This is the practices of examples in chapter 3 - Wooldridge

Ex3.1

setwd("/Users/vancam/Documents/WAIKATO-Thesis/Rworking/Wooldridge/StataFile")
library(foreign)
library(tidyverse)
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
rm(list = ls())
gpa1 <- read.dta("GPA1.DTA")
## Warning in read.dta("GPA1.DTA"): cannot read factor labels from Stata 5
## files
gpa1reg <- lm(data=gpa1,colGPA~hsGPA+ACT)
summary(gpa1reg)
## 
## Call:
## lm(formula = colGPA ~ hsGPA + ACT, data = gpa1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85442 -0.24666 -0.02614  0.28127  0.85357 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.286328   0.340822   3.774 0.000238 ***
## hsGPA       0.453456   0.095813   4.733 5.42e-06 ***
## ACT         0.009426   0.010777   0.875 0.383297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3403 on 138 degrees of freedom
## Multiple R-squared:  0.1764, Adjusted R-squared:  0.1645 
## F-statistic: 14.78 on 2 and 138 DF,  p-value: 1.526e-06

Ex3.2

wage1 <- read.dta("WAGE1.DTA")
## Warning in read.dta("WAGE1.DTA"): cannot read factor labels from Stata 5
## files
wage1reg <- lm(data = wage1, log(wage)~educ+exper+tenure)
summary(wage1reg)
## 
## Call:
## lm(formula = log(wage) ~ educ + exper + tenure, data = wage1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.05802 -0.29645 -0.03265  0.28788  1.42809 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.284360   0.104190   2.729  0.00656 ** 
## educ        0.092029   0.007330  12.555  < 2e-16 ***
## exper       0.004121   0.001723   2.391  0.01714 *  
## tenure      0.022067   0.003094   7.133 3.29e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4409 on 522 degrees of freedom
## Multiple R-squared:  0.316,  Adjusted R-squared:  0.3121 
## F-statistic: 80.39 on 3 and 522 DF,  p-value: < 2.2e-16

Ex3.3

K401 <- read.dta("401K.DTA")
## Warning in read.dta("401K.DTA"): cannot read factor labels from Stata 5
## files
k401reg <- lm(data = K401, prate~mrate+age)
summary(k401reg)
## 
## Call:
## lm(formula = prate ~ mrate + age, data = K401)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -81.162  -8.067   4.787  12.474  18.256 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  80.1191     0.7790  102.85  < 2e-16 ***
## mrate         5.5213     0.5259   10.50  < 2e-16 ***
## age           0.2432     0.0447    5.44 6.21e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.94 on 1531 degrees of freedom
## Multiple R-squared:  0.09225,    Adjusted R-squared:  0.09106 
## F-statistic: 77.79 on 2 and 1531 DF,  p-value: < 2.2e-16

Ex3.5

crime1 <- read.dta("CRIME1.DTA")
## Warning in read.dta("CRIME1.DTA"): cannot read factor labels from Stata 5
## files
crime1reg <- lm(data = crime1, narr86~pcnv+avgsen+ptime86+qemp86)
summary(crime1reg)
## 
## Call:
## lm(formula = narr86 ~ pcnv + avgsen + ptime86 + qemp86, data = crime1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9330 -0.4247 -0.2934  0.3506 11.4403 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.706756   0.033151  21.319  < 2e-16 ***
## pcnv        -0.150832   0.040858  -3.692 0.000227 ***
## avgsen       0.007443   0.004734   1.572 0.115993    
## ptime86     -0.037391   0.008794  -4.252 2.19e-05 ***
## qemp86      -0.103341   0.010396  -9.940  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8414 on 2720 degrees of freedom
## Multiple R-squared:  0.04219,    Adjusted R-squared:  0.04079 
## F-statistic: 29.96 on 4 and 2720 DF,  p-value: < 2.2e-16

Ex 3.6

wage1reg2 <- lm(data = wage1, log(wage)~educ)
summary(wage1reg2)
## 
## Call:
## lm(formula = log(wage) ~ educ, data = wage1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.21158 -0.36393 -0.07263  0.29712  1.52339 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.583773   0.097336   5.998 3.74e-09 ***
## educ        0.082744   0.007567  10.935  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4801 on 524 degrees of freedom
## Multiple R-squared:  0.1858, Adjusted R-squared:  0.1843 
## F-statistic: 119.6 on 1 and 524 DF,  p-value: < 2.2e-16