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Exercise

Predictive Power

In our experiment about how much people like you when you give them different amounts of money, we found the regression equation: \(liking = 1.501 + 0.778 * money\). We can look at how well the predictor (money) describes the response variable (liking) through looking at the R squared. This tells us how much of the variance in the response variable (liking) is explained by the predictor variable (money).

One way of finding the R squared is through squaring the correlation between the predictor and response variable. To find this you can use the function cor(), which takes your two variables, separated by a comma, as arguments. e.g. cor(variable1, variable2). Remember that you can square a value in R using ^2. For example, 3^2 would return 9.

However, another way of finding the R squared is using lm() and summary(). If we ask R to give us the summary of lm() we get quite a lot of extra information in addition to our regression coeffcients, including the R squared! To use the summary(), simply place the information you want to find information about between brackets. E.g. `summary(lm(variable1~variable2)).

Instructions

100 XP
  • In your script, calculate the R squared using the function cor().
  • In your script, add the summary() function to the lm() function you used earlier. Assign this to the object sum.
  • In your script, print sum.
  • Hit 'Submit' and compare the R squared values obtained by both of these methods!