Exercise

# Fitting linear models

If your future role involves building predictive models, the interviewer might be interested in testing your knowledge of **linear regression**.

Linear regression models are one of the basic forms of predicting values for linearly related data.
Linear regression model requires **normality** and **homoscedasticity** of the errors.
If you fit a linear regression model during the interview, ensure that these assumptions are met.

You are already familiar with the `cats`

dataset. The dataset is available in your environment.
To add a regression line to the plot, you can use `abline()`

applied on a linear model's object.

Instructions 1/3

**undefined XP**

- Draw the scatterplot of
`Bwt`

on the x-axis and`Hwt`

on the y-axis. - Fit a linear regression model explaining heart weight by body weight.
- Add the regression line to the scatterplot.