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Response in binary response/frequency format

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.

  1. From the drop-down, select Response in binary response/frequency format.
  2. 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.
  3. 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.
  4. (Optional) In Frequency, enter the column that contains the counts that correspond to the response and predictor values.
  5. In Continuous predictors, enter the continuous variables that may explain or predict changes in the response. The predictors are also called X variables.
  6. 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

Response in event/trial format

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.

  1. From the drop-down, select Response in event/trial format if your response data is contained in two columns that include events and trials.
  2. In Event name, enter a name for the event in the data. For example, the event could be successes, non-conforming units, or purchases.
  3. In Number of events, enter the column that contains the number of events.
  4. In Number of trials, enter the column that contains the number of trials. Trials represent the number of events plus the number of nonevents.
  5. In Continuous predictors, enter the continuous variables that may explain or predict changes in the response. The predictor is also called the X variable.
  6. 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

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