Question

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Q1. What is the difference between simple linear regression and multiple linear regression?

Q2. What is the difference between R, R² and adjusted R² in multiple linear regression.

Answer 1

Answer: Q1:

The difference between simple linear regression and multiple linear regression is that:

In simple linear regression, there is only one dependent and one independent variable. The independent variable is used to predict the dependent variable in the simple linear regression model.

While in the multiple linear regression, there is one dependent variable and two or more independent variables. These independent variables are used to predict the dependent variable in the multiple linear regression model.

Q2: R denotes the correlation coefficient and it tells us the strength and direction of the linear relationship between the two variables. The value of R lies between -1 to +1

R² denotes the coefficient of determination and it tells us the amount of variation in the dependent variable that is explained by the independent variables in the regression model. The value of R-square lies between 0 and 1.

Adjusted R² is a modified version of R-square and it tells us the amount of variation in the dependent variable that is explained by only those independent variable that truly affects the dependent variable. The value of Adjusted R-square is always less than R-square and sometimes negative as well