It is easy to include interaction terms in a linear model using the `lm()`

function.
The syntax `lstat:black`

tells R to include an interaction term between
`lstat`

and `black`

. The syntax `lstat*age`

simultaneously includes `lstat`

, `age`

,
and the interaction term `lstat×age`

as predictors; it is a shorthand for
`lstat+age+lstat:age`

.
Note that when including an interaction effect, the main effects should also be in the model.

```
> summary(lm(medv ~ lstat * age, data = Boston))
Call:
lm(formula = medv ~ lstat * age, data = Boston)
Residuals:
Min 1Q Median 3Q Max
-15.806 -4.045 -1.333 2.085 27.552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.0885359 1.4698355 24.553 < 2e-16 ***
lstat -1.3921168 0.1674555 -8.313 8.78e-16 ***
age -0.0007209 0.0198792 -0.036 0.9711
lstat:age 0.0041560 0.0018518 2.244 0.0252 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.149 on 502 degrees of freedom
Multiple R-squared: 0.5557, Adjusted R-squared: 0.5531
F-statistic: 209.3 on 3 and 502 DF, p-value: < 2.2e-16
```

Try creating a model with `medv`

as dependent variable and the interaction between `lstat`

and `rm`

.
Make sure the main effects (`lstat`

and `rm`

) are also part of your model, but not the other predictors.

Assume that:

- The
`MASS`

library has been loaded - The
`Boston`

dataset has been loaded and attached