# Regression Analysis

Use regression analysis to describe the statistical relationship between one or more predictors and the response variable and to predict new observations.

To add output from a regression analysis, go to Add and complete a form.

## Best subsets regression

Use best subsets regression to compare different regression models that contain subsets of the predictors you specify predictors you specify.

For example, an analyst at a retail store wants to predict sales volume. The predictors include traffic, population, average income, and direct competitors near the store. The analyst uses best subsets regression to identify the set of predictors that best predict sales volume. To see an example, go to Minitab Help: Example of Best Subsets Regression.

### Data considerations

Your data must be a continuous value for Y and numeric values for the Xs. You can convert categorical Xs into indicator variables. For details, go to Minitab Help: Data considerations for Best Subsets Regression.

## Fitted line plot

Use fitted line plot to display the relationship between one continuous predictor and a response.

You can fit a linear, quadratic, or cubic model to the data. A fitted line plot shows a scatterplot of the data with a regression line representing the regression equation.

For example, an engineer at a manufacturing site wants to examine the relationship between energy consumption and the setting of a machine used in the manufacturing process. The engineer thinks the relationship between these variables is curvilinear. Therefore, the engineer creates a fitted line plot and fits a quadratic model to the data. To see an example, go to Minitab Help: Example of Fitted Line Plot.

### Data considerations

Your data must be a continuous value for Y and a continuous or discrete value for X (with multiple levels). For details, go to Minitab Help: Data considerations for Fitted Line Plot.

## Multiple regression

Use multiple regression to examine the relationships between one continuous response and two or more predictors.

If the number of predictors is large, then before fitting a regression model with all the predictors, you should use stepwise or best subsets model-selection techniques to screen out predictors not associated with the responses.

For example, a research chemist wants to understand how several predictors are associated with the wrinkle resistance of cotton cloth. The chemist performs a multiple regression analysis to fit a model with the predictors and eliminate the predictors that do not have a statistically significant relationship with the response. To see an example, go to Minitab Help: Example of Fit Regression Model.

### Data considerations

Your data must be a continuous value for Y and numeric values for Xs. You can convert categorical Xs into indicator variables. For details, go to Minitab Help: Data considerations for Fit Regression Model.

## Simple regression

Use simple regression to provide the linear relationship between two continuous variables: one response (Y) and one predictor (X).

Simple regression allows you to predict the value of the output Y for any value of the input X. To see an example, go to Minitab Help: Example of Fit Regression Model.

### Data considerations

Your data must be a continuous value for Y and a numeric value for X. For details, go to Minitab Help: Data considerations for Fit Regression Model.

## Stepwise regression

Use stepwise regression to evaluate multiple process inputs without the use of a designed experiment.

Stepwise regression is an automated tool used in the exploratory stages of model building to identify a useful subset of predictors. The process systematically adds the most significant variable or removes the least significant variable during each step. It also allows you to predict the value of the output (Y) for any combination of values of the inputs (Xs). For more information, go to Minitab Help: Perform stepwise regression for Fit Regression Model.

### Data considerations

Your data must be a continuous value for Y and numeric values for the Xs. You can convert categorical Xs into indicator variables. For details, go to Minitab Help: Data considerations for Fit Regression Model.