Homework 3
Christian Paulo Aian Guanzon
March 27, 2020
From Lesson 3 - Multiple Regression
WAGES <- readXL("WAGES.xls")
WAGESstr(WAGES)'data.frame': 1289 obs. of 7 variables:
$ obs : num 1 2 3 4 5 6 7 8 9 10 ...
$ wage : num 11.6 5 12 7 21.1 ...
$ female : num 1 0 0 0 1 1 1 1 0 1 ...
$ nonwhite : num 0 0 0 1 1 0 0 1 0 0 ...
$ union : num 0 0 0 1 0 0 0 0 0 0 ...
$ education: num 12 9 16 14 16 12 12 12 18 18 ...
$ exper : num 20 9 15 38 19 4 14 32 7 5 ...summary(WAGES) obs wage female nonwhite union education exper
Min. : 1 Min. : 0.84 Min. :0.0000 Min. :0.0000 Min. :0.000 Min. : 0.00 Min. : 0.00
1st Qu.: 323 1st Qu.: 6.92 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:12.00 1st Qu.: 9.00
Median : 645 Median :10.08 Median :0.0000 Median :0.0000 Median :0.000 Median :12.00 Median :18.00
Mean : 645 Mean :12.37 Mean :0.4973 Mean :0.1528 Mean :0.159 Mean :13.15 Mean :18.79
3rd Qu.: 967 3rd Qu.:15.63 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:16.00 3rd Qu.:27.00
Max. :1289 Max. :64.08 Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :20.00 Max. :56.00 cor(WAGES) obs wage female nonwhite union education exper
obs 1.000000000 -0.05843877 0.006296486 -0.02566251 -0.002907478 0.003806248 -0.006655294
wage -0.058438769 1.00000000 -0.223301829 -0.12783381 0.102246655 0.456517979 0.173173303
female 0.006296486 -0.22330183 1.000000000 0.04327185 -0.088856935 -0.031439159 -0.022656813
nonwhite -0.025662507 -0.12783381 0.043271852 1.00000000 0.080587911 -0.087061729 -0.039129103
union -0.002907478 0.10224666 -0.088856935 0.08058791 1.000000000 0.003966952 0.154319024
education 0.003806248 0.45651798 -0.031439159 -0.08706173 0.003966952 1.000000000 -0.180103012
exper -0.006655294 0.17317330 -0.022656813 -0.03912910 0.154319024 -0.180103012 1.000000000WAGES.lm <- lm(wage~.,data=WAGES)
summary(WAGES.lm)
Call:
lm(formula = wage ~ ., data = WAGES)
Residuals:
Min 1Q Median 3Q Max
-20.622 -3.668 -1.001 2.609 50.493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.3673562 1.0617226 -5.997 0.00000000261 ***
obs -0.0012550 0.0004863 -2.581 0.00997 **
female -3.0679942 0.3638243 -8.433 < 2e-16 ***
nonwhite -1.5994861 0.5082405 -3.147 0.00169 **
union 1.0973070 0.5049656 2.173 0.02996 *
education 1.3703624 0.0657593 20.839 < 2e-16 ***
exper 0.1663016 0.0160127 10.386 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.494 on 1282 degrees of freedom
Multiple R-squared: 0.3268, Adjusted R-squared: 0.3237
F-statistic: 103.7 on 6 and 1282 DF, p-value: < 2.2e-16WAGES.lm2 <- lm(wage~.-obs,data=WAGES)
summary(WAGES.lm2)
Call:
lm(formula = wage ~ . - obs, data = WAGES)
Residuals:
Min 1Q Median 3Q Max
-20.781 -3.760 -1.044 2.418 50.414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.18334 1.01579 -7.072 0.00000000000251 ***
female -3.07488 0.36462 -8.433 < 2e-16 ***
nonwhite -1.56531 0.50919 -3.074 0.00216 **
union 1.09598 0.50608 2.166 0.03052 *
education 1.37030 0.06590 20.792 < 2e-16 ***
exper 0.16661 0.01605 10.382 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.508 on 1283 degrees of freedom
Multiple R-squared: 0.3233, Adjusted R-squared: 0.3207
F-statistic: 122.6 on 5 and 1283 DF, p-value: < 2.2e-16\[ wage = -7.18 -3.07female - 1.57nonwhite + 1.10union + 1.37education + 0.17exper + \epsilon\ \]
library(carData)
PrestigePRESTIGE <- PrestigePRESTIGE.lm <- lm(income~.,data=PRESTIGE)
summary(PRESTIGE.lm)
Call:
lm(formula = income ~ ., data = PRESTIGE)
Residuals:
Min 1Q Median 3Q Max
-7752.4 -954.6 -331.2 742.6 14301.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.32053 3037.27048 0.002 0.99808
education 131.18372 288.74961 0.454 0.65068
women -53.23480 9.83107 -5.415 0.000000496 ***
prestige 139.20912 36.40239 3.824 0.00024 ***
census 0.04209 0.23568 0.179 0.85865
typeprof 509.15150 1798.87914 0.283 0.77779
typewc 347.99010 1173.89384 0.296 0.76757
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2633 on 91 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.6363, Adjusted R-squared: 0.6123
F-statistic: 26.54 on 6 and 91 DF, p-value: < 2.2e-16\[ income = +7.32 + 131.18education - 53.23women + 139.21prestige + 0.04census + 509.15typeprof + 347.99typewc \]
From Lesson 4 - Regression Diagnostics
source("scripts/r4abep-01.R")
library(RcmdrMisc)
options(scipen = 10)
library(esquisse)
WOMEN <- women
WOMENstr(WOMEN)'data.frame': 15 obs. of 2 variables:
$ height: num 58 59 60 61 62 63 64 65 66 67 ...
$ weight: num 115 117 120 123 126 129 132 135 139 142 ...WOMEN.lm <- lm(height~weight,data=WOMEN)
summary(WOMEN.lm)
Call:
lm(formula = height ~ weight, data = WOMEN)
Residuals:
Min 1Q Median 3Q Max
-0.83233 -0.26249 0.08314 0.34353 0.49790
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.723456 1.043746 24.64 0.0000000000026848 ***
weight 0.287249 0.007588 37.85 0.0000000000000109 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.44 on 13 degrees of freedom
Multiple R-squared: 0.991, Adjusted R-squared: 0.9903
F-statistic: 1433 on 1 and 13 DF, p-value: 0.00000000000001091r4abep.plotlm(WOMEN.lm)
WOMEN.lm2 <- lm(height~weight+I(weight^2),data=WOMEN)
summary(WOMEN.lm2)
Call:
lm(formula = height ~ weight + I(weight^2), data = WOMEN)
Residuals:
Min 1Q Median 3Q Max
-0.105338 -0.035764 -0.004898 0.049430 0.141593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -11.74693860 1.71998084 -6.83 0.000018241147800 ***
weight 0.83434066 0.02502062 33.35 0.000000000000336 ***
I(weight^2) -0.00197330 0.00009014 -21.89 0.000000000048424 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07158 on 12 degrees of freedom
Multiple R-squared: 0.9998, Adjusted R-squared: 0.9997
F-statistic: 2.732e+04 on 2 and 12 DF, p-value: < 2.2e-16r4abep.plotlm(WOMEN.lm2)
WAGES01 <- readXL("WAGES01.xls")
WAGES01str(WAGES01)'data.frame': 1289 obs. of 8 variables:
$ obs : num 1 2 3 4 5 6 7 8 9 10 ...
$ wage : num 11.6 5 12 7 21.1 ...
$ female : num 1 0 0 0 1 1 1 1 0 1 ...
$ nonwhite : num 0 0 0 1 1 0 0 1 0 0 ...
$ union : num 0 0 0 1 0 0 0 0 0 0 ...
$ education: num 12 9 16 14 16 12 12 12 18 18 ...
$ exper : num 20 9 15 38 19 4 14 32 7 5 ...
$ age : num 38 24 37 58 41 22 32 50 31 29 ...WAGES01.lm <- lm(wage~.,data=WAGES01)
summary(WAGES01.lm)
Call:
lm(formula = wage ~ ., data = WAGES01)
Residuals:
Min 1Q Median 3Q Max
-20.622 -3.668 -1.001 2.609 50.493
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.3673562 1.0617226 -5.997 0.00000000261 ***
obs -0.0012550 0.0004863 -2.581 0.00997 **
female -3.0679942 0.3638243 -8.433 < 2e-16 ***
nonwhite -1.5994861 0.5082405 -3.147 0.00169 **
union 1.0973070 0.5049656 2.173 0.02996 *
education 1.3703624 0.0657593 20.839 < 2e-16 ***
exper 0.1663016 0.0160127 10.386 < 2e-16 ***
age NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.494 on 1282 degrees of freedom
Multiple R-squared: 0.3268, Adjusted R-squared: 0.3237
F-statistic: 103.7 on 6 and 1282 DF, p-value: < 2.2e-16r4abep.cor(WAGES01)
r4abep.corgram(WAGES01)
WAGES01.lm2 <- lm(wage~.-exper,data=WAGES01)
summary(WAGES01.lm2)
Call:
lm(formula = wage ~ . - exper, data = WAGES01)
Residuals:
Min 1Q Median 3Q Max
-20.622 -3.668 -1.001 2.609 50.493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.3651657 1.1068167 -6.654 0.000000000042 ***
obs -0.0012550 0.0004863 -2.581 0.00997 **
female -3.0679942 0.3638243 -8.433 < 2e-16 ***
nonwhite -1.5994861 0.5082405 -3.147 0.00169 **
union 1.0973070 0.5049656 2.173 0.02996 *
education 1.2040609 0.0646781 18.616 < 2e-16 ***
age 0.1663016 0.0160127 10.386 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.494 on 1282 degrees of freedom
Multiple R-squared: 0.3268, Adjusted R-squared: 0.3237
F-statistic: 103.7 on 6 and 1282 DF, p-value: < 2.2e-16WWORK <- readXL("WWORK.xls")
WWORKstr(WWORK)'data.frame': 753 obs. of 25 variables:
$ taxableinc : num 12200 18000 24000 16400 10000 ...
$ federaltax : num 1494 2615 3957 2279 1063 ...
$ hsiblings : num 1 8 4 6 3 8 0 2 6 5 ...
$ hfathereduc : num 14 7 7 7 7 7 7 7 7 7 ...
$ hmothereduc : num 16 3 10 12 7 7 10 7 7 7 ...
$ siblings : num 4 0 2 5 7 4 8 7 7 0 ...
$ lfp : num 1 1 1 1 1 1 1 1 1 1 ...
$ hours : num 1610 1656 1980 456 1568 ...
$ kidsl6 : num 1 0 1 0 1 0 0 0 0 0 ...
$ kids618 : num 0 2 3 3 2 0 2 0 2 2 ...
$ age : num 32 30 35 34 31 54 37 54 48 39 ...
$ educ : num 12 12 12 12 14 12 16 12 12 12 ...
$ wage : num 3.35 1.39 4.55 1.1 4.59 ...
$ wage76 : num 2.65 2.65 4.04 3.25 3.6 ...
$ hhours : num 2708 2310 3072 1920 2000 ...
$ hage : num 34 30 40 53 32 57 37 53 52 43 ...
$ heduc : num 12 9 12 10 12 11 12 8 4 12 ...
$ hwage : num 4.03 8.44 3.58 3.54 10 ...
$ faminc : num 16310 21800 21040 7300 27300 ...
$ mtr : num 0.721 0.661 0.692 0.781 0.622 ...
$ mothereduc : num 12 7 12 7 12 14 14 3 7 7 ...
$ fathereduc : num 7 7 7 7 14 7 7 3 7 7 ...
$ unemployment: num 5 11 5 5 9.5 7.5 5 5 3 5 ...
$ largecity : num 0 1 0 0 1 1 0 0 0 0 ...
$ exper : num 14 5 15 6 7 33 11 35 24 21 ...WWORK.lm <- lm(hours~.,data=WWORK)
summary(WWORK.lm)
Call:
lm(formula = hours ~ ., data = WWORK)
Residuals:
Min 1Q Median 3Q Max
-1796.4 -256.0 20.2 242.0 3301.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3073.596550 546.055531 5.629 0.0000000259188942 ***
taxableinc -0.004201 0.006432 -0.653 0.513867
federaltax 0.017394 0.021555 0.807 0.419945
hsiblings 0.826868 8.324151 0.099 0.920901
hfathereduc 13.421522 6.676375 2.010 0.044768 *
hmothereduc -3.553929 6.490500 -0.548 0.584163
siblings -19.884246 8.384105 -2.372 0.017968 *
lfp 1047.764693 56.560129 18.525 < 2e-16 ***
kidsl6 -109.730258 41.785588 -2.626 0.008820 **
kids618 -26.444051 16.443507 -1.608 0.108230
age -8.651555 5.299301 -1.633 0.102989
educ -14.854387 11.668634 -1.273 0.203418
wage -61.453206 8.377708 -7.335 0.0000000000005912 ***
wage76 84.292944 11.414349 7.385 0.0000000000004187 ***
hhours -0.190040 0.039520 -4.809 0.0000018464411482 ***
hage -2.747531 5.091228 -0.540 0.589597
heduc -8.532048 8.436929 -1.011 0.312221
hwage -67.284656 8.623613 -7.802 0.0000000000000211 ***
faminc 0.013336 0.003550 3.757 0.000186 ***
mtr -2287.788894 553.299716 -4.135 0.0000396638887059 ***
mothereduc 6.374116 7.013803 0.909 0.363759
fathereduc -6.989771 6.674763 -1.047 0.295358
unemployment -5.834074 6.135897 -0.951 0.342016
largecity -14.959321 42.065584 -0.356 0.722229
exper 14.926883 2.780136 5.369 0.0000001065061420 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 502.6 on 728 degrees of freedom
Multiple R-squared: 0.6779, Adjusted R-squared: 0.6673
F-statistic: 63.84 on 24 and 728 DF, p-value: < 2.2e-16r4abep.vif(WWORK.lm)
VIF
----------
taxableinc federaltax hsiblings hfathereduc hmothereduc siblings lfp kidsl6 kids618
17.415361 16.727126 1.195571 1.373546 1.411905 1.119400 2.339501 1.427039 1.402303
age educ wage wage76 hhours hage heduc hwage faminc
5.448154 2.107616 2.195931 2.271331 1.649221 5.011564 1.933758 3.962434 5.573780
mtr mothereduc fathereduc unemployment largecity exper
6.353831 1.660743 1.692596 1.087531 1.211230 1.498213
sqrt(VIF)
----------
taxableinc federaltax hsiblings hfathereduc hmothereduc siblings lfp kidsl6 kids618
4.173172 4.089881 1.093422 1.171984 1.188236 1.058017 1.529543 1.194587 1.184189
age educ wage wage76 hhours hage heduc hwage faminc
2.334128 1.451763 1.481867 1.507094 1.284220 2.238652 1.390596 1.990586 2.360885
mtr mothereduc fathereduc unemployment largecity exper
2.520681 1.288698 1.300998 1.042848 1.100559 1.224015 r4abep.cor(WWORK)
WWORK.lm2 <- lm(hours~.-federaltax-mtr-hage,data=WWORK)
summary(WWORK.lm2)
Call:
lm(formula = hours ~ . - federaltax - mtr - hage, data = WWORK)
Residuals:
Min 1Q Median 3Q Max
-1647.3 -258.5 33.2 247.8 3300.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 979.0047031 235.9604277 4.149 0.000037316929218 ***
taxableinc 0.0007106 0.0016886 0.421 0.674017
hsiblings 0.6821181 8.3728001 0.081 0.935092
hfathereduc 14.3764575 6.7304325 2.136 0.033008 *
hmothereduc -4.1091283 6.5460816 -0.628 0.530381
siblings -20.1553148 8.4314758 -2.390 0.017078 *
lfp 1074.2273986 56.2631759 19.093 < 2e-16 ***
kidsl6 -121.5513603 41.6330034 -2.920 0.003613 **
kids618 -46.2842866 15.8819067 -2.914 0.003674 **
age -11.4168114 3.0922474 -3.692 0.000239 ***
educ -14.0343383 11.7754944 -1.192 0.233716
wage -60.2581574 8.4578382 -7.125 0.000000000002507 ***
wage76 86.5249106 11.4407838 7.563 0.000000000000119 ***
hhours -0.1455273 0.0384263 -3.787 0.000165 ***
heduc -3.9710037 8.4261375 -0.471 0.637587
hwage -55.0234051 8.1559868 -6.746 0.000000000030839 ***
faminc 0.0230408 0.0027065 8.513 < 2e-16 ***
mothereduc 7.5093287 7.0723474 1.062 0.288683
fathereduc -7.1984887 6.7124572 -1.072 0.283891
unemployment -6.1673995 6.1876121 -0.997 0.319224
largecity -15.6636225 42.3743650 -0.370 0.711751
exper 15.4177309 2.8024640 5.501 0.000000052117188 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 507.8 on 731 degrees of freedom
Multiple R-squared: 0.6699, Adjusted R-squared: 0.6604
F-statistic: 70.64 on 21 and 731 DF, p-value: < 2.2e-16r4abep.vif(WWORK.lm2)
VIF
----------
taxableinc hsiblings hfathereduc hmothereduc siblings lfp kidsl6 kids618 age
1.176082 1.185098 1.367618 1.407113 1.109165 2.268130 1.387955 1.281667 1.817514
educ wage wage76 hhours heduc hwage faminc mothereduc fathereduc
2.102940 2.192826 2.235665 1.527646 1.889763 3.472591 3.175060 1.654396 1.677110
unemployment largecity exper
1.083550 1.204194 1.491553
sqrt(VIF)
----------
taxableinc hsiblings hfathereduc hmothereduc siblings lfp kidsl6 kids618 age
1.084473 1.088622 1.169452 1.186218 1.053169 1.506031 1.178115 1.132107 1.348152
educ wage wage76 hhours heduc hwage faminc mothereduc fathereduc
1.450152 1.480819 1.495214 1.235980 1.374687 1.863489 1.781870 1.286233 1.295033
unemployment largecity exper
1.040937 1.097358 1.221291 WWORK.lm3 <- lm(hours~hfathereduc+siblings+lfp+kidsl6+kids618+age+wage+wage76+hhours+hwage+faminc+exper,data=WWORK)
summary(WWORK.lm3)
Call:
lm(formula = hours ~ hfathereduc + siblings + lfp + kidsl6 +
kids618 + age + wage + wage76 + hhours + hwage + faminc +
exper, data = WWORK)
Residuals:
Min 1Q Median 3Q Max
-1601.7 -276.4 23.8 250.5 3309.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 750.282079 190.823943 3.932 0.000092228000441 ***
hfathereduc 13.030009 5.875155 2.218 0.026870 *
siblings -20.196460 8.170389 -2.472 0.013663 *
lfp 1074.146421 55.503429 19.353 < 2e-16 ***
kidsl6 -130.131720 41.192157 -3.159 0.001647 **
kids618 -43.578325 15.665493 -2.782 0.005543 **
age -11.249071 2.995039 -3.756 0.000186 ***
wage -62.726300 8.279287 -7.576 0.000000000000106 ***
wage76 84.176015 11.301802 7.448 0.000000000000264 ***
hhours -0.154220 0.037320 -4.132 0.000040012161607 ***
hwage -59.137627 7.681320 -7.699 0.000000000000044 ***
faminc 0.022742 0.002684 8.472 < 2e-16 ***
exper 15.360228 2.774562 5.536 0.000000042973445 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 507.3 on 740 degrees of freedom
Multiple R-squared: 0.6664, Adjusted R-squared: 0.661
F-statistic: 123.2 on 12 and 740 DF, p-value: < 2.2e-16r4abep.vif(WWORK.lm3)
VIF
----------
hfathereduc siblings lfp kidsl6 kids618 age wage wage76 hhours
1.043866 1.043282 2.210987 1.360993 1.249066 1.707895 2.104740 2.185333 1.443380
hwage faminc exper
3.085314 3.128623 1.464450
sqrt(VIF)
----------
hfathereduc siblings lfp kidsl6 kids618 age wage wage76 hhours
1.021697 1.021412 1.486939 1.166616 1.117616 1.306865 1.450772 1.478287 1.201408
hwage faminc exper
1.756506 1.768791 1.210145 ABORT <- readXL("ABORT.xls")
ABORTstr(ABORT)'data.frame': 50 obs. of 9 variables:
$ state : Factor w/ 50 levels "ALABAMA","ALASKA",..: 24 31 44 48 4 18 40 26 36 1 ...
$ abortion: num 12.4 17.7 9.3 7.7 13.5 ...
$ religion: num 38 44.7 76.7 9.8 30 ...
$ price : num 256 332 298 251 248 228 292 329 281 272 ...
$ laws : num 0 0 1 0 1 1 1 0 0 1 ...
$ funds : num 0 0 0 1 0 0 0 0 0 0 ...
$ educ : num 64.3 75.1 85.1 66 66.3 ...
$ income : num 14082 15458 15573 15598 15635 ...
$ picket : num 100 20 0 50 33 60 57 50 75 89 ...ABORT.lm <- lm(abortion~educ+funds+income+laws+picket+price+religion,data=ABORT)
summary(ABORT.lm)
Call:
lm(formula = abortion ~ educ + funds + income + laws + picket +
price + religion, data = ABORT)
Residuals:
Min 1Q Median 3Q Max
-11.6110 -4.6493 -0.6696 4.5253 15.9514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.2839573 15.0776294 0.947 0.3489
educ -0.2872551 0.1995545 -1.439 0.1574
funds 2.8200030 2.7834747 1.013 0.3168
income 0.0024007 0.0004552 5.274 0.00000435 ***
laws -0.8731018 2.3765662 -0.367 0.7152
picket -0.1168712 0.0421799 -2.771 0.0083 **
price -0.0423631 0.0222232 -1.906 0.0635 .
religion 0.0200709 0.0863805 0.232 0.8174
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.063 on 42 degrees of freedom
Multiple R-squared: 0.5774, Adjusted R-squared: 0.507
F-statistic: 8.199 on 7 and 42 DF, p-value: 0.000002847r4abep.plotlm(ABORT.lm)
r4abep.vif(ABORT.lm)
VIF
----------
educ funds income laws picket price religion
1.380153 1.416584 1.606933 1.304444 1.214623 1.153023 1.175216
sqrt(VIF)
----------
educ funds income laws picket price religion
1.174799 1.190204 1.267649 1.142122 1.102099 1.073789 1.084074 r4abep.bp(ABORT.lm)
studentized Breusch-Pagan test
data: model
BP = 16.001, df = 7, p-value = 0.02511ABORT.lm2 <- lm(log(abortion)~educ+funds+income+laws+picket+price+religion,data=ABORT)
summary(ABORT.lm2)
Call:
lm(formula = log(abortion) ~ educ + funds + income + laws + picket +
price + religion, data = ABORT)
Residuals:
Min 1Q Median 3Q Max
-0.79388 -0.23582 0.01907 0.27809 0.56150
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.8332642 0.7552629 3.751 0.000533 ***
educ -0.0144884 0.0099960 -1.449 0.154648
funds 0.0876877 0.1394288 0.629 0.532816
income 0.0001265 0.0000228 5.547 0.00000178 ***
laws -0.0128839 0.1190461 -0.108 0.914332
picket -0.0065153 0.0021129 -3.084 0.003607 **
price -0.0031121 0.0011132 -2.796 0.007777 **
religion 0.0004575 0.0043269 0.106 0.916290
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3538 on 42 degrees of freedom
Multiple R-squared: 0.5892, Adjusted R-squared: 0.5207
F-statistic: 8.605 on 7 and 42 DF, p-value: 0.000001649r4abep.plotlm(ABORT.lm2)
r4abep.vif(ABORT.lm2)
VIF
----------
educ funds income laws picket price religion
1.380153 1.416584 1.606933 1.304444 1.214623 1.153023 1.175216
sqrt(VIF)
----------
educ funds income laws picket price religion
1.174799 1.190204 1.267649 1.142122 1.102099 1.073789 1.084074 r4abep.bp(ABORT.lm2)
studentized Breusch-Pagan test
data: model
BP = 7.95, df = 7, p-value = 0.337r4abep.ptxform(ABORT.lm)non-list contrasts argument ignoredEstimated transformation parameter
Y1
0.301365 r4abep.comparelm(ABORT.lm,ABORT.lm2)length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
============================================================
Dependent variable:
------------------------------
abortion log(abortion)
(1) (2)
------------------------------------------------------------
educ -0.287 -0.014
(0.200) (0.010)
funds 2.820 0.088
(2.783) (0.139)
income 0.002*** 0.0001***
(0.0005) (0.00002)
laws -0.873 -0.013
(2.377) (0.119)
picket -0.117** -0.007**
(0.042) (0.002)
price -0.042 -0.003**
(0.022) (0.001)
religion 0.020 0.0005
(0.086) (0.004)
Constant 14.284 2.833***
(15.078) (0.755)
------------------------------------------------------------
Observations 50 50
R2 0.577 0.589
Adjusted R2 0.507 0.521
Residual Std. Error (df = 42) 7.063 0.354
F Statistic (df = 7; 42) 8.199*** 8.605***
============================================================
Note: *p<0.05; **p<0.01; ***p<0.001mean(ABORT.lm2$residuals)[1] -2.77881e-18r4abep.biasint(ABORT.lm2)[1] -2.77881e-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