# Data considerations for Multiple 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

You can have 1 to 12 continuous predictors and, optionally, 1 categorical 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 one continuous predictor, you can use Simple Regression.
• If you have one categorical predictor and no continuous predictors, use One-Way ANOVA.
• If you have two categorical predictors and no continuous predictors, use Two-way ANOVA.
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 you have a categorical response variable, use logistic regression, which is available in Minitab Statistical Software.
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 correlation among the predictors, also known as multicollinearity, should not be severe

If multicollinearity is severe, you may not easily 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 regression output.

The model should provide a good fit to the data

If the model does not fit the data, then the results can be misleading. In the output, use residual plots, diagnostic statistics for unusual observations, and model summary statistics to determine how well the model fits the data.

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