kid.score mom.hs mom.iq
1      91.18      0 121.50
2      70.00      0 102.00
3      80.00      0 107.00
4      93.31      1 110.00
5      98.00      1 120.00
6      99.00      1 123.00
7      92.88      1 126.24
8      95.87      1 114.56
9      93.73      1 100.00
10     80.70      0 108.72
11     95.44      1 128.00
12     92.03      1 110.00
13     90.00      0 129.00
14     95.01      1 120.40
15     94.59      1  97.04
16     92.45      0 120.00
17     88.00      0 100.00
18     83.50      0  99.00
19     98.00      1 112.00
20     86.50      0 100.20
21     90.75      0 102.88
22     91.60      0 104.20
23     94.16      1 120.40
24     96.40      1 122.40

（一）做简单回归曲线检验母亲智商、学历与学生成绩的关系

#读入data
data1 <- read.csv("chapter 3 data1.csv")
kid.score <- data1$kid.score
mom.hs <- data1$mom.hs
mom.iq <- data1$mom.iq
#拟合有两个变量的回归模型
fit.3 <- lm (kid.score ~ mom.hs + mom.iq)
summary(fit.3)#观察模型主要参数
#拟合一个变量的回归模型（单个变量制作平面可视图）

结果：

Call:
lm(formula = kid.score ~ mom.hs + mom.iq)

Residuals:
     Min       1Q   Median       3Q      Max 
-16.9100  -2.9430   0.7737   4.6099   8.9200 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) 75.74834   31.49762   2.405   0.0255 *
mom.hs       0.14823    0.18918   0.784   0.4420  
mom.iq      -0.07502    0.05608  -1.338   0.1953  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.734 on 21 degrees of freedom
Multiple R-squared:  0.09141,	Adjusted R-squared:  0.004876 
F-statistic: 1.056 on 2 and 21 DF,  p-value: 0.3655

结果fit.3不显著

（二）母亲智商与学生成绩关系的回归散点图（单变量可视化）

fit.2 <- lm (kid.score ~ mom.iq)
plot (mom.iq, kid.score, xlab="Mother IQ score", ylab="Child test score")
curve (coef(fit.2)[1] + coef(fit.2)[2]*x, add=TRUE)#绘制fit.2的回归直线
#curve (cbind(1,x) %*% coef(fit.2), add=TRUE)也可以