Voraussetzungen bei der linearen Regression

Prerequisites for Linear Regression For the results of regression analysis to be meaningfully interpreted, 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 error must be normally distributed. 4) No Multicollinearity: There must be no high correlation between the independent variables. Linearity: In linear regression, a straight line is drawn through the data. This line should represent all points as accurately as possible. If the relationship is nonlinear, the line cannot meet this requirement. Normal Distribution of the Error: One assumption of linear regression is that the error ε must be normally distributed. To verify this, there are two methods: the analytical approach and the graphical approach. Homoscedasticity: One assumption for linear regression is that the residuals have a constant variance. Since your regression model never predicts your dependent variable exactly in practice, you will always have an error. You can plot your dependent variable on the x-axis and the error on the y-axis. Multicollinearity: Multicollinearity occurs when two or more of the predictors are highly correlated. Online Regression Calculator: https://numiqo.com/statistik-rechner/... More information on linear regression: https://numiqo.com/tutorial/lineare-r...