时间序列分析（3）R语言-最基础的回归模型

rt<-read.table("exam0203.txt", head=TRUE); rt
lm.sol<-lm(Weight~Height, data=rt)
summary(lm.sol)

rt<-read.table("exam0203.txt", head=TRUE); rt
Name Sex Age Height Weight
1    Alice   F  13   56.5   84.0
2    Becka   F  13   65.3   98.0
3     Gail   F  14   64.3   90.0
4    Karen   F  12   56.3   77.0
5    Kathy   F  12   59.8   84.5
6     Mary   F  15   66.5  112.0
7    Sandy   F  11   51.3   50.5
8   Sharon   F  15   62.5  112.5
9    Tammy   F  14   62.8  102.5
10  Alfred   M  14   69.0  112.5
11    Duke   M  14   63.5  102.5
12   Guido   M  15   67.0  133.0
13   James   M  12   57.3   83.0
14 Jeffrey   M  13   62.5   84.0
15    John   M  12   59.0   99.5
16  Philip   M  16   72.0  150.0
17  Robert   M  12   64.8  128.0
18  Thomas   M  11   57.5   85.0
19 William   M  15   66.5  112.0

summary(lm.sol)

Call:
lm(formula = Weight ~ Height, data = rt)

Residuals:
Min       1Q   Median       3Q      Max
-17.6807  -6.0642   0.5115   9.2846  18.3698

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -143.0269    32.2746  -4.432 0.000366 ***
Height         3.8990     0.5161   7.555 7.89e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.23 on 17 degrees of freedom
Multiple R-squared:  0.7705,	Adjusted R-squared:  0.757
F-statistic: 57.08 on 1 and 17 DF,  p-value: 7.887e-07