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

    • (1 p) plot with regression line,
    • (1 p) label axes and title.
  2. 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.
  3. (1 p) Interpret the intercept. Does it make sense in the context of your study?

  4. 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).
  5. 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.