Question

• What E assumes to possess in a multiple regression model?
• List out some assumptions for performing a regression analysis
• When evaluating E in a regression model, what are some facts about E?
• Facts about a simple linear regression analysis?

Answer 1

Within a linear regression model tracking a stock’s price over time, the error term is the difference between the expected price at a particular time and the price that was actually observed.

There are four assumptions associated with a linear regression model:

1. Linearity: The relationship between X and the mean of Y is linear.
2. Homoscedasticity: The variance of residual is the same for any value of X.
3. Independence: Observations are independent of each other.
4. Normality: For any fixed value of X, Y is normally distributed.

An error term appears in a statistical model, like a regression model, to indicate the uncertainty in the model.

The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable?