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

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