# Data considerations for Simple Binary 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 data should include only 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 more than one predictor, use Binary Logistic Regression.

The response variable should be binary

A binary response has two outcomes, such as pass and fail.

If you want to plot the relationship between a continuous (numeric) predictor and a continuous response, use Simple 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, 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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