Data considerations for Best Subsets Regression

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

The data should include more than one continuous predictor

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, use Multiple Regression with a stepwise procedure to select a regression model by automatically adding or removing predictors based on their statistical significance.

The response variable should be continuous

If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.

If your response variable has two categories such as pass and fail, use Binary Logistic Regression.

Collect data using best practices
To ensure that your results are valid, consider the following guidelines:
  • Make sure 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 model should provide a good fit to the data
If the model does not fit the data, the results can be misleading. Best subsets identifies candidate models and provides results to determine how well the models fit. Best subsets does not provide residual plots or output to evaluate individual model terms. If you want to evaluate this output, use Multiple Regression to explore the candidate models further.
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