On the Data tab of the Binary Logistic Regression dialog box, select the option that best describes your data.

Complete the following steps if the response data is a single column with two distinct values. Optionally, the data can include a column that contains the count of responses that corresponds to the response and predictor values in the row.

- From the drop-down, select Response in binary response/frequency format.
- In Response, enter the column of binary data that you want to explain or predict. Binary variables are categorical variables that have two possible levels, such as pass/fail or true/false. The response is also called the Y variable.
- In Response event, select which event the analysis will describe. Changing the response event does not affect the overall significance, but it can make the results more meaningful. The event is also known as a success.
- (Optional) In Frequency, enter the column that contains the counts that correspond to the response and predictor values in the row.
- In Continuous predictors, enter the continuous variables that may explain or predict changes in the response. The predictor is also called the X variable.
- In Categorical predictor, enter the categorical classification or group assignment, such as type of raw material, that may explain or predict changes in the response. You can enter only one categorical predictor. The predictor is also called the X variable.

In this worksheet, Bought is the response and indicates whether a consumer purchased a new brand of cereal. The response event is Yes. Income is a continuous predictor and Ad is a categorical predictor. The first row in the worksheet shows that one consumer with an income of $37,000 who viewed the advertisement bought the new brand of cereal.

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

Bought | Income | Ad |

Yes | 37 | Yes |

No | 47 | Yes |

Yes | 34 | No |

Yes | 58 | No |

In this worksheet, the response and predictor variables are the same as the previous example but the data also include a frequency variable. Frequency contains the count of consumers that correspond to the combination of response and predictor values in each row. The first row in the worksheet shows that 2 consumers with an income of $40,000 and who viewed the advertisement bought the new brand of cereal.

C1 | C2 | C3 | C4 |
---|---|---|---|

Bought | Income | Ad | Frequency |

Yes | 40 | Yes | 2 |

No | 40 | No | 12 |

Yes | 45 | Yes | 1 |

No | 45 | No | 6 |

Complete the following steps if the response data are contained in two columns – one column that contains the number of successes or events of interest and one column that contains the number of trials.

- From the drop-down, select Response in event/trial format if your response data is contained in two columns that include events and trials.
- In Event name, enter a name for the event in the data.
- In Number of events, enter the column that contains the number of events. Events are also known as successes.
- In Number of trials, enter the column that contains the number of trials. Trials represent the number of events plus the number of nonevents.
- In Continuous predictors, enter the continuous variables that may explain or predict changes in the response.
- In Categorical predictors, enter the categorical classifications or group assignments, such as type of raw material, that may explain or predict changes in the response.

In this worksheet, Bought contains the number of events, which indicates how many consumers bought a new brand of cereal. Trials contains the number of trials, which indicates the total number of consumers that were surveyed for that combination of predictor variables. Income is a continuous predictor and Ad is a categorical predictor. The first row in the worksheet shows that 20 consumers were surveyed that had an income of $37,000 and viewed the advertisement , and 2 of them bought the new brand of cereal.

C1 | C2 | C3 | C4 |
---|---|---|---|

Bought | Trials | Income | Ad |

2 | 20 | 37 | Yes |

0 | 3 | 37 | No |

4 | 12 | 40 | Yes |

3 | 18 | 34 | No |