3. Estimate all possible linear models with two predictors and interaction. Use half your data. That is, regress `y` on the intercept only. Then regress `y` on `x1`. Then regress `y` on `x2`. Then on `x1` and `x2`. Then on `x1*x2`, then on `x1` and `x1*x2`, etc. This should result in 8 different models. All models have an intercept, but the first has only the intercept. Note: if you type `formula('y~X1*X2')` this will be expanded to `'y~X1+X2+X1*X2'`. You need to use `'y~I(X1*X2)'` to avoid this behavior.
Estimate all possible linear models with two predictors and interaction. Use half your data. That is, regress `y` on the intercept only. Then regress `y` on `x1`. Then regress `y` on `x2`. Then on `x1` and `x2`. Then on `x1*x2`, then on `x1` and `x1*x2`, etc. This should result in 8 different models. All models have an intercept, but the first has only the intercept. Note: if you type `formula('y~X1*X2')` this will be expanded to `'y~X1+X2+X1*X2'`. You need to use `'y~I(X1*X2)'` to avoid this behavior.
用两个预测因子和相互作用估计所有可能的线性模型。使用您的数据的一半。也就是说，只在截距上回归y。然后在x1上回归y。然后在x2上回归y。然后是x1和x2。然后在' x1*x2 '上，然后是' x1 '和' x1*x2 '等等，这将导致8个不同的模型。所有的模型都有一个截距，但第一个只有截距。注意:如果你输入“公式(y~X1*X2)”，那么它将被扩展到“y~X1+X2+X1*X2”。你需要使用“y~I(X1*X2)”来避免这种行为。
数据
#1.
```{r generate-function, echo=TRUE}
generate.lm |
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