This book is in Open Review. I want your feedback to make the book better for you and other readers. To add your annotation, select some text and then click the on the pop-up menu. To see the annotations of others, click the button in the upper right hand corner of the page

11.3 Interpretation of parameters

Finally, we come to the discussion of parameters of a model. As mentioned earlier, each one of them represents the slope of the model. But there is more to the meaning of parameters of the model. Consider the coefficients of the previously estimated model:

coef(mtcarsModel02)

## (Intercept)         cyl        disp          hp        drat          wt 
## 17.88963741 -0.41459575  0.01293240 -0.02084886  1.10109551 -3.92064847 
##        qsec        gear        carb 
##  0.54145693  1.23321026 -0.25509911

Each of the parameters of this model shows an average effect of each variable on the mileage. They have a simple interpretation and show how the response variable will change on average with the increase of a variable by 1 unit, keeping all the other variables constant. For example, the parameter for wt (weight) shows that with the increase of weight of a car by 1000 pounds, the mileage would decrease on average by 3.921 miles per gallon, if all the other variables do not change. I have made the word “average” boldface three times in this paragraph for a reason. This is a very important point to keep in mind - the parameters will not tell you how variable will change for any specific observation. They do not show how it will change for each point. The regression model capture average tendencies and thus the word “average” is very important in the interpretation. In each specific case, the increase of weight by 1 will lead to different decreases (and even increases in some cases). But if we take the arithmetic mean of those individual effects, it will be close to the value of the parameter in the model. This however is only possible if all the assumptions of regression hold (see Section 15).