Ödev 2
komhıdı Crecelle
26/10/2022
R Markdown
Örnek3.1 Kolej Gpa’sının Belirleyicileri
library(wooldridge)
data(gpa1)library(rmarkdown)
paged_table(gpa1)coklureg <- lm(colGPA~ hsGPA+ACT,data = gpa1)
summary(coklureg)##
## 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-06basitreg <- lm(colGPA~ACT,data = gpa1)
summary(basitreg)##
## Call:
## lm(formula = colGPA ~ ACT, data = gpa1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85251 -0.25251 -0.04426 0.26400 0.89336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.40298 0.26420 9.095 8.8e-16 ***
## ACT 0.02706 0.01086 2.491 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3656 on 139 degrees of freedom
## Multiple R-squared: 0.04275, Adjusted R-squared: 0.03586
## F-statistic: 6.207 on 1 and 139 DF, p-value: 0.0139plot(gpa1$ACT,gpa1$colGPA)
abline(basitreg)
Loading required package: ggplot2
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
##install.packages("stargazer")
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerstargazer(list(coklureg,basitreg),type = "text")##
## =================================================================
## Dependent variable:
## ---------------------------------------------
## colGPA
## (1) (2)
## -----------------------------------------------------------------
## hsGPA 0.453***
## (0.096)
##
## ACT 0.009 0.027**
## (0.011) (0.011)
##
## Constant 1.286*** 2.403***
## (0.341) (0.264)
##
## -----------------------------------------------------------------
## Observations 141 141
## R2 0.176 0.043
## Adjusted R2 0.164 0.036
## Residual Std. Error 0.340 (df = 138) 0.366 (df = 139)
## F Statistic 14.781*** (df = 2; 138) 6.207** (df = 1; 139)
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01sapmareg<-lm(hsGPA~ACT,data = gpa1)
stargazer(list(sapmareg),type = "text")##
## ===============================================
## Dependent variable:
## ---------------------------
## hsGPA
## -----------------------------------------------
## ACT 0.039***
## (0.009)
##
## Constant 2.463***
## (0.218)
##
## -----------------------------------------------
## Observations 141
## R2 0.120
## Adjusted R2 0.113
## Residual Std. Error 0.301 (df = 139)
## F Statistic 18.879*** (df = 1; 139)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01standarthata<-summary(coklureg)$sigma
standarthata## [1] 0.3403158sigmakare<-standarthata^2
sigmakare## [1] 0.1158148Rjkare<-summary(sapmareg)$r.squared
Rjkare## [1] 0.1195815VIF<-1/(1-Rjkare)
VIF## [1] 1.135823n<-nobs(coklureg)
n## [1] 141varxj<-var(gpa1$hsGPA)*(n-1)/n
varxj## [1] 0.1016267varbetaj<-(1/n)*(sigmakare/varxj)*VIF
varbetaj## [1] 0.009180115sdbetaj<-varbetaj^0.5
sdbetaj## [1] 0.09581292require(car)## Le chargement a nécessité le package : car## Le chargement a nécessité le package : carData## Warning: le package 'carData' a été compilé avec la version R 4.1.3vif(coklureg)## hsGPA ACT
## 1.135823 1.135823