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have you ever wondered why a summary function of a test like Anova tells you that the number of cylinders docHubly affects car mileage without explaining how or have you ever been puzzled why a model Compares all categories of a categorical predictor to only the reference category without comparing categories to each other and what about those mysterious slopes with standard arrows we get as a model coefficient instead of the averages per category with 95 confidence intervals that we actually want moreover summary function doesnamp;#39;t adjust P values for multiple comparisons which increases the probability of discovering nonsense by making too many type 1 errors summary function doesnamp;#39;t plot the results of a model and makes it almost impossible to interpret interactions so if youamp;#39;ve ever been frustrated due to similar issues you are definitely not alone this summary function doesnamp;#39;t actually provide a very useful summary and thatamp;#39;s why we need a