0 1 0 0 0 259 New Jersey 0 36.00
1 0 1 0 0 0 259 New Jersey 1 31.00
1 0 1 0 0 0 260 New Jersey 0 25.00
1 0 1 0 0 0 260 New Jersey 1 33.00
1 0 1 0 0 0 261 New Jersey 0 29.50
1 0 1 0 0 0 261 New Jersey 1 37.75
1 0 0 1 0 0 262 New Jersey 0 22.00
1 0 0 1 0 0 262 New Jersey 1 9.00
1 0 0 1 0 0 263 New Jersey 0 11.00
1 0 0 1 0 0 263 New Jersey 1 11.00
1 0 0 1 0 0 264 New Jersey 0 13.50
1 0 0 1 0 0 264 New Jersey 1 12.75
1 0 0 1 0 0 265 New Jersey 0 13.50
1 0 0 1 0 0 265 New Jersey 1 10.50
1 0 0 1 0 0 266 New Jersey 0 18.00
1 0 0 1 0 0 266 New Jersey 1 22.00
1 0 0 1 0 0 267 New Jersey 0 9.50
1 0 0 1 0 0 267 New Jersey 1 8.00
1 0 0 0 1 0 268 New Jersey 0 24.00
1 0 0 0 1 0 268 New Jersey 1 25.00
1 0 0 0 1 0 269 New Jersey 0 14.50
1 0 0 0 1 0 269 New Jersey 1 12.50
1 0 0 0 0 1 270 New Jersey 0 29.00
1 0 0 0 0 1 270 New Jersey 1 22.50
1 0 0 0 0 1 271 New Jersey 0 10.50
1 0 0 0 0 1 271 New Jersey 1 11.50
1 0 0 0 0 1 272 New Jersey 0 18.00
1 0 0 0 0 1 272 New Jersey 1 23.50
1 0 1 0 0 0 273 New Jersey 0 NA
1 0 1 0 0 0 273 New Jersey 1 30.00
1 0 0 1 0 0 274 New Jersey 0 9.00
1 0 0 1 0 0 274 New Jersey 1 16.50
1 0 1 0 0 0 275 New Jersey 0 33.00
1 0 1 0 0 0 275 New Jersey 1 29.00
1 0 1 0 0 0 276 New Jersey 0 19.50
1 0 1 0 0 0 276 New Jersey 1 30.00
1 0 1 0 0 0 277 New Jersey 0 41.00
1 0 1 0 0 0 277 New Jersey 1 22.50
1 0 1 0 0 0 278 New Jersey 0 17.00
1 0 1 0 0 0 278 New Jersey 1 24.00
1 0 1 0 0 0 279 New Jersey 0 18.25
1 0 1 0 0 0 279 New Jersey 1 21.75
1 0 0 1 0 0 280 New Jersey 0 17.00
1 0 0 1 0 0 280 New Jersey 1 13.50
1 0 0 0 1 0 281 New Jersey 0 32.00
1 0 0 0 1 0 281 New Jersey 1 24.00
1 0 0 0 1 0 282 New Jersey 0 26.00
1 0 0 0 1 0 282 New Jersey 1 28.50
1 0 0 0 0 1 283 New Jersey 0 11.50
1 0 0 0 0 1 283 New Jersey 1 10.00
1 0 1 0 0 0 284 New Jersey 0 21.00
1 0 1 0 0 0 284 New Jersey 1 23.50
1 0 1 0 0 0 285 New Jersey 0 22.75
1 0 1 0 0 0 285 New Jersey 1 30.50
1 0 1 0 0 0 286 New Jersey 0 18.50
1 0 1 0 0 0 286 New Jersey 1 24.75
1 0 1 0 0 0 287 New Jersey 0 20.00
1 0 1 0 0 0 287 New Jersey 1 22.50
1 0 1 0 0 0 288 New Jersey 0 18.00
1 0 1 0 0 0 288 New Jersey 1 28.00
1 0 0 1 0 0 289 New Jersey 0 10.50
1 0 0 1 0 0 289 New Jersey 1 14.00
1 0 0 1 0 0 290 New Jersey 0 10.50
1 0 0 1 0 0 290 New Jersey 1 9.00
1 0 0 1 0 0 291 New Jersey 0 11.00
1 0 0 1 0 0 291 New Jersey 1 9.50
1 0 0 1 0 0 292 New Jersey 0 10.50
1 0 0 1 0 0 292 New Jersey 1 15.50
1 0 0 1 0 0 293 New Jersey 0 12.50
1 0 0 1 0 0 293 New Jersey 1 12.00
1 0 0 1 0 0 294 New Jersey 0 18.00
1 0 0 1 0 0 294 New Jersey 1 11.50
1 0 0 0 1 0 295 New Jersey 0 17.00
1 0 0 0 1 0 295 New Jersey 1 12.50
1 0 0 0 1 0 296 New Jersey 0 15.00
1 0 0 0 1 0 296 New Jersey 1 16.00
1 0 0 0 1 0 297 New Jersey 0 19.50
1 0 0 0 1 0 297 New Jersey 1 28.50
1 0 0 0 0 1 298 New Jersey 0 13.00
1 0 0 0 0 1 298 New Jersey 1 15.00
1 0 0 0 0 1 299 New Jersey 0 20.25
1 0 0 0 0 1 299 New Jersey 1 23.50
1 0 1 0 0 0 300 New Jersey 0 26.00
1 0 1 0 0 0 300 New Jersey 1 30.50
1 0 1 0 0 0 301 New Jersey 0 15.50
1 0 1 0 0 0 301 New Jersey 1 26.50
1 0 1 0 0 0 302 New Jersey 0 24.00
1 0 1 0 0 0 302 New Jersey 1 27.00
1 0 1 0 0 0 303 New Jersey 0 25.50
1 0 1 0 0 0 303 New Jersey 1 21.50
1 0 1 0 0 0 304 New Jersey 0 14.50
1 0 1 0 0 0 304 New Jersey 1 NA
1 0 1 0 0 0 305 New Jersey 0 21.50
1 0 1 0 0 0 305 New Jersey 1 21.50
1 0 0 1 0 0 306 New Jersey 0 14.00
1 0 0 1 0 0 306 New Jersey 1 37.50
1 0 0 1 0 0 307 New Jersey 0 20.00
1 0 0 1 0 0 307 New Jersey 1 11.50
1 0 0 0 1 0 308 New Jersey 0 22.50
1 0 0 0 1 0 308 New Jersey 1 21.00
1 0 0 0 1 0 309 New Jersey 0 30.00
1 0 0 0 1 0 309 New Jersey 1 28.00
1 0 0 0 0 1 310 New Jersey 0 24.00
1 0 0 0 0 1 310 New Jersey 1 24.00
1 0 0 0 0 1 311 New Jersey 0 22.00
1 0 0 0 0 1 311 New Jersey 1 23.00
1 0 1 0 0 0 312 New Jersey 0 13.00
1 0 1 0 0 0 312 New Jersey 1 19.00
1 0 0 1 0 0 313 New Jersey 0 5.00
1 0 0 1 0 0 313 New Jersey 1 7.00
1 0 0 0 1 0 314 New Jersey 0 13.50
1 0 0 0 1 0 314 New Jersey 1 14.00
1 0 1 0 0 0 315 New Jersey 0 28.00
1 0 1 0 0 0 315 New Jersey 1 NA
1 0 1 0 0 0 316 New Jersey 0 18.00
1 0 1 0 0 0 316 New Jersey 1 32.00
1 0 1 0 0 0 317 New Jersey 0 24.00
1 0 1 0 0 0 317 New Jersey 1 22.00
1 0 1 0 0 0 318 New Jersey 0 16.50
1 0 1 0 0 0 318 New Jersey 1 14.25
1 0 1 0 0 0 319 New Jersey 0 7.00
1 0 1 0 0 0 319 New Jersey 1 16.50
1 0 1 0 0 0 320 New Jersey 0 11.00
1 0 1 0 0 0 320 New Jersey 1 0.00
1 0 1 0 0 0 321 New Jersey 0 19.50
1 0 1 0 0 0 321 New Jersey 1 47.50
1 0 0 1 0 0 322 New Jersey 0 10.00
1 0 0 1 0 0 322 New Jersey 1 15.50
1 0 0 1 0 0 323 New Jersey 0 13.00
1 0 0 1 0 0 323 New Jersey 1 10.50
1 0 0 1 0 0 324 New Jersey 0 10.00
1 0 0 1 0 0 324 New Jersey 1 7.00
1 0 0 1 0 0 325 New Jersey 0 12.50
1 0 0 1 0 0 325 New Jersey 1 11.00
1 0 0 1 0 0 326 New Jersey 0 8.00
1 0 0 1 0 0 326 New Jersey 1 8.50
1 0 0 0 1 0 327 New Jersey 0 12.00
1 0 0 0 1 0 327 New Jersey 1 0.00
1 0 0 0 1 0 328 New Jersey 0 20.50
1 0 0 0 1 0 328 New Jersey 1 15.00
1 0 0 0 1 0 329 New Jersey 0 18.50
1 0 0 0 1 0 329 New Jersey 1 13.50
1 0 0 0 1 0 330 New Jersey 0 11.50
1 0 0 0 1 0 330 New Jersey 1 12.50
1 0 0 0 1 0 331 New Jersey 0 15.00
1 0 0 0 1 0 331 New Jersey 1 12.50
1 0 0 0 1 0 332 New Jersey 0 13.00
1 0 0 0 1 0 332 New Jersey 1 12.50
1 0 0 0 1 0 333 New Jersey 0 10.00
1 0 0 0 1 0 333 New Jersey 1 10.00
1 0 0 0 0 1 334 New Jersey 0 17.00
1 0 0 0 0 1 334 New Jersey 1 13.50
1 0 0 0 0 1 335 New Jersey 0 17.00
1 0 0 0 0 1 335 New Jersey 1 16.00
1 0 0 0 0 1 336 New Jersey 0 20.50
1 0 0 0 0 1 336 New Jersey 1 23.50
1 0 1 0 0 0 337 New Jersey 0 15.50
1 0 1 0 0 0 337 New Jersey 1 NA
1 0 1 0 0 0 338 New Jersey 0 19.50
1 0 1 0 0 0 338 New Jersey 1 34.00
1 0 0 0 1 0 339 New Jersey 0 30.50
1 0 0 0 1 0 339 New Jersey 1 32.50
1 0 0 0 1 0 340 New Jersey 0 23.00
1 0 0 0 1 0 340 New Jersey 1 21.50
1 0 0 0 1 0 341 New Jersey 0 13.00
1 0 0 0 1 0 341 New Jersey 1 20.00
1 0 0 0 1 0 342 New Jersey 0 30.25
1 0 0 0 1 0 342 New Jersey 1 43.50
1 0 0 0 0 1 343 New Jersey 0 18.00
1 0 0 0 0 1 343 New Jersey 1 17.50
1 0 1 0 0 0 344 New Jersey 0 10.00
1 0 1 0 0 0 344 New Jersey 1 7.00
1 0 1 0 0 0 345 New Jersey 0 19.50
1 0 1 0 0 0 345 New Jersey 1 NA
1 0 1 0 0 0 346 New Jersey 0 17.00
1 0 1 0 0 0 346 New Jersey 1 22.00
1 0 1 0 0 0 347 New Jersey 0 25.50
1 0 1 0 0 0 347 New Jersey 1 22.00
1 0 1 0 0 0 348 New Jersey 0 6.00
1 0 1 0 0 0 348 New Jersey 1 7.00
1 0 0 1 0 0 349 New Jersey 0 11.50
1 0 0 1 0 0 349 New Jersey 1 14.00
1 0 0 1 0 0 350 New Jersey 0 8.50
1 0 0 1 0 0 350 New Jersey 1 27.00
1 0 0 0 1 0 351 New Jersey 0 18.50
1 0 0 0 1 0 351 New Jersey 1 18.50
1 0 0 0 1 0 352 New Jersey 0 15.50
1 0 0 0 1 0 352 New Jersey 1 11.50
1 0 1 0 0 0 353 New Jersey 0 14.25
1 0 1 0 0 0 353 New Jersey 1 16.00
1 0 1 0 0 0 354 New Jersey 0 38.00
1 0 1 0 0 0 354 New Jersey 1 25.00
1 0 1 0 0 0 355 New Jersey 0 24.75
1 0 1 0 0 0 355 New Jersey 1 30.50
1 0 1 0 0 0 356 New Jersey 0 23.00
1 0 1 0 0 0 356 New Jersey 1 11.50
1 0 1 0 0 0 357 New Jersey 0 24.00
1 0 1 0 0 0 357 New Jersey 1 25.50
1 0 0 1 0 0 358 New Jersey 0 7.50
1 0 0 1 0 0 358 New Jersey 1 9.50
1 0 0 1 0 0 359 New Jersey 0 15.00
1 0 0 1 0 0 359 New Jersey 1 11.50
1 0 0 1 0 0 360 New Jersey 0 9.25
1 0 0 1 0 0 360 New Jersey 1 12.00
1 0 0 0 1 0 361 New Jersey 0 11.25
1 0 0 0 1 0 361 New Jersey 1 0.00
1 0 0 0 1 0 362 New Jersey 0 16.50
1 0 0 0 1 0 362 New Jersey 1 17.00
1 0 0 0 1 0 363 New Jersey 0 18.75
1 0 0 0 1 0 363 New Jersey 1 24.00
1 0 0 0 1 0 364 New Jersey 0 13.50
1 0 0 0 1 0 364 New Jersey 1 NA
1 0 0 0 1 0 365 New Jersey 0 21.75
1 0 0 0 1 0 365 New Jersey 1 NA
1 0 0 0 0 1 366 New Jersey 0 13.50
1 0 0 0 0 1 366 New Jersey 1 13.50
1 0 1 0 0 0 367 New Jersey 0 15.00
1 0 1 0 0 0 367 New Jersey 1 17.00
1 0 1 0 0 0 368 New Jersey 0 17.50
1 0 1 0 0 0 368 New Jersey 1 28.00
1 0 1 0 0 0 369 New Jersey 0 17.50
1 0 1 0 0 0 369 New Jersey 1 21.50
1 0 0 1 0 0 370 New Jersey 0 NA
1 0 0 1 0 0 370 New Jersey 1 30.00
1 0 0 1 0 0 371 New Jersey 0 15.50
1 0 0 1 0 0 371 New Jersey 1 19.50
1 0 0 1 0 0 372 New Jersey 0 16.50
1 0 0 1 0 0 372 New Jersey 1 20.00
1 0 0 0 1 0 373 New Jersey 0 13.50
1 0 0 0 1 0 373 New Jersey 1 14.50
1 0 0 0 0 1 374 New Jersey 0 13.00
1 0 0 0 0 1 374 New Jersey 1 14.50
1 0 1 0 0 0 375 New Jersey 0 11.50
1 0 1 0 0 0 375 New Jersey 1 0.00
1 0 1 0 0 0 376 New Jersey 0 29.05
1 0 1 0 0 0 376 New Jersey 1 31.50
1 0 1 0 0 0 377 New Jersey 0 13.00
1 0 1 0 0 0 377 New Jersey 1 11.50
1 0 1 0 0 0 378 New Jersey 0 18.00
1 0 1 0 0 0 378 New Jersey 1 20.00
1 0 1 0 0 0 379 New Jersey 0 30.00
1 0 1 0 0 0 379 New Jersey 1 34.00
1 0 1 0 0 0 380 New Jersey 0 20.50
1 0 1 0 0 0 380 New Jersey 1 25.00
1 0 1 0 0 0 381 New Jersey 0 10.00
1 0 1 0 0 0 381 New Jersey 1 10.00
1 0 1 0 0 0 382 New Jersey 0 NA
1 0 1 0 0 0 382 New Jersey 1 18.00
1 0 1 0 0 0 383 New Jersey 0 26.00
1 0 1 0 0 0 383 New Jersey 1 25.00
1 0 1 0 0 0 384 New Jersey 0 12.50
1 0 1 0 0 0 384 New Jersey 1 23.00
1 0 0 1 0 0 385 New Jersey 0 19.50
1 0 0 1 0 0 385 New Jersey 1 17.50
1 0 0 1 0 0 386 New Jersey 0 12.00
1 0 0 1 0 0 386 New Jersey 1 23.00
1 0 0 0 1 0 387 New Jersey 0 36.00
1 0 0 0 1 0 387 New Jersey 1 26.00
1 0 0 0 1 0 388 New Jersey 0 21.00
1 0 0 0 1 0 388 New Jersey 1 16.50
1 0 0 0 1 0 389 New Jersey 0 24.50
1 0 0 0 1 0 389 New Jersey 1 35.50
1 0 0 0 1 0 390 New Jersey 0 20.00
1 0 0 0 1 0 390 New Jersey 1 31.00
1 0 0 0 1 0 391 New Jersey 0 26.50
1 0 0 0 1 0 391 New Jersey 1 23.00
1 0 0 0 1 0 392 New Jersey 0 15.00
1 0 0 0 1 0 392 New Jersey 1 22.50
1 0 0 0 1 0 393 New Jersey 0 19.00
1 0 0 0 1 0 393 New Jersey 1 21.50
1 0 0 0 1 0 394 New Jersey 0 17.50
1 0 0 0 1 0 394 New Jersey 1 12.00
1 0 1 0 0 0 395 New Jersey 0 20.50
1 0 1 0 0 0 395 New Jersey 1 20.50
1 0 1 0 0 0 396 New Jersey 0 21.00
1 0 1 0 0 0 396 New Jersey 1 25.00
1 0 0 1 0 0 397 New Jersey 0 10.50
1 0 0 1 0 0 397 New Jersey 1 8.50
1 0 1 0 0 0 398 New Jersey 0 23.00
1 0 1 0 0 0 398 New Jersey 1 13.00
1 0 1 0 0 0 399 New Jersey 0 23.00
1 0 1 0 0 0 399 New Jersey 1 21.50
1 0 1 0 0 0 400 New Jersey 0 29.00
1 0 1 0 0 0 400 New Jersey 1 23.00
1 0 1 0 0 0 401 New Jersey 0 21.00
1 0 1 0 0 0 401 New Jersey 1 25.50
1 0 1 0 0 0 402 New Jersey 0 41.50
1 0 1 0 0 0 402 New Jersey 1 21.50
1 0 0 1 0 0 403 New Jersey 0 12.00
1 0 0 1 0 0 403 New Jersey 1 9.50
1 0 0 1 0 0 404 New Jersey 0 33.00
1 0 0 1 0 0 404 New Jersey 1 36.50
1 0 0 1 0 0 405 New Jersey 0 6.50
1 0 0 1 0 0 405 New Jersey 1 16.00
1 0 0 1 0 0 406 New Jersey 0 9.00
1 0 0 1 0 0 406 New Jersey 1 23.75
1 0 0 1 0 0 407 New Jersey 0 9.75
1 0 0 1 0 0 407 New Jersey 1 17.50
1 0 0 0 1 0 408 New Jersey 0 24.50
1 0 0 0 1 0 408 New Jersey 1 20.50
1 0 0 0 0 1 409 New Jersey 0 14.00
1 0 0 0 0 1 409 New Jersey 1 20.50
1 0 0 0 0 1 410 New Jersey 0 19.50
1 0 0 0 0 1 410 New Jersey 1 25.00

Here is a ggplot of the data, overlaid with the summary statistics (group means and trend lines):

# Creating group level summaries 

card_state_summaries = card %>% group_by(State, Time) %>%
  summarise(Outcome = mean(Outcome, na.rm = T))

# Visualize the data 

ggplot(card, aes(x = Time, y = Outcome, color = State)) +
  geom_point(position = "jitter",
             alpha = 0.15) +
  geom_line(stat='summary', fun.y='mean', linetype = "longdash") +
  geom_point(card_state_summaries, 
             mapping = aes(x = Time, y = Outcome, color = State), 
             size = 3, shape = 19, position = position_dodge(width = 0.05)) +
  theme_economist_white(gray_bg = TRUE)

And a table that produces the coefficients from a regression with the above specification:

# Estimation

dif_model1 = lm_robust(Outcome ~ nj + Time + nj*Time,  data = card) %>%
  extract.lm_robust(include.ci = FALSE)

dif_model2 = lm_robust(Outcome ~ nj + Time + nj*Time,
                      clusters = ID,
                      data = card) %>%
  extract.lm_robust(include.ci = FALSE)

texreg::htmlreg(list(dif_model1, dif_model2),
                stars = c(0.001, 0.01, 0.05),
                custom.coef.names = c("(Intercept)", "New Jersey", "Time",
  "New Jersey x Time"),
  custom.model.names = c("Baseline", "Clustered SEs"),
  digits = 3,
  caption = "Card and Krueger (1994)"
  )
Card and Krueger (1994)
Baseline Clustered SEs
(Intercept) 23.331*** 23.331***
(1.351) (1.351)
New Jersey -2.892* -2.892*
(1.444) (1.444)
Time -2.166 -2.166
(1.648) (1.222)
New Jersey x Time 2.754 2.754*
(1.801) (1.310)
R2 0.007 0.007
Adj. R2 0.004 0.004
Num. obs. 794 794
RMSE 9.406 9.406
p < 0.001, p < 0.01, p < 0.05

Exercise 2

Using the regression table, can you tell me what the following quantities are?

Figure 2: Card and Krueger, Table 3

Figure 2: Card and Krueger, Table 3

Answers:

Here is what I get:

Figure 3: Card and Krueger, Table 3

Figure 3: Card and Krueger, Table 3