The appropriate use of multiple regression depends on being able to make four basic assumptions about the data being used to develop the regression model:
that variables are normally distributed;
that the relationship between an independent variable and the dependent variable is linear
that the variance of errors is homoscedastic; and
that there is no multicollinearity among independent variables.
Pick two of these assumptions, and describe how you would check it for a dataset that you want to use to build a regression model.© BrainMass Inc. brainmass.com June 4, 2020, 1:46 am ad1c9bdddf
that variables are normally distributed
You can create a few histograms and see what the shape of the distribution looks ...
The expert examines the basic assumptions of multiple regression analysis.