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Assumptions of Linear Regression

Assumptions of Linear Regression: In order for the results of the regression analysis to be interpreted meaningfully, certain conditions must be met: 1) Linearity: There must be a linear relationship between the dependent and independent variables. 2) Homoscedasticity: The residuals must have a constant variance. 3) Normality: The residuals must be normally distributed. 4) No Multicollinearity: No high correlation between the independent variables Linearity: In linear regression, a straight line is placed through the data. This straight line should represent all points as good as possible. If the relation is nonlinear the straight line cannot fulfill this requirement. Normal distribution of the error: One assumption of linear Regression is that the error epsilon must be normally distributed, To check this there are two ways, one is the analytical way and the other is the graphical way. Homoscedasticity: A assumption for linear regression is that the residuals have a constant variance. Since your regression model never exactly predicts your dependent variable in practice, you always have an error. Now you can plot your dependent variable on the x axis and the error on the y axis. Multicollinearity: In multicollinearity, two or more of the predictors correlate strongly with each other. Test your assumptions for the linear Regression online: https://datatab.net/statistics-calcul... And here are mor informations about Regression: https://datatab.net/tutorial/linear-r...