On the Data tab of the Multiple Regression dialog box, specify the data for your analysis and determine whether you want to include the intercept in the regression equation.

Complete the following steps to specify the columns of data that you want to analyze.

- In Response, enter the columns of numeric data that you want to explain or predict. The response is also called the Y variable.
- In Continuous predictors, enter the columns of numeric data that may explain or predict changes in the response. The predictors are also called X variables.
- (Optional) In Categorical predictor, enter the categorical classifications or group assignments, such as a type of raw material, that may explain or predict changes in the response. You can enter one categorical predictor. The predictors are also called X variables.

In this worksheet, Strength is the response and contains the strength measurements of a sample of synthetic fibers. Temperature in a continuous predictor and Machine is a categorical predictor. The predictors may explain differences in fiber strength. The first row of the worksheet shows that the first sample of fiber has a strength measurement of 40, has a temperature of 136, and was produced on Machine A.

C1 | C2 | C3 |
---|---|---|

Strength | Temperature | Machine |

40 | 136 | A |

53 | 142 | A |

32 | 119 | B |

36 | 127 | A |

42 | 151 | B |

45 | 121 | B |

Select Fit intercept to include the intercept (also called the constant) in the regression model. In most cases, you should include the constant in the model.

A possible reason to remove the constant is when you can assume that the response is 0 when the predictor values equal 0. For example, consider a model that predicts calories based on the fat, protein, and carbohydrate contents of a food. When the fat, protein, and carbohydrates are 0, the number of calories will also be 0 (or very close to 0).

When you compare models that do not include the constant, use S instead of the R^{2} statistics to evaluate the fit of models.