CFA Level 2, Reading 12.d) Assumptions of Multiple Regression

CFA Level 2, Volume 1, Quantitative methods, Reading 12, Multiple Regression

(References refer to CFA text book)

LOS 12.d) Explain the assumptions of a multiple regression model

The assumptions of the Multiple Regression model is very much the same as that of the classical normal linear regression model. Also see the link to post to memorize these assumptions.
The differences are noted in RED.

Classic Normal Linear Regression Model Assumptions		Multiple Regression Model Assumptions
1. The relationship between the dependent variable, Y, and the independent variable, X is linear in the parameters b0 and b1.		1. The relationship between the dependent variable, Y, and the independent variables is linear.
2. The independent variable, X, is not random.		2. The independent variables are not random. No exact linear relation exists between two or more of the independent variables.
3. The expected value of the error term is 0: E(℮) = 0.		3. The expected value of the error term, conditioned on the independent variables, is 0
4. The variance of the error term is the same for all observations. (also called Homoscedasticity)		4. The variance of the error term is the same for all observations. (also called Homoscedasticity)
5. The error term, ℮, is uncorrelated across observations. Consequently, E(℮i, ℮j) = 0 for all i not equal to j		5. The error term, ℮, is uncorrelated across observations. Consequently, E(℮i, ℮j) = 0 for all i not equal to j
6. The error term, ℮, is normally distributed.		6. The error term is normally distributed

Sources

1. www.duke.edu/~rnau/testing.htm

2. Wikipedia.org/wiki/forecast_bias