To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
A continuous variable can be measured and ordered, and has an infinite number of values between any two values. For example, the diameters of a sample of tires is a continuous variable.
Categorical variables contain a finite, countable number of categories or distinct groups. Categorical data might not have a logical order. For example, categorical predictors include gender, material type, and payment method.
If you have a discrete variable, you can decide whether to treat it as a continuous or categorical predictor. A discrete variable can be measured and ordered but it has a countable number of values. For example, the number of people that live in a household is a discrete variable. The decision to treat a discrete variable as continuous or categorical depends on the number of levels, as well as the purpose of the analysis. For more information, go to What are categorical, discrete, and continuous variables?.
If you have categorical predictors that are nested or random, use Fit General Linear Model if you have all fixed factors or Fit Mixed Effects Model if you have random factors. For Fit General Linear Model, the response is continuous.
If multicollinearity is severe, you might not be able to determine which predictors to include in the model. To determine the severity of the multicollinearity, use the variance inflation factors (VIF) in the Coefficients table of the output.
If the model does not fit the data, the results can be misleading. In the output, use the residual plots, the diagnostic statistics for unusual observations, and the model summary statistics to determine how well the model fits the data.