Reading 11, 3.2 Linear Regression Model Assumptions in pictures

Level 2, Volume 1, Quantitative methods, Reading 11, Correlation & Regression


3.2 Recalling the assumptions of the Linear Regression Model

The six assumptions, noted yesterday, is a bit dry. So I have added some images (and the explanation of the images) to make it easier to recall these assumptions.

Assumptions	Ideas pictures	Images
1. The relationship between the dependent variable, Y, and the independent variable, X is linear in the parameters b0 and b1.	I associate “Independent” with the American flag, and the lines on the flag is “Linear”
2. The independent variable, X, is not random.	To be “Independent” does not happen at random, but is the result of “hard work”
3. The expected value of the error term is 0: E(℮) = 0. (Unbiased)	One of the American values is  that judges should be “unbiased”.
4. The variance of the error term is the same for all observations. (also called Homoscedasticity)	Homo = Same, therefore associate it with twins (with little variance in looks etc)
5. The error term, ℮, is uncorrelated across observations. Consequently, E(℮i, ℮j) = 0 for all i not equal to j	Error term is uncorrelated across observations (One would think that Arnie & Danny is uncorrelated)
6. The error term, ℮, is normally distributed.	The Eiffel Tower has the shape of a normal distribution