Enter your data for Nominal Logistic Regression

Stat > Regression > Nominal Logistic Regression

Complete the following steps to specify the columns of data that you want to analyze.

  1. In Response, enter the column of nominal data that you want to explain or predict. Nominal variables are categorical variables that have three or more possible levels with no natural ordering. For example, the levels in a food tasting study may include crunchy, mushy, and crispy.
  2. In Frequency (optional), enter the column that contains the number of times that each response and predictor combination occurred.
  3. In Model, enter the terms that may explain or predict changes in the response. Terms can be continuous or categorical variables. The model can also have interactions and nested terms.
  4. In Categorical predictors (optional), specify which of the variables in the model are categorical classifications or group assignments, such as type of raw material. Minitab assumes that all variables in the model are continuous predictors (covariates) unless you specify them as categorical. Continuous predictors are modeled as covariates, and categorical predictors are modeled as factors.
In this worksheet, Subject is the response and the favorite school subject of each student who was surveyed. Method is a categorical predictor (factor) and represents the teaching method used in the class. Age is a continuous predictor (covariate) and is the age of the student.
C1 C2 C3
Subject Method Age
Math Explain 10
Science Demonstrate 12
Arts Explain 15
Math Demonstrate 11
In this worksheet, the response and model variables are the same as the previous example, but the data also include a frequency variable. Frequency contains the number of times that each response and predictor combination occurred. The first row shows that 2 students who are 13 years old prefer math taught with the Explain method.
C1 C2 C3 C4
Subject Frequency Method Age
Math 2 Explain 13
Science 6 Demonstrate 12
Arts 4 Explain 15
Math 1 Demonstrate 11
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