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.
- (Optional) In Frequency, enter the column that contains the counts that correspond to the response and predictor values.
- In Continuous predictors, enter the continuous variables that may explain or predict changes in the response. The predictors are also called X variables.
- 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. The predictors are also called X variables.

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 Children is a categorical predictor. The first row in the worksheet shows that one consumer with children and with an income of $37,000 bought the new brand of cereal.

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

Bought | Income | Children |

Yes | $37,000 | Yes |

No | $47,000 | Yes |

Yes | $34,000 | No |

Yes | $58,000 | 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 children and with an income of $40,000 bought the new brand of cereal.

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

Bought | Income | Children | Frequency |

Yes | $40,000 | Yes | 2 |

No | $40,000 | No | 12 |

Yes | $45,000 | Yes | 1 |

No | $45,000 | 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. For example, the event could be successes, non-conforming units, or purchases.
- In Number of events, enter the column that contains the number of events.
- 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. The predictor is also called the X variable.
- 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. The predictor is also called the X variable.

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 Children is a categorical predictor. The first row in the worksheet shows that 20 consumers with children and with incomes of $37,000 were surveyed, and 2 of these consumers bought the new brand of cereal.

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

Bought | Trials | Income | Children |

2 | 20 | $37,000 | Yes |

0 | 3 | $37,000 | No |

4 | 12 | $40,000 | Yes |

3 | 18 | $34,000 | No |