problem-10.25
problem-10.25
We can fit the models using update():
> res.1 = lm(weight ~ age + height, data=kid.weights)
> res.2 = update(res.1, . ~ . + I(height^2))
> res.3 = update(res.2, . ~ . + I(height^3))
> res.4 = update(res.3, . ~ . + I(height^4))
> anova(res.1,res.2,res.3,res.4)
Analysis of Variance Table
Model 1: weight ~ age + height
Model 2: weight ~ age + height + I(height^2)
Model 3: weight ~ age + height + I(height^2) + I(height^3)
Model 4: weight ~ age + height + I(height^2) + I(height^3) + I(height^4)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 247 33408
2 246 30604 1 2803 25.4 9.1e-07 ***
3 245 27880 1 2724 24.7 1.3e-06 ***
4 244 26931 1 949 8.6 0.0037 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
The ANOVA table shows that for each nested model the new term is
statistically significant. The full model is the one selected by
this criteria.