Data considerations for Fit Poisson Model

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 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.

The response variable should describe the number of times an event occurs in a finite observation space
A Poisson response counts events, such as the number of defects detected on an item.
  • 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 contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.
Consider the use of a model validation technique
Minitab lets you choose to validate the model with a test data set or with cross-validation. Model summary statistics, like deviance R2, that are for the data from the model fitting process tend to be optimistic. The use of a test data set or cross-validation can give a more accurate representation of how the model performs for new data.
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, use the variance inflation factors (VIF) in the Coefficients table of the output.

The model should provide a good fit to the data

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.