rm(list=ls())
library(wooldridge) ## Warning: package 'wooldridge' was built under R version 4.1.3library(lmtest) # load package; to conduct hypothesis test using robust SE## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(plm)
data("k401ksubs")###(i) How many single-person households are there in the data set? 2017
single.house <- subset(k401ksubs, fsize==1)
sum(single.house$fsize)## [1] 2017fit.ksubs <- lm(nettfa~inc+age, data = `single.house`)
summary(fit.ksubs)##
## Call:
## lm(formula = nettfa ~ inc + age, data = single.house)
##
## Residuals:
## Min 1Q Median 3Q Max
## -179.95 -14.16 -3.42 6.03 1113.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -43.03981 4.08039 -10.548 <2e-16 ***
## inc 0.79932 0.05973 13.382 <2e-16 ***
## age 0.84266 0.09202 9.158 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 44.68 on 2014 degrees of freedom
## Multiple R-squared: 0.1193, Adjusted R-squared: 0.1185
## F-statistic: 136.5 on 2 and 2014 DF, p-value: < 2.2e-16The coefficient is positive and statistically significant. However the number is not large. I think it is expected as age goes up and income goes up net financial wealth also goes up.
summary(single.house)## e401k inc marr male
## Min. :0.0000 Min. : 10.01 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 17.88 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median : 25.25 Median :0.00000 Median :1.0000
## Mean :0.3604 Mean : 29.45 Mean :0.01834 Mean :0.5419
## 3rd Qu.:1.0000 3rd Qu.: 36.00 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.0000 Max. :143.07 Max. :1.00000 Max. :1.0000
## age fsize nettfa p401k
## Min. :25.00 Min. :1 Min. :-143.50 Min. :0.0000
## 1st Qu.:30.00 1st Qu.:1 1st Qu.: -0.13 1st Qu.:0.0000
## Median :37.00 Median :1 Median : 1.40 Median :0.0000
## Mean :39.28 Mean :1 Mean : 13.60 Mean :0.2429
## 3rd Qu.:47.00 3rd Qu.:1 3rd Qu.: 13.20 3rd Qu.:0.0000
## Max. :64.00 Max. :1 Max. :1134.10 Max. :1.0000
## pira incsq agesq
## Min. :0.0000 Min. : 100.2 Min. : 625
## 1st Qu.:0.0000 1st Qu.: 319.7 1st Qu.: 900
## Median :0.0000 Median : 637.3 Median :1369
## Mean :0.2142 Mean : 1144.9 Mean :1660
## 3rd Qu.:0.0000 3rd Qu.: 1296.0 3rd Qu.:2209
## Max. :1.0000 Max. :20468.2 Max. :4096The intercept is and adjustment for the Salary. Would make not much sense since we are using age as a control and 0 age is not really meaningful.
(0.84266 - 1 )/0.09202## [1] -1.709846qnorm(.99)## [1] 2.326348pnorm(-1.71)## [1] 0.04363294#t.test(single.house$age, mu=1) # whty this doesn't work?As we can see the result is -1.709846 t test and p value = 0.044, which is not enough to reject the null hypothesis at 1%, but it is enough to reject at 5%
fit.ksubs <- lm(nettfa~inc, data = `single.house`)
summary(fit.ksubs)##
## Call:
## lm(formula = nettfa ~ inc, data = single.house)
##
## Residuals:
## Min 1Q Median 3Q Max
## -185.12 -12.85 -4.85 1.78 1112.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.5709 2.0607 -5.13 3.18e-07 ***
## inc 0.8207 0.0609 13.48 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 45.59 on 2015 degrees of freedom
## Multiple R-squared: 0.08267, Adjusted R-squared: 0.08222
## F-statistic: 181.6 on 1 and 2015 DF, p-value: < 2.2e-16Actually it is very similar, probably because it is consistent with the correct especification. However B0 Change a lot
###Logically, what are the smallest and largest values that can be taken on by the variable score? What are the smallest and largest values in the sample?
data.frame(econmath)## age work study econhs colgpa hsgpa acteng actmth act mathscr male calculus
## 1 23 15.0 10.0 0 3.4909 3.355 24 26 27 10 1 1
## 2 23 0.0 22.5 1 2.1000 3.219 23 20 24 9 1 0
## 3 21 25.0 12.0 0 3.0851 3.306 21 24 21 8 1 1
## 4 22 30.0 40.0 0 2.6805 3.977 31 28 31 10 0 1
## 5 22 25.0 15.0 1 3.7454 3.890 28 31 32 8 1 1
## 6 22 0.0 30.0 0 3.0555 3.500 25 30 28 10 1 1
## 7 22 20.0 25.0 1 2.1666 3.000 15 19 18 9 0 1
## 8 22 20.0 15.0 0 3.2544 3.770 28 30 32 9 1 1
## 9 22 28.0 7.0 0 3.1298 3.927 28 28 30 6 0 0
## 10 21 22.5 25.0 0 2.2424 2.770 18 19 17 9 0 1
## 11 21 0.0 25.0 0 2.8666 3.677 25 23 24 10 0 1
## 12 20 30.0 15.0 0 2.9363 3.222 27 21 26 8 0 1
## 13 19 8.0 22.5 1 3.6607 3.575 27 21 23 9 0 0
## 14 20 22.5 17.5 1 1.6511 3.394 22 19 22 7 0 0
## 15 22 10.0 12.0 1 2.7405 3.260 NA NA NA 7 0 1
## 16 19 10.0 20.0 0 3.3898 3.842 28 28 26 9 0 1
## 17 21 22.5 10.5 0 1.8604 2.760 17 16 17 2 0 0
## 18 20 10.0 10.0 0 3.0952 3.389 24 26 24 9 0 1
## 19 21 25.0 12.0 0 2.5078 3.378 28 24 29 6 0 0
## 20 21 10.0 20.0 1 3.6931 3.980 28 23 24 9 0 1
## 21 28 0.0 20.0 1 2.4890 3.450 21 21 19 6 0 0
## 22 22 20.0 4.0 0 2.8026 3.244 18 18 20 4 0 0
## 23 19 12.5 11.0 0 2.8600 3.812 23 26 23 8 1 1
## 24 20 0.0 20.0 1 1.9693 3.160 23 21 21 9 0 1
## 25 21 25.0 25.0 1 2.6272 3.750 32 25 28 8 1 1
## 26 21 25.0 25.0 0 1.9500 3.024 25 24 25 4 1 0
## 27 20 17.5 20.0 0 3.9892 4.000 32 32 33 10 0 1
## 28 21 12.0 20.0 0 3.2680 3.750 23 19 24 6 0 1
## 29 21 20.0 10.0 0 3.0925 3.612 21 27 23 10 0 1
## 30 21 10.0 30.0 0 3.3600 3.220 24 29 27 10 1 1
## 31 21 20.0 10.0 0 3.4824 3.254 29 21 26 5 1 0
## 32 21 27.5 12.0 1 2.0750 3.225 20 19 20 7 0 1
## 33 21 0.0 25.0 0 3.7651 3.750 27 30 28 10 1 1
## 34 20 10.0 30.0 1 2.8245 3.694 21 24 24 9 0 1
## 35 21 10.0 15.0 0 3.0743 3.257 26 23 23 5 0 0
## 36 21 12.0 15.0 0 2.5281 3.210 13 12 15 7 0 0
## 37 21 20.0 30.0 1 2.1126 3.405 12 18 15 7 0 1
## 38 21 12.0 13.5 0 3.1046 2.861 20 18 19 8 0 1
## 39 21 4.0 8.0 0 2.1323 3.263 24 22 24 9 0 1
## 40 21 18.0 15.0 1 2.7635 3.359 24 22 25 7 1 1
## 41 22 0.0 7.0 0 1.7222 2.920 20 22 22 5 1 0
## 42 21 35.0 5.0 0 2.0283 3.367 21 25 23 9 1 1
## 43 21 14.0 17.5 0 2.4303 3.180 15 23 18 9 1 1
## 44 20 10.0 12.5 1 2.3145 3.490 23 28 23 7 0 1
## 45 21 0.0 2.0 0 3.7867 4.080 21 26 25 10 0 1
## 46 21 17.0 12.0 0 3.0922 3.436 21 25 23 10 1 1
## 47 21 25.0 10.0 1 2.1171 3.275 15 20 18 9 1 1
## 48 20 15.0 15.0 0 2.5727 3.166 26 25 27 9 0 1
## 49 22 25.0 20.0 0 2.5000 3.210 22 23 19 9 1 0
## 50 20 20.0 5.0 1 2.5833 3.263 25 25 26 7 0 0
## 51 21 10.0 20.0 1 2.8461 3.750 24 27 28 10 1 1
## 52 21 14.0 25.0 0 3.3709 3.162 19 20 21 3 1 1
## 53 21 16.0 2.0 0 3.4943 3.984 29 36 31 10 1 1
## 54 20 13.0 10.0 1 2.9444 3.380 25 18 23 9 0 1
## 55 20 13.5 7.5 0 2.5326 3.190 24 29 25 9 1 1
## 56 21 0.0 9.0 0 2.3125 2.658 16 20 19 7 1 1
## 57 19 0.0 15.0 0 2.3928 3.129 17 25 21 8 1 1
## 58 21 13.5 50.0 1 2.1811 3.583 12 15 13 4 1 1
## 59 20 20.0 17.5 0 2.2666 3.077 29 25 28 8 0 0
## 60 20 0.0 12.5 0 3.2857 3.456 21 18 20 8 1 1
## 61 20 5.0 6.0 0 2.6770 3.388 19 19 18 5 0 0
## 62 20 12.5 12.0 0 2.7234 2.740 25 20 22 7 0 1
## 63 20 6.0 10.0 0 3.4019 3.289 19 19 20 6 1 0
## 64 20 19.5 20.0 0 2.6491 3.571 20 28 24 9 0 1
## 65 21 15.0 20.0 1 2.2500 3.180 16 17 17 1 0 1
## 66 20 5.0 10.0 0 2.0000 2.776 21 22 24 7 1 1
## 67 20 15.0 20.0 0 2.0510 3.937 27 29 28 10 0 1
## 68 19 18.0 7.5 1 2.6176 3.070 16 23 18 10 1 1
## 69 19 17.0 16.0 0 3.1458 3.514 23 24 22 8 0 1
## 70 19 0.0 22.0 0 3.4285 3.533 23 24 25 10 1 1
## 71 19 10.0 8.0 1 2.8620 3.500 23 24 25 10 1 1
## 72 19 8.0 6.0 1 3.0535 3.743 25 29 25 9 1 1
## 73 20 0.0 8.0 0 2.7636 2.989 17 21 20 4 1 1
## 74 20 0.0 2.0 0 1.9732 3.690 18 25 20 5 1 1
## 75 20 12.0 7.0 0 2.4622 3.000 22 20 20 3 1 0
## 76 20 15.0 5.0 0 2.0106 3.207 26 21 25 7 1 1
## 77 20 8.0 10.0 1 2.7169 3.220 21 22 21 9 1 1
## 78 21 0.0 7.5 0 2.8939 3.648 25 29 27 10 0 1
## 79 19 25.0 10.0 1 2.5208 3.530 27 25 27 9 0 0
## 80 20 0.0 10.0 0 2.4038 3.490 22 29 25 10 1 1
## 81 19 14.0 30.0 0 2.3666 2.886 23 20 20 8 0 1
## 82 20 8.0 11.0 1 2.2454 3.058 20 23 20 8 0 1
## 83 20 22.5 14.0 0 2.7796 3.206 25 27 25 9 0 1
## 84 20 25.0 12.5 1 3.0090 3.514 26 17 24 5 0 1
## 85 20 7.0 15.0 0 2.5250 2.834 24 23 22 10 1 1
## 86 19 10.0 35.0 1 3.4347 3.650 24 20 22 9 0 0
## 87 21 8.0 10.0 1 2.5964 3.178 14 19 17 8 1 1
## 88 20 23.5 12.5 0 2.2325 2.870 25 17 25 5 0 0
## 89 20 0.0 15.0 0 2.2000 2.738 19 19 21 8 0 0
## 90 20 0.0 12.5 0 2.2916 3.222 19 21 21 3 1 0
## 91 20 0.0 15.0 0 3.0517 3.256 24 19 23 8 0 1
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## 99 20 25.0 14.0 0 2.3367 3.111 20 20 20 7 1 0
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## 101 19 0.0 12.0 1 2.7118 3.000 16 18 19 9 0 0
## 102 21 0.0 15.0 1 2.2500 2.861 19 20 19 5 1 1
## 103 20 30.0 10.0 0 2.5118 3.469 25 19 24 7 0 1
## 104 20 0.0 15.0 0 2.4534 3.305 26 25 25 9 1 0
## 105 19 17.5 27.5 0 3.1071 3.571 23 25 24 5 0 1
## 106 19 0.0 15.0 0 3.3437 3.444 25 24 24 7 0 0
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## 110 20 0.0 25.0 0 2.2586 3.330 16 21 18 8 1 1
## 111 20 15.0 40.0 1 2.3725 3.019 25 18 23 7 1 1
## 112 19 0.0 6.0 0 2.1730 3.200 24 24 25 9 1 1
## 113 19 0.0 20.0 0 2.8571 3.155 19 17 18 5 0 0
## 114 20 13.0 10.0 0 2.2058 2.768 23 20 23 5 1 1
## 115 19 20.0 8.0 0 2.4537 3.230 24 27 25 8 0 1
## 116 20 15.0 7.0 0 2.5729 3.787 29 30 30 10 1 1
## 117 20 15.0 10.0 0 2.9827 3.350 24 26 24 8 1 1
## 118 20 3.0 12.0 1 2.8360 2.967 23 23 23 9 0 0
## 119 20 20.0 8.0 1 2.7539 3.076 NA NA NA 5 1 1
## 120 21 10.0 15.0 0 2.7193 2.940 19 21 21 7 1 1
## 121 20 15.0 10.0 1 2.7916 3.240 26 27 27 8 1 1
## 122 21 0.0 2.5 0 2.1304 3.211 26 27 28 9 1 1
## 123 20 10.0 7.0 0 2.8800 3.474 22 24 22 10 1 1
## 124 20 15.0 20.0 0 1.9326 3.499 27 30 29 10 1 1
## 125 20 0.0 10.0 0 3.7580 3.361 20 22 23 7 0 0
## 126 21 15.0 40.0 0 2.6057 2.650 23 29 25 7 1 1
## 127 20 0.0 10.0 0 3.0192 3.083 20 25 24 8 1 1
## 128 20 0.0 6.5 0 2.2282 2.861 16 18 18 6 1 0
## 129 20 0.0 20.0 0 2.4722 3.305 20 19 22 6 0 0
## 130 20 0.0 7.5 0 1.8039 3.245 13 18 17 8 0 0
## 131 20 0.0 12.5 0 2.8000 3.382 24 29 28 5 1 1
## 132 20 0.0 10.0 1 2.4326 3.250 21 23 24 10 1 1
## 133 20 12.5 20.0 1 1.9803 3.158 31 33 30 9 1 1
## 134 19 10.0 10.0 0 3.1964 3.870 15 19 17 6 0 0
## 135 19 15.0 10.0 0 3.1666 3.000 26 26 25 8 0 1
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## 141 19 0.0 25.0 0 2.6551 3.229 24 20 21 1 0 1
## 142 18 0.0 12.5 0 3.6428 3.780 28 23 28 9 0 1
## 143 19 19.0 20.0 1 2.9310 3.500 22 24 24 9 0 0
## 144 19 8.0 20.0 0 2.5517 3.000 23 25 26 9 1 1
## 145 19 0.0 6.0 1 2.3103 3.210 21 22 21 9 1 1
## 146 20 7.5 12.5 1 2.3673 2.943 17 14 17 4 0 0
## 147 20 13.0 5.0 1 3.2954 4.021 24 24 24 8 0 1
## 148 20 8.0 10.0 0 2.0118 3.315 20 24 22 10 1 1
## 149 20 0.0 20.0 0 3.5131 3.620 22 18 21 5 0 0
## 150 20 20.0 3.0 1 2.3979 3.476 21 23 24 9 1 1
## 151 20 0.0 25.0 0 2.4519 2.928 20 20 18 7 1 1
## 152 19 0.0 15.0 1 3.8653 3.786 24 24 24 7 0 1
## 153 19 20.0 6.0 0 3.6666 3.707 23 27 26 7 1 1
## 154 20 37.5 35.0 0 2.3272 2.963 17 18 17 6 0 1
## 155 20 0.0 23.0 0 2.6406 2.880 19 18 19 8 0 0
## 156 20 0.0 10.0 1 2.4824 2.631 19 17 18 8 0 1
## 157 20 10.0 20.0 1 3.2900 3.078 NA NA NA 9 1 1
## 158 20 0.0 20.0 1 2.9482 3.839 25 27 26 9 1 0
## 159 19 20.0 5.0 1 2.9375 3.165 23 28 23 6 1 1
## 160 18 10.0 7.5 1 1.6296 3.130 18 19 17 8 0 0
## 161 19 0.0 15.0 1 2.7500 3.972 28 29 26 9 1 1
## 162 20 17.0 10.0 1 2.6964 2.980 NA NA NA 9 1 1
## 163 19 15.0 4.0 1 2.0609 2.866 12 17 17 7 1 1
## 164 21 22.5 4.0 1 2.5731 2.678 20 23 21 10 1 1
## 165 19 0.0 20.0 0 3.0000 3.085 NA NA NA 9 0 0
## 166 19 0.0 12.0 0 1.6458 3.135 24 25 24 9 0 1
## 167 19 15.0 10.0 0 2.5625 3.180 19 22 19 6 0 0
## 168 19 8.0 10.0 0 2.9107 3.376 23 22 23 8 0 1
## 169 19 30.0 6.0 0 1.8953 2.945 30 24 29 9 0 0
## 170 20 27.5 17.5 0 2.4038 3.451 24 31 28 9 1 1
## 171 19 30.0 12.5 1 2.7857 3.680 18 23 21 5 0 0
## 172 19 6.0 7.0 0 2.7500 2.400 29 20 25 5 0 1
## 173 20 30.0 15.0 0 2.5254 3.527 20 25 22 8 0 1
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## 175 19 0.0 16.0 1 2.0750 3.038 13 19 16 8 1 1
## 176 19 20.0 17.5 0 1.7708 3.086 18 19 17 6 0 0
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## 178 19 0.0 8.0 1 3.1071 3.272 19 24 23 10 1 1
## 179 19 0.0 10.0 1 2.8275 3.783 26 23 26 9 1 1
## 180 19 8.0 10.0 1 3.1774 3.443 22 22 21 8 0 1
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## 182 19 0.0 10.0 0 3.5000 3.840 26 23 26 8 0 1
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## 185 20 0.0 8.0 0 2.7500 2.355 18 16 19 8 1 1
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## 191 19 0.0 20.0 1 2.9642 3.518 20 23 20 10 1 1
## 192 19 25.0 3.5 1 3.3035 3.306 24 25 25 9 1 0
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## 207 19 10.0 15.0 0 3.1250 3.666 26 23 23 7 1 1
## 208 19 15.0 6.0 1 2.2000 2.864 21 25 23 8 1 1
## 209 19 0.0 3.0 0 3.6250 3.191 24 20 22 7 1 1
## 210 20 0.0 10.0 0 3.3833 3.355 18 22 20 8 0 1
## 211 19 30.0 10.0 0 2.2500 2.936 21 24 20 9 1 1
## 212 19 20.0 12.0 1 2.8571 3.298 23 20 21 6 1 1
## 213 19 7.5 7.0 0 2.9375 3.029 22 20 20 10 0 0
## 214 20 15.0 40.0 0 3.1296 3.597 15 25 20 10 1 1
## 215 19 0.0 13.5 0 3.4827 3.269 26 20 23 5 1 1
## 216 20 15.0 20.0 0 3.3750 3.694 24 24 23 9 1 1
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## 218 19 5.0 10.0 1 3.3703 3.222 23 24 23 8 0 1
## 219 19 0.0 8.5 1 3.1551 3.213 22 25 23 9 1 1
## 220 19 10.0 20.0 1 3.1964 3.587 26 27 27 8 0 0
## 221 19 20.0 30.0 0 2.2241 3.420 26 20 26 7 1 0
## 222 19 0.0 2.0 0 2.3214 2.911 27 28 29 6 1 1
## 223 19 0.0 17.5 0 2.6785 3.017 21 23 22 7 0 1
## 224 19 0.0 17.5 0 2.7656 3.015 24 24 23 9 1 1
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## 227 19 11.5 10.0 0 3.5208 3.472 19 27 22 9 1 1
## 228 18 5.0 5.0 1 2.3043 3.000 22 21 23 6 1 0
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## 230 19 20.0 25.0 1 3.3750 3.486 19 24 20 6 1 1
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## 232 19 0.0 5.0 0 2.6400 3.528 19 25 21 10 1 1
## 233 19 5.0 3.0 0 3.2500 2.806 21 20 21 4 0 1
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## 236 19 0.0 10.0 0 2.6128 3.446 22 21 22 8 0 1
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## 238 19 12.0 17.5 0 2.2285 3.750 24 23 24 9 0 1
## 239 19 0.0 10.0 0 3.1666 3.463 23 28 24 8 1 1
## 240 19 0.0 15.0 1 2.2727 3.028 17 17 18 5 0 0
## 241 19 22.5 15.0 0 2.4807 3.040 21 23 20 4 0 1
## 242 19 20.0 20.0 0 2.8833 3.430 15 25 22 9 1 1
## 243 19 13.0 20.0 0 2.2799 2.883 18 17 19 7 1 1
## 244 19 0.0 20.0 1 2.4827 3.129 22 17 23 10 1 1
## 245 19 0.0 9.0 0 3.6000 3.529 24 28 24 9 1 0
## 246 19 10.0 15.0 0 3.4821 3.589 27 24 25 6 0 0
## 247 19 15.0 10.0 1 3.2758 4.198 21 27 24 9 0 1
## 248 19 0.0 20.0 1 3.6607 3.675 19 22 19 9 1 0
## 249 19 0.0 9.0 1 3.4827 3.319 23 22 24 8 1 0
## 250 19 0.0 15.0 1 2.3200 2.838 25 21 24 8 0 1
## 251 19 0.0 27.5 1 3.3392 3.920 21 20 21 7 1 1
## 252 19 15.0 15.0 1 2.6896 3.500 21 21 22 6 0 0
## 253 19 20.0 20.0 0 1.8695 3.233 17 19 17 7 0 0
## 254 19 4.5 25.0 1 3.7857 3.281 19 21 21 6 1 0
## 255 19 0.0 6.0 0 2.8333 3.126 20 20 20 8 1 1
## 256 19 0.0 40.0 1 3.7069 3.993 25 25 26 9 0 1
## 257 18 0.0 10.0 0 2.6128 3.486 22 22 23 8 0 1
## 258 19 10.0 10.0 0 3.8653 3.537 21 24 22 6 0 0
## 259 19 10.0 2.0 0 2.0200 2.861 15 20 20 8 1 0
## 260 19 0.0 10.0 0 2.6128 3.490 29 20 26 8 0 1
## 261 19 0.0 12.0 0 2.7777 3.171 19 22 20 10 1 0
## 262 19 18.0 20.0 0 2.1666 2.778 18 19 18 8 0 1
## 263 19 5.0 6.0 1 2.8392 2.944 22 20 21 8 1 1
## 264 19 0.0 25.0 0 3.5161 3.763 24 31 30 9 1 0
## 265 19 0.0 12.0 0 1.9807 3.241 26 26 25 9 1 1
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## 593 19 17.5 15.0 1 3.3214 3.180 19 25 21 7 0 1
## 594 19 15.0 12.0 0 3.5000 3.583 26 30 27 10 0 1
## 595 19 0.0 14.5 0 2.3035 3.770 24 25 23 8 0 1
## 596 19 0.0 12.5 1 3.3653 3.875 25 29 27 10 0 1
## 597 19 17.0 20.0 1 3.5666 3.569 18 22 18 10 0 1
## 598 19 10.0 13.0 0 2.7037 3.444 15 19 17 7 1 1
## 599 20 0.0 15.0 0 3.3035 3.094 26 28 25 10 0 0
## 600 19 20.0 8.5 0 2.5178 3.066 23 25 23 9 0 0
## 601 19 0.0 6.0 0 2.7678 2.916 20 22 25 2 1 0
## 602 19 0.0 10.0 0 2.6128 3.487 24 24 26 10 1 1
## 603 19 0.0 8.0 1 2.2321 3.103 23 27 25 7 1 1
## 604 19 17.5 12.5 0 2.7741 3.280 21 26 23 8 1 0
## 605 19 15.0 20.0 1 2.6071 3.738 23 25 25 10 0 1
## 606 19 0.0 15.0 1 2.9444 3.572 26 26 28 9 1 1
## 607 19 15.0 15.0 1 3.7272 3.520 18 19 19 9 0 0
## 608 19 0.0 25.0 0 3.6964 3.690 27 27 25 10 0 1
## 609 19 20.0 1.0 1 2.8518 2.920 18 24 19 9 0 1
## 610 19 0.0 15.0 0 2.9821 2.530 14 16 15 5 1 1
## 611 18 0.0 15.0 0 3.0416 3.055 22 20 21 6 1 0
## 612 20 0.0 30.0 0 2.9333 3.582 19 21 20 9 1 1
## 613 19 14.0 7.0 0 2.4807 2.604 20 23 20 9 1 1
## 614 18 0.0 7.0 0 3.5217 2.857 20 24 23 7 1 0
## 615 19 0.0 12.5 1 3.4821 3.711 28 26 27 8 1 1
## 616 19 10.0 19.0 0 2.9428 3.240 17 24 20 6 0 1
## 617 18 12.0 12.0 1 1.6363 3.260 13 16 15 7 0 0
## 618 19 27.0 13.5 0 3.0178 3.565 24 26 26 9 0 1
## 619 18 15.0 35.0 1 2.3571 2.730 21 22 21 9 0 0
## 620 19 22.5 12.5 0 2.5000 3.628 20 20 21 5 0 0
## 621 19 0.0 17.5 0 2.4791 2.972 23 20 20 10 0 1
## 622 19 0.0 25.0 1 2.5370 4.260 23 21 24 10 0 1
## 623 19 0.0 25.0 1 1.7692 3.268 20 19 20 7 1 0
## 624 19 0.0 15.0 1 3.1964 3.223 25 20 22 8 1 1
## 625 19 6.5 6.0 1 2.9827 3.361 20 27 24 6 1 1
## 626 19 0.0 10.0 0 1.8035 3.777 22 26 24 9 0 1
## 627 19 0.0 12.5 0 3.3846 3.700 26 25 26 7 0 0
## 628 18 18.0 12.0 1 2.3235 3.070 21 17 19 5 1 0
## 629 20 25.0 2.5 1 3.0689 3.493 30 26 29 7 1 0
## 630 19 22.5 40.0 1 2.0454 3.000 16 15 15 6 0 0
## 631 18 14.0 20.0 0 2.7069 3.700 29 26 28 10 0 1
## 632 24 30.0 25.0 0 2.0769 3.056 21 18 19 6 0 0
## 633 18 5.0 14.0 1 3.4464 3.500 21 19 23 9 0 1
## 634 22 30.0 15.0 0 3.2688 3.680 22 28 26 9 1 1
## 635 22 0.0 8.0 0 2.5186 3.441 21 22 20 9 1 1
## 636 20 0.0 12.5 0 2.9782 2.990 19 25 23 10 0 1
## 637 20 16.0 7.5 0 2.3642 3.170 23 21 24 10 1 1
## 638 21 0.0 40.0 1 2.3082 3.038 24 21 24 9 0 0
## 639 21 12.0 6.0 1 3.0738 3.571 24 22 23 8 0 1
## 640 21 0.0 5.0 0 2.8133 2.861 25 28 28 10 1 1
## 641 21 15.0 10.0 0 3.0645 2.726 28 21 27 8 0 1
## 642 21 25.0 4.0 0 2.7926 3.277 29 25 28 9 0 1
## 643 20 0.0 6.0 0 2.6304 3.194 23 21 22 9 1 1
## 644 21 0.0 10.0 0 2.3546 3.160 24 19 24 6 1 0
## 645 21 20.0 20.0 0 2.3888 3.110 28 26 27 9 1 1
## 646 20 8.0 25.0 1 3.9649 3.930 29 28 29 8 0 0
## 647 19 0.0 6.5 0 3.2500 3.480 22 26 24 9 0 1
## 648 19 0.0 8.0 0 2.7258 3.564 28 29 26 9 0 1
## 649 19 15.0 20.0 0 2.8000 3.740 16 20 18 8 0 0
## 650 22 14.0 8.0 1 3.3750 3.672 22 20 23 8 1 0
## 651 21 0.0 21.0 0 1.7093 3.397 NA NA NA 9 0 1
## 652 20 15.0 10.0 1 1.8703 3.250 21 25 22 9 0 0
## 653 19 15.0 10.0 0 2.0000 3.297 NA NA NA 6 0 1
## 654 20 12.0 6.5 1 2.8333 3.750 26 29 26 8 1 1
## 655 20 8.0 6.0 1 3.2968 3.744 22 22 22 9 0 0
## 656 19 0.0 21.0 0 3.6785 3.661 23 27 25 5 0 1
## 657 19 12.0 15.0 1 2.1333 2.975 21 22 19 9 0 0
## 658 20 20.0 25.0 1 2.7741 3.000 21 17 21 8 1 0
## 659 20 4.0 6.0 0 3.0446 3.768 20 22 22 7 0 0
## 660 20 0.0 10.0 0 2.2692 2.966 21 20 21 7 0 0
## 661 20 13.0 10.0 0 3.4830 3.630 30 29 30 9 0 1
## 662 20 8.0 6.0 1 3.4435 3.908 30 31 31 10 0 1
## 663 19 15.0 7.0 0 2.6451 3.566 30 27 29 9 0 1
## 664 19 25.0 10.0 0 3.3823 3.894 31 29 30 10 0 1
## 665 20 30.0 5.0 1 2.5267 3.358 18 26 20 10 1 1
## 666 20 25.0 15.0 1 2.6949 3.800 27 27 29 9 0 0
## 667 20 0.0 15.0 0 2.3557 3.527 22 28 24 9 0 1
## 668 20 12.5 7.5 1 3.2945 3.824 31 23 26 10 0 1
## 669 20 20.0 12.0 0 2.2500 2.810 25 20 24 8 0 1
## 670 20 13.5 12.0 1 2.8684 3.230 27 23 25 7 0 1
## 671 20 21.0 20.0 0 3.0086 2.796 21 18 21 8 0 0
## 672 19 0.0 7.0 0 2.4042 2.959 26 25 24 9 0 1
## 673 20 15.0 10.0 0 1.9313 3.124 27 27 24 10 1 1
## 674 20 0.0 25.0 0 2.7946 3.370 18 22 20 10 0 1
## 675 20 20.0 16.0 1 2.3617 3.306 24 14 20 4 1 0
## 676 20 0.0 15.0 0 2.4673 3.194 23 23 22 7 0 0
## 677 20 25.0 10.0 0 2.6346 3.581 27 17 25 7 0 0
## 678 20 0.0 6.0 0 2.2755 3.103 25 21 23 8 1 1
## 679 19 10.0 20.0 0 2.5983 3.353 20 19 21 10 0 1
## 680 19 0.0 20.0 1 3.0227 3.410 20 17 20 8 1 0
## 681 20 10.0 20.0 0 2.4523 2.980 17 17 16 5 0 1
## 682 19 0.0 28.0 1 2.7500 3.736 24 26 22 7 1 1
## 683 20 12.5 12.5 0 3.8070 4.000 24 24 27 8 0 1
## 684 20 17.0 10.0 0 2.7241 3.314 26 21 22 7 0 0
## 685 20 12.0 7.0 0 2.3775 3.470 24 28 24 7 1 1
## 686 20 20.0 7.5 1 3.1311 3.353 26 21 24 6 0 0
## 687 20 12.0 10.0 0 2.6000 3.633 18 18 18 9 0 1
## 688 19 15.0 10.0 0 2.3728 3.189 27 24 28 7 0 0
## 689 21 0.0 15.0 0 2.7363 3.497 20 21 19 8 1 1
## 690 20 0.0 5.5 1 2.7672 3.055 22 27 24 9 1 1
## 691 20 11.0 20.0 0 1.7903 3.083 17 19 18 8 1 1
## 692 20 11.0 14.0 1 3.1346 4.194 NA NA NA 10 1 1
## 693 20 14.0 15.0 0 2.4716 3.081 19 29 23 10 1 1
## 694 20 12.0 15.0 0 3.2966 3.407 18 26 20 9 1 1
## 695 19 0.0 10.0 0 3.3152 3.222 25 27 27 9 1 1
## 696 20 0.0 22.5 0 3.1886 3.285 23 24 21 9 0 1
## 697 20 0.0 10.0 0 1.8076 2.609 NA NA NA 7 1 1
## 698 20 20.0 12.0 0 2.2692 3.625 23 27 24 9 1 1
## 699 19 0.0 40.0 0 3.4333 3.031 16 26 20 9 0 1
## 700 20 15.0 10.0 0 3.0916 3.630 25 24 24 8 0 1
## 701 20 0.0 6.0 0 3.1128 3.428 19 21 21 8 0 1
## 702 19 12.5 25.0 0 2.8269 3.457 18 19 21 6 0 0
## 703 19 0.0 15.0 1 3.2121 3.290 24 25 22 9 0 1
## 704 19 35.0 5.0 1 2.6666 3.556 34 25 29 10 0 1
## 705 20 9.0 17.5 0 2.4200 3.333 16 15 17 5 1 0
## 706 19 11.0 4.0 0 3.1379 3.083 26 25 25 9 1 0
## 707 20 12.0 35.0 0 2.9807 3.620 NA NA NA 9 1 0
## 708 19 0.0 10.0 0 2.9629 3.000 20 28 24 9 1 1
## 709 19 0.0 14.0 1 3.0714 3.070 NA NA NA 8 0 1
## 710 19 20.0 15.0 0 2.5185 2.981 20 22 22 7 0 1
## 711 19 7.0 14.0 0 3.1379 3.567 27 25 27 9 0 1
## 712 19 10.0 10.0 0 2.3400 3.696 21 26 25 9 1 1
## 713 19 13.0 9.0 1 1.8750 3.300 20 18 20 6 0 1
## 714 19 20.0 20.0 1 2.4444 3.360 21 20 20 9 1 0
## 715 20 0.0 10.0 0 2.5816 2.957 NA NA NA 9 1 1
## 716 19 0.0 11.0 0 2.9642 3.111 22 20 22 8 0 0
## 717 19 16.0 25.0 0 2.9285 3.100 22 20 23 6 0 0
## 718 18 11.0 15.0 1 2.5200 2.880 21 22 22 8 1 1
## 719 19 10.0 17.5 0 2.6206 3.545 33 26 29 9 0 1
## 720 19 0.0 20.0 1 3.0666 3.787 27 28 27 9 0 1
## 721 19 4.5 5.0 0 3.2857 3.816 29 29 28 10 0 1
## 722 19 0.0 12.0 1 1.9629 2.471 22 23 20 8 1 1
## 723 19 15.0 5.0 1 2.6607 3.580 18 24 20 8 1 1
## 724 18 10.0 12.0 0 3.1034 3.325 25 25 27 7 0 1
## 725 19 0.0 10.0 0 3.8620 3.830 22 29 26 10 1 1
## 726 19 0.0 17.5 1 3.5370 3.916 24 34 26 10 0 1
## 727 19 0.0 3.0 0 3.5185 3.444 25 26 26 9 1 1
## 728 19 0.0 2.0 0 2.5384 3.228 22 25 27 8 1 1
## 729 19 0.0 5.0 0 1.5178 2.777 21 27 23 10 1 1
## 730 19 12.0 20.0 0 3.4310 3.628 24 26 23 9 1 1
## 731 19 0.0 15.0 0 2.4848 2.750 20 26 21 8 0 1
## 732 19 6.0 15.0 1 2.4642 3.341 24 25 23 9 0 1
## 733 19 0.0 16.5 1 3.6379 3.981 26 27 26 9 1 0
## 734 19 0.0 8.0 1 2.9259 3.280 23 22 22 8 1 1
## 735 19 15.0 14.0 0 3.1833 3.407 21 25 23 9 1 1
## 736 19 0.0 7.5 0 3.1800 3.526 17 23 21 9 1 1
## 737 19 14.0 14.0 0 3.0892 3.176 19 19 19 7 0 0
## 738 19 11.5 15.0 1 2.2307 3.346 16 17 17 7 0 0
## 739 19 0.0 11.0 1 3.4137 3.625 23 21 22 7 0 1
## 740 19 12.0 30.0 0 3.8103 4.095 24 25 26 9 0 1
## 741 19 8.0 20.0 0 3.1935 3.914 22 25 25 9 0 1
## 742 19 0.0 48.0 0 3.7000 3.363 22 26 22 9 1 0
## 743 20 0.0 8.0 0 2.5303 3.194 26 28 28 6 0 0
## 744 19 0.0 20.0 0 3.4482 3.611 27 25 27 9 1 1
## 745 19 15.0 35.0 0 2.1200 3.686 26 23 25 8 0 0
## 746 19 0.0 10.0 0 2.1666 2.640 16 21 20 9 0 0
## 747 19 0.0 20.0 0 3.3214 3.777 23 25 23 10 1 1
## 748 19 0.0 2.0 1 3.6379 3.688 26 26 30 10 1 1
## 749 18 0.0 7.5 0 2.3214 3.025 22 19 22 1 1 0
## 750 19 0.0 25.0 0 2.3965 3.081 23 21 24 7 1 1
## 751 19 5.0 15.0 0 3.7419 3.517 23 27 25 10 0 1
## 752 19 0.0 9.0 0 3.2963 3.657 24 20 22 8 0 1
## 753 19 10.0 10.0 1 2.4400 3.647 20 20 21 4 0 0
## 754 19 10.0 15.0 0 2.9400 2.548 16 18 17 8 1 1
## 755 19 6.0 7.0 1 3.1000 3.730 26 25 26 8 0 1
## 756 29 22.5 14.0 0 2.9166 3.200 22 22 22 8 0 1
## 757 19 0.0 20.0 0 3.8461 3.460 28 29 28 10 1 1
## 758 19 0.0 17.5 0 3.2321 3.541 23 24 24 6 0 1
## 759 19 10.0 25.0 1 3.5689 3.860 24 29 26 8 0 1
## 760 19 17.5 12.5 1 2.9230 3.230 23 23 25 7 1 0
## 761 19 0.0 15.0 1 2.9464 3.740 18 24 22 4 1 0
## 762 18 10.0 25.0 0 2.5689 2.950 22 25 24 10 0 0
## 763 19 0.0 10.0 0 3.2500 3.778 23 22 23 10 0 1
## 764 19 15.0 10.0 1 2.7758 3.767 29 32 31 10 1 1
## 765 19 0.0 5.0 0 3.0192 3.648 29 29 30 9 0 1
## 766 18 0.0 10.0 0 2.0000 3.305 19 25 21 10 1 1
## 767 19 8.0 20.0 0 3.0000 3.257 19 19 19 6 0 1
## 768 19 10.0 30.0 0 3.3888 3.600 23 20 21 8 0 0
## 769 19 0.0 12.0 1 3.3181 4.000 26 29 27 10 1 1
## 770 19 12.5 11.0 0 3.2419 3.806 23 23 24 10 0 1
## 771 19 0.0 11.0 0 3.7678 3.919 24 29 29 9 1 1
## 772 20 10.0 4.0 1 2.6379 3.430 24 25 24 9 1 0
## 773 20 7.5 5.0 0 3.3518 3.840 30 24 28 8 1 1
## 774 19 0.0 10.0 1 3.0161 3.526 26 30 26 10 0 1
## 775 19 0.0 25.0 0 3.5892 3.828 21 28 24 8 0 1
## 776 19 0.0 20.0 0 1.9130 3.066 26 19 21 8 0 0
## 777 20 15.0 3.0 0 2.2037 3.058 30 24 28 8 1 0
## 778 20 0.0 10.0 1 3.3871 3.417 29 27 28 9 1 0
## 779 19 0.0 15.0 0 3.7142 4.000 23 27 25 9 1 1
## 780 19 0.0 12.0 0 3.1093 3.189 20 22 20 9 1 1
## 781 19 15.0 15.0 0 3.6290 3.927 28 29 27 9 1 1
## 782 19 15.0 17.5 0 2.1250 3.190 15 22 18 9 0 1
## 783 19 0.0 12.0 1 2.6000 3.104 20 21 21 6 1 0
## 784 19 10.0 5.0 1 3.1071 3.490 22 22 23 6 1 0
## 785 19 0.0 3.0 0 2.6800 3.171 19 25 22 7 1 1
## 786 19 0.0 10.0 1 2.7166 3.111 19 24 23 8 0 1
## 787 19 0.0 5.0 1 2.5178 2.600 23 25 24 7 1 1
## 788 19 0.0 20.0 1 2.7321 3.170 24 26 25 7 1 1
## 789 18 0.0 17.5 1 3.1551 2.850 NA NA NA 9 1 1
## 790 19 12.0 20.0 1 3.0892 3.629 NA NA NA 8 1 1
## 791 19 0.0 22.5 1 2.4285 3.446 15 24 21 7 1 1
## 792 19 0.0 25.0 0 3.8103 3.680 22 27 24 8 1 1
## 793 19 0.0 6.0 1 2.2500 3.292 22 21 22 8 0 0
## 794 19 0.0 10.0 1 2.8653 3.483 24 24 25 9 1 1
## 795 19 0.0 9.0 1 2.8076 3.250 21 19 20 6 0 1
## 796 19 21.0 10.0 0 3.3448 3.467 28 22 25 6 0 1
## 797 19 8.0 10.0 1 3.4814 3.660 27 29 28 9 0 1
## 798 18 12.0 8.0 0 2.5769 3.405 23 27 24 8 0 1
## 799 19 15.0 5.0 0 2.8333 3.115 24 25 25 5 1 0
## 800 19 0.0 15.0 0 3.1153 3.457 24 24 24 7 1 0
## 801 19 0.0 13.5 0 3.4833 3.439 19 24 22 6 0 1
## 802 19 15.0 10.0 0 3.6911 3.597 25 26 28 9 0 1
## 803 20 0.0 16.0 1 2.9333 3.294 20 24 23 9 1 0
## 804 20 6.0 10.0 1 2.8333 3.550 20 26 20 9 1 1
## 805 19 17.5 20.0 1 2.8035 2.900 27 19 23 9 0 1
## 806 19 0.0 5.0 0 2.7678 3.305 20 25 21 9 1 1
## 807 19 0.0 10.0 0 2.3928 3.586 29 29 26 8 1 1
## 808 19 0.0 7.0 0 3.0714 3.278 22 22 24 6 1 0
## 809 19 10.0 25.0 1 2.6166 3.390 21 21 23 5 0 0
## 810 19 0.0 10.0 0 2.7500 3.114 19 21 20 8 0 1
## 811 19 0.0 17.5 0 2.1206 3.542 24 27 26 9 1 1
## 812 19 10.0 10.0 1 3.0200 3.650 27 25 27 7 0 0
## 813 18 0.0 8.0 1 3.2291 3.230 18 23 21 6 0 1
## 814 18 10.0 8.0 0 3.0172 3.388 26 18 22 8 0 1
## 815 19 10.0 25.0 0 1.7000 2.994 20 27 21 10 1 1
## 816 19 0.0 14.0 0 2.4400 3.178 17 17 20 7 1 0
## 817 19 0.0 4.0 1 3.0555 3.050 25 28 29 7 1 1
## 818 19 15.0 16.0 0 2.4800 3.025 16 21 20 9 1 1
## 819 19 8.0 11.0 0 1.9259 3.583 13 19 18 8 1 1
## 820 19 12.0 10.0 0 2.6200 3.117 19 17 19 7 0 0
## 821 19 22.5 0.0 1 3.0892 3.790 NA NA NA 9 0 1
## 822 19 20.0 10.0 1 2.0000 3.270 27 25 25 8 0 0
## 823 19 17.5 17.5 0 1.4615 3.170 23 21 22 7 0 0
## 824 19 0.0 25.0 0 2.3750 3.000 25 24 24 8 1 1
## 825 19 25.0 12.0 0 2.5000 3.591 22 21 25 9 0 1
## 826 19 25.0 20.0 0 2.7758 3.550 26 24 27 10 0 0
## 827 19 0.0 40.0 1 3.3636 3.162 22 17 21 6 1 0
## 828 19 0.0 15.0 1 2.8000 2.583 23 24 23 8 1 0
## 829 18 12.0 12.5 1 2.9642 3.000 NA NA NA 7 1 0
## 830 19 0.0 5.0 0 2.6800 3.222 19 19 23 7 1 0
## 831 19 0.0 7.0 1 3.1666 3.200 16 25 19 4 0 1
## 832 20 0.0 12.5 1 3.0925 3.306 22 24 22 7 1 1
## 833 19 0.0 14.0 0 2.9166 3.151 20 17 18 6 0 0
## 834 19 12.0 6.0 0 1.8478 3.586 24 30 25 8 1 1
## 835 19 0.0 25.0 0 3.5161 3.105 NA NA NA 7 1 1
## 836 19 0.0 14.0 1 2.5833 3.187 24 22 21 8 1 1
## 837 19 10.0 9.0 0 3.2777 3.600 26 28 26 9 0 1
## 838 19 0.0 15.0 1 3.6290 4.005 21 26 24 10 0 1
## 839 19 9.5 15.0 0 3.1206 3.604 24 23 23 10 0 1
## 840 19 0.0 10.0 0 2.9655 3.463 25 30 26 9 0 1
## 841 19 11.0 15.0 0 2.9833 3.350 NA NA NA 7 0 1
## 842 19 18.0 10.0 0 3.3214 4.000 26 32 27 10 0 0
## 843 18 10.0 17.5 0 3.3214 3.411 22 27 24 10 1 1
## 844 19 0.0 8.0 1 3.5625 3.840 NA NA NA 9 1 1
## 845 19 0.0 20.0 0 3.1607 3.555 20 21 21 10 0 1
## 846 18 9.5 10.0 0 3.6607 3.130 NA NA NA 8 0 1
## 847 19 27.5 5.0 1 0.8750 3.160 21 26 23 9 0 0
## 848 19 20.0 15.0 1 3.0535 3.331 24 27 23 6 1 1
## 849 19 15.0 20.0 1 2.4285 3.009 26 21 28 6 0 1
## 850 19 6.0 7.0 0 2.3333 2.742 19 15 17 4 1 0
## 851 19 0.0 15.0 1 1.9482 3.318 19 27 24 9 1 1
## 852 19 10.0 9.0 0 3.2115 3.000 20 19 21 5 1 0
## 853 19 20.0 5.0 1 2.3888 3.071 23 23 22 10 1 1
## 854 19 20.0 21.5 1 3.1785 2.971 22 27 22 10 0 1
## 855 19 0.0 10.0 1 2.5892 3.861 21 19 21 9 0 0
## 856 19 0.0 15.0 1 3.7258 3.120 26 27 28 10 1 1
## attexc attgood fathcoll mothcoll score
## 1 0 0 1 1 84.43
## 2 0 0 0 1 57.38
## 3 1 0 0 1 66.39
## 4 0 1 1 1 81.15
## 5 0 1 0 1 95.90
## 6 1 0 0 1 83.61
## 7 0 1 0 0 76.23
## 8 1 0 1 1 84.43
## 9 1 0 0 1 79.51
## 10 0 1 0 0 46.72
## 11 0 1 0 0 83.61
## 12 0 0 0 1 71.31
## 13 1 0 0 0 79.51
## 14 0 1 1 1 59.02
## 15 0 1 0 1 65.57
## 16 1 0 1 1 83.61
## 17 0 1 0 0 54.92
## 18 0 1 1 1 77.87
## 19 0 0 1 1 67.21
## 20 0 1 0 1 74.59
## 21 0 1 1 1 40.98
## 22 0 1 1 0 69.67
## 23 1 0 0 0 75.41
## 24 0 1 0 0 69.67
## 25 0 1 1 1 67.21
## 26 0 1 1 0 50.00
## 27 1 0 1 1 92.62
## 28 0 1 0 1 72.95
## 29 0 0 0 0 86.07
## 30 1 0 1 1 88.52
## 31 0 1 1 1 86.89
## 32 0 1 1 1 45.90
## 33 1 0 0 1 86.89
## 34 0 1 1 1 89.34
## 35 1 0 0 0 54.10
## 36 0 1 0 0 62.30
## 37 0 1 0 0 60.66
## 38 0 1 1 1 60.66
## 39 0 1 0 0 62.30
## 40 0 1 1 0 73.77
## 41 0 1 0 1 76.23
## 42 0 0 0 0 82.79
## 43 0 1 0 0 77.05
## 44 1 0 1 1 63.11
## 45 0 1 1 1 86.07
## 46 0 1 1 0 85.25
## 47 0 1 0 0 59.84
## 48 0 1 1 1 67.21
## 49 0 1 0 0 28.69
## 50 1 0 1 0 77.05
## 51 0 1 0 0 85.25
## 52 0 1 1 1 76.23
## 53 1 0 1 1 97.54
## 54 1 0 0 1 70.49
## 55 0 1 1 1 60.66
## 56 0 1 0 0 41.80
## 57 0 1 0 0 68.85
## 58 1 0 1 1 41.80
## 59 0 1 1 1 62.30
## 60 1 0 0 1 85.25
## 61 1 0 1 0 49.18
## 62 0 1 0 0 71.31
## 63 1 0 0 0 90.16
## 64 0 1 0 1 73.77
## 65 0 1 1 1 53.28
## 66 0 1 0 1 63.93
## 67 0 1 1 1 92.62
## 68 1 0 0 0 78.69
## 69 1 0 0 0 62.30
## 70 0 1 1 1 94.26
## 71 0 1 0 0 71.31
## 72 0 1 1 1 88.52
## 73 0 1 1 0 88.52
## 74 0 1 0 0 65.57
## 75 0 1 1 1 54.92
## 76 0 1 1 0 59.02
## 77 0 1 0 1 73.77
## 78 0 1 1 1 66.39
## 79 1 0 1 1 68.85
## 80 0 1 1 1 81.15
## 81 1 0 0 1 79.51
## 82 0 1 1 1 63.93
## 83 0 0 0 0 58.20
## 84 1 0 1 0 66.39
## 85 0 1 1 1 63.93
## 86 0 1 1 1 80.33
## 87 1 0 1 1 58.20
## 88 0 0 0 1 54.10
## 89 0 1 1 1 86.07
## 90 1 0 0 1 60.66
## 91 1 0 1 1 72.95
## 92 0 0 0 0 78.69
## 93 1 0 1 1 84.43
## 94 1 0 1 1 86.89
## 95 1 0 1 1 74.59
## 96 1 0 0 0 68.85
## 97 0 1 0 0 68.85
## 98 1 0 0 0 84.43
## 99 1 0 0 0 44.26
## 100 0 0 1 1 63.11
## 101 0 1 0 1 70.49
## 102 0 1 0 1 82.79
## 103 0 0 0 1 65.57
## 104 0 0 1 1 63.11
## 105 0 1 1 1 75.41
## 106 1 0 0 1 74.59
## 107 1 0 0 0 67.21
## 108 0 1 0 0 65.57
## 109 1 0 1 1 48.36
## 110 1 0 0 1 55.74
## 111 0 1 0 1 54.10
## 112 0 1 1 1 79.51
## 113 0 1 1 1 65.57
## 114 0 1 0 0 77.05
## 115 0 1 1 0 61.48
## 116 0 0 1 0 74.59
## 117 0 1 0 1 88.52
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## 841 0 1 1 1 82.03
## 842 1 0 1 1 68.75
## 843 1 0 1 1 89.84
## 844 0 1 0 1 91.41
## 845 0 1 0 1 78.91
## 846 1 0 0 1 94.53
## 847 0 1 1 0 41.41
## 848 0 1 1 1 81.25
## 849 1 0 0 0 67.97
## 850 1 0 1 1 38.28
## 851 0 1 0 0 75.00
## 852 0 1 1 0 79.69
## 853 0 1 0 0 78.13
## 854 0 1 1 0 68.75
## 855 0 1 1 0 77.34
## 856 0 1 0 0 91.41max(econmath$score)## [1] 98.44min(econmath$score)## [1] 19.53The minimum is 0% and maximum 100% The dataset has minimum at 19.53 and maximum at 98.44
fit.c6 <- lm(score~colgpa+actmth+acteng, data = econmath)
summary((fit.c6))##
## Call:
## lm(formula = score ~ colgpa + actmth + acteng, data = econmath)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.855 -6.215 0.444 6.812 32.670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.17402 2.80044 5.776 1.09e-08 ***
## colgpa 12.36620 0.71506 17.294 < 2e-16 ***
## actmth 0.88335 0.11220 7.873 1.11e-14 ***
## acteng 0.05176 0.11106 0.466 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.35 on 810 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.3972, Adjusted R-squared: 0.395
## F-statistic: 177.9 on 3 and 810 DF, p-value: < 2.2e-16In this case the distribution is not following the normality so we can not use the assumption 6. We would have wrong estimations for B3 in this case.
0.05176/0.111## [1] 0.4663063By showing that assumptions 1 to 5 holds we can approximate the value for the T distribution. As we know as N increases the distribution will follow a Normal distribution, so we can still obtain a consistent estimate.