Healthy breakfast, sick data A regression model for data on breakfast cereals originally looked like this:

Let’s take a closer look at the coefficient for Fiber. Here’s the partial regression plot for Fiber in that regression model:

a) The line on the plot is the least squares line fit to this plot. What is its slope? (You may need to look back at the facts about partial regression plots in Chapter 28.)

b) One point is labeled as corresponding to Quaker Oatmeal.
What effect does this point have on the slope of the line?
(Does it make it larger, smaller, or have no effect at all?)
Here is the same regression with Quaker Oatmeal removed from the data:

c) Compare this regression with the previous one. In particular, which model is likely to make the best predictions of calories? Which seems to fit the data better?

d) How would you interpret the coefficient of Fiber in this model? Does Fiber contribute significantly to modeling calories? (In fact, the data for Quaker Oatmeal was determined to be in error and was corrected for the subsequent analyses seen elsewhere in this book.)

R-squared Dependent variable is: Calories 84.5% R-squared (adjusted) = 83.4% 7.947 with 77 671 degrees of freedom Source Sum of Squares DF Mean Square F-ratio Regression 24367.5 5 4873.50 77.2 Residual 4484.45 71 63.1613