计量经济学
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C5.1
（1）代码：mydata<-read.csv("C:\\Users\\x1nreborn\\Desktop\\新建文件夹\\伍德里奇计量经济学导论第四版数据总和\\excel 伍德里奇\\wage1.csv", header=F,sep=",",stringsAsFactors=FALSE)
wage<-mydata[,1:4]
names(wage)<-c("wage","educ","exper","tenure")
fit1<-lm(wage~educ+exper+tenure,data=wage)
fit1
summary(fit1)
residual<-resid(fit1)
hist(residual)
结果：Coefficients:(Intercept) educ exper tenure -2.87273 0.59897 0.02234 0.16927 Residuals:
Min 1Q Median 3Q Max
-7.6068 -1.7747 -0.6279 1.1969 14.6536
Histogram of residual
residual F r e q u e n c y
-505
10
15
05010015020
（2）代码：mydata<-read.csv("C:\\Users\\x1nreborn\\Desktop\\新建文件夹
\\伍德里奇计量经济学导论第四版数据总和\\excel 伍德里奇\\wage1.csv", header=F,sep=",",stringsAsFactors=FALSE)
wage<-mydata[,1:4]
names(wage)<-c("wage","educ","exper","tenure")
fit2<-lm(log(wage)~educ+exper+tenure,data=wage)
fit2
summary(fit2)
residual2<-resid(fit2)
hist(residual2)
结果：Coefficients:(Intercept) educ exper tenure 0.284360 0.092029 0.004121 0.022067 Residuals:
Min 1Q Median 3Q Max
-2.05802 -0.29645 -0.03265 0.28788 1.42809
Histogram of residual2
residual2F r e q u e n c y
-2-1
1
05010015020
（3）我认为对数—水平值模型更接近于满足假定MLR.6
C5.2
（1）代码：
mydata<-read.csv("C:\\Users\\x1nreborn\\Desktop\\新建文件夹\\伍德里奇计量经济学导论第四版数据总和\\excel伍德里奇\\gpa2.csv",
header=F,sep=",",stringsAsFactors=FALSE)
gpa2<-mydata[,c(1,3,8)]
names(gpa2)<-c("sat","colgpa","hsperc")
fit<-lm(colgpa~hsperc+sat,data=gpa2)
summary(fit)
结果：
Coefficients:
(Intercept) hsperc sat
1.391757 -0.013519 0.001476
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.392e+00 7.154e-02 19.45 <2e-16
hsperc -1.352e-02 5.495e-04 -24.60 <2e-16
sat 1.476e-03 6.531e-05 22.60 <2e-16
（2）代码：
gpa3<-gpa2[1:2070,]
fit1<-lm(colgpa~hsperc+sat,data=gpa3)
结果：
Coefficients:
(Intercept) hsperc sat
1.436017 -0.012749 0.001468
（3）c11=4.6 c12=0.0353 c13=0.0042
c21=4.4 c22=0.0327 c23=0.00403
所以c1i=c2i，可认为符合（5.10）中的等式。
C5.3
mydata<-read.csv("C:\\Users\\x1nreborn\\Desktop\\新建文件夹\\伍德里奇
计量经济学导论第四版数据总和\\excel伍德里奇\\bwght.csv",
header=F,sep=",",stringsAsFactors=FALSE)
bwght1<-mydata[,c(4,10,7,1,5,6)]
names(bwght1)<-c("bwght","cigs","parity","faminc","motheduc","fathedu c")
bwght1$motheduc[bwght1$motheduc=="."]<-NA
bwght1$fatheduc[bwght1$fatheduc=="."]<-NA
bwght2<-na.omit(bwght1)
fit<-lm(bwght~cigs+parity+faminc+motheduc+fatheduc,data=bwght2)
R<-residuals(fit)
bwght2$R<-R
fit1<-lm(R~cigs+parity+faminc+motheduc+fatheduc,data=bwght2)
结果显示，motheduc和fatheduc不联合显著。
C6.2
（1）代码：
mydata<-read.csv("C:\\Users\\x1nreborn\\Desktop\\新建文件夹\\伍德里奇计量经济学导论第四版数据总和\\excel伍德里奇\\wage1.csv",
header=F,sep=",",stringsAsFactors=FALSE)
wage<-mydata[,1:3]
names(wage)<-c("wage","educ","exper")
fit1<-lm(log(wage)~educ+exper+I(exper^2),data=wage)
结果：
Coefficients:
(Intercept) educ exper I(exper^2)
0.1279975 0.0903658 0.0410089 -0.0007136
Coefficients:
Estimate Std. Error t value Pr(>|t|)