R Tutorial : Interpreting parallel slopes coefficients

Want to learn more? Take the full course at https://learn.datacamp.com/courses/multiple-and-logistic-regression at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Most often, our primary interest in models like this one is interpreting the value of the coefficients. What does the model tell us about the relationship between fuel economy and engine size in the context of the year when the cars were manufactured? Let's start with the main intercept. The value is 35.276 and the units are the same as those of the response variable---miles per gallon. Recall that this is the expected fuel economy for a car from 1999 that had an engine size of 0 liters. Of course, in this case, this value has little meaning, since there is no such thing as a car with an engine size of 0 liters, but that is the literal interpretation of the role that 35.276 plays in our model. As we saw above, the coefficient on year can also be thought of as an intercept. Note here that R has chosen to report the name of the coefficient as factor(year)2008. This reflects the fact that factor(year) was the name of the variable we gave R, and 2008 is the value of that variable that R is reporting about. This variable is identical to the newer variable that we defined in the previous set of slides. Here, R is telling us that cars manufactured in 2008 get about 1.4 miles per gallon better gas mileage than those manufactured in 1999, after controlling for engine size. This is our key finding, and I'll return to this in just a minute. How did we "control for engine size"? By including the displ variable in our model, we also obtain a coefficient for engine size. This is our slope coefficient, and it tells us that each extra liter of engine size is associated with a decrease in expected fuel economy of 3.61 miles per gallon, after controlling for year of manufacture. This is the negative relationship we saw earlier: larger engines tend to go in l