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Testing Assumptions Linear Regression in R

How do you test the assumptions for linear regression or multiple regression in R? This video tutorial shows you how to test the necessary regression assumptions in R using R commands based on various packages. CONTENT: The following procedures for testing regression assumptions are shown here: Homoscedasticity Homoscedasticity in a linear regression with R can be tested both graphically (scatter plot of predicted values and residuals) and with a hypothesis test, e.g. the Breusch-Pagan test. Normal distribution If you test the normal distribution in R, or more precisely the normal distribution of the residuals, you can do this in several graphical and numerical ways. Graphical tests: Histogram for normal distribution QQ plot Numerical tests: Shapiro-Wilk test Skewness and Kurtosis Agostino test and Anscombe test (for skewness and kurtosis) Linearity When testing linearity in R, you can look at scatterplots, you can also run a hypothesis test for linearity, e.g. the Rainbow test. Multicollinearity Checking the absence of strong multicollinearity using Variance Inflation Factors (VIF). Outliers To determine outliers in R, there are many different diagnostic procedures. When I want to detect outliers in R, I mainly use the following diagnostics: Studentized residuals Cook's distance Leverage values DfBetaS The assumptions uncorrelatedness/independence of the residuals as well as the appropriate scale properties (metric variables, predictors also binary possible), have to be checked by looking at the study design. Companion webpage to this tutorial with all the R code examples used in the video: http://www.regorz-statistik.de/en/r_t...