Simple linear regression
Onto a substantially less trivial example; let's say No Scone Unturned has been keeping careful records of how many raisins (in grams) they have been using for their famous oatmeal raisin cookies. They want to construct a linear model describing the relationship between the area of a cookie (in centimeters squared) and how many raisins they use, on average.
In particular, they want to use linear regression to predict how many grams of raisins they will need for a 1-meter long oatmeal raisin cookie. Predicting a continuous variable (grams of raisins) from other variables sounds like a job for regression! In particular, when we use just a single predictor variable (the area of the cookies), the technique is called simple linear regression.
The left panel of Figure 9.2 illustrates the relationship between the area of cookies and the amount of raisins it used. It also shows the best-fit regression line:
Figure 9.2: A scatterplot of areas and grams of raisins in No Scone...
