1. Chapter 8 – Linear Regression

2. Total Fat vs. Protein at Burger King
From: Intro Stats, De Veaux, Pearson

3. Residual: observed value – predicted value
From: Intro Stats, De Veaux, Pearson

4. “Best Fit” line = Least Squares
Algebraic linear model: y = mx + b
Statistics linear model:
b1 is slope
b0 is intercept

5. Burger King model
Slope of 0.97: for every gram of protein, fat goes up 0.97 grams
Intercept of 6.8: Even items with no protein will have 6.8 grams of fat as an estimate 

6. Least Squares Line So where did those values come from? Slope:
Intercept:

7. Residuals again
Remember that the residual represents how far the data was from its predicted value
Residual = Data – Model
Scatterplot of residuals vs. x-values should have no pattern