With your previous (or new) bivariate scatter plot, add a regression line.

- (1 p) plot with regression line,
- (1 p) label axes and title.

Use

`lm()`

to fit the linear regression and interpret slope and \(R^2\) (R-squared) values.- (1 p) lm
`summary()`

table is presented, - (2 p) slope is interpreted with respect to a per-unit increase of the \(x\) variable in the context of the variables in the plot,
- (1 p) \(R^2\) is interpretted in a sentence.

- (1 p) lm
(1 p) Interpret the intercept. Does it make sense in the context of your study?

Try plotting the data on a logarithmic scale (\(x\)-only, \(y\)-only, and both \(x\) and \(y\)). What happened to your data when you transformed it?

- (1 p) At least one logarithmic relationship is plotted, they indicate why they chose this transformation,
- (1 p) they describe what happened to the relationship (compare original scale to transformed scale).

Does your relationship benefit from a logarithmic transformation?

- (1 p) They say whether the relationship became more linear, and it is supported by their plots.