On the Data tab of the 1 Proportion dialog box, specify the data and the null hypothesis for your analysis.

Select the option that best describes your data.

Complete the following steps if your data are in a column of the worksheet.

- From the drop-down list, select Sample data are in a column.
- In Sample, enter the column of data that you want to analyze. The column must contain two distinct values, such as True and False.
- In Event, select the outcome that Minitab uses as the event (also called success). The sample proportion equals the number of events divided by the total number of trials.

In this worksheet, Purchase is the sample and indicates whether a household made a purchase after receiving an advertisement. The event is Yes.

C1 |
---|

Purchase |

Yes |

No |

No |

No |

Complete the following steps if you know the number of events and trials, and do not have actual sample data in the worksheet.

- From the drop-down list, select Summarized data.
- In Number of events, enter the number of successes. For example, if you want to determine the proportion of defective parts, the number of events would equal the number of defective parts.
- In Number of trials, enter the total number of observations. For example, if you want to determine the proportion of defective parts, the number of trials would equal the total number of parts that you sampled.

Complete the following steps to perform a hypothesis test and to specify a hypothesized value. For more information, go to What is a hypothesis test?

- Select Perform hypothesis test to determine whether the population proportion (denoted as ρ), differs significantly from the hypothesized value (denoted as ρ
_{0}) that you specify. If you don't perform the test, Minitab still displays a confidence interval, which is a range of values that is likely to include the population proportion. - Enter a value in Hypothesized proportion. The Hypothesized proportion defines your null hypothesis (H
_{0}: ρ = ρ_{0}). Think of this value as a target value or a reference value. For example, an analyst enters 0.043 to determine whether the proportion of customers that respond to a direct-mail offer is different from 4.3% (H_{0}: p = 0.043).