Assumptions of linear regression

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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