![]() If there is a clear pattern or trend in the residual plot, it indicates violations of these assumptions. Residual plots: Plot the residuals against the predicted values to check for independence and homoscedasticity.Scatter plots: Examine scatter plots of the dependent variable against each independent variable to assess linearity. ![]() To test these assumptions, several diagnostic tests can be performed: Multicollinearity occurs when two or more independent variables are strongly correlated, which can lead to problems in interpreting the individual effects of the variables. No multicollinearity: The independent variables should not be highly correlated with each other.This assumption implies that the errors are normally distributed with a mean of zero. Normality: The residuals should follow a normal distribution.Homoscedasticity means that the spread or dispersion of the residuals is the same across the range of predicted values. This assumption is also known as homogeneity of variance. Homoscedasticity: The variance of the residuals should be constant across all levels of the independent variables.In other words, there should be no correlation or dependence between the residuals (the differences between the observed and predicted values). ![]()
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