Data considerations for Nominal Logistic Regression

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The predictors can be continuous or categorical

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 factors that are random, use Fit Mixed Effects Model. For Fit General Linear Model, the response is continuous.
The response variable should be nominal
A nominal response has three or more outcomes that do not have an order, such as a scratch, dent, and tear.
  • If your response variable has two categories, such as pass and fail, use Fit Binary Logistic Model.
  • If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
  • If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.
Collect data using best practices
To ensure that your results are valid, consider the following guidelines:
  • Make certain that the data represent the population of interest.
  • Collect enough data to provide the necessary precision.
  • Measure variables as accurately and precisely as possible.
  • Record the data in the order it is collected.
The correlation among the predictors, also known as multicollinearity, should not be severe

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, examine the correlation between the predictor variables. To determine whether the predictors are highly correlated, choose Stat > Basic Statistics > Correlation.

The model should provide a good fit to the data

If the model does not fit the data, then the results can be misleading. An adequate model has p-values for the goodness-of-fit tests that are greater than your alpha value. This condition indicates that there is insufficient evidence to claim that the model does not fit the data adequately. In the output, check the goodness-of-fit tests.

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