July 2017
Beginner to intermediate
442 pages
10h 8m
English
Content preview from Statistics for Machine Learning
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Assumptions of linear regression
Linear regression has the following assumptions, failing which the linear regression model does not hold true:
- The dependent variable should be a linear combination of independent variables
- No autocorrelation in error terms
- Errors should have zero mean and be normally distributed
- No or little multi-collinearity
- Error terms should be homoscedastic
These are explained in detail as follows:
- The dependent variable should be a linear combination of independent variables: Y should be a linear combination of X variables. Please note, in the following equation, X2 has raised to the power of 2, the equation is still holding the assumption of a linear combination of variables:
How to diagnose: Look into residual ...
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