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

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!

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