Linear Regression

Question 1

What are the four key assumptions used to be able to draw valid conclusions from a simple linear regression model?

Answer 1

Linearity: The relationship between the dependent variable, Y, and the independent variable, X, is linear.

Homoskedasticity: The variance of the regression residuals is the same for all observations.

Independence: The observations, pairs of Ys and Xs, are independent of one another. This implies the regression residuals are uncorrelated across observations.

Normality: The regression residuals are normally distributed.

Question 2

What is Linearity?

Answer 2

The relationship between the dependent variable, Y, and the independent variable, X, is linear.

Question 3

What is homeoskedacity?

Answer 3

The variance of the regression residuals is the same for all observations.

Question 4

What is independence?

Answer 4

The observations, pairs of Ys and Xs, are independent of one another. This implies the regression residuals are uncorrelated across observations.

Question 5

What is normality?

Answer 5

The regression residuals are normally distributed.