RM | Unit 200 - Liner model with one dummy and one numeric variable
Book: analyzing data using linear models
Chapter 6: 6.5
Chapter 6.5: Two independent variables: one dummy and one numeric variable
If we have two or more numeric variables, one usually talks about multiple regression models.
When we have a categorical variable that we treat as a numeric dummy variable, then we can
therefore also have linear models with both a categorical variable and a numeric variable.
The slope coefficient for the window should be interpreted as ”the increase in price if we change the seat
from aisle to window, given a certain amount of legroom”.
The intercept of 41.5 means that the model predicts that you pay 41.5 if you happen to have 0
centrimeters of leg room and an aisle seat (the reference category).
Book: analyzing data using linear models
Chapter 6: 6.5
Chapter 6.5: Two independent variables: one dummy and one numeric variable
If we have two or more numeric variables, one usually talks about multiple regression models.
When we have a categorical variable that we treat as a numeric dummy variable, then we can
therefore also have linear models with both a categorical variable and a numeric variable.
The slope coefficient for the window should be interpreted as ”the increase in price if we change the seat
from aisle to window, given a certain amount of legroom”.
The intercept of 41.5 means that the model predicts that you pay 41.5 if you happen to have 0
centrimeters of leg room and an aisle seat (the reference category).
