Enter your data for 1 Proportion

Stat > Basic Statistics > 1 Proportion

Specify the data and the null hypothesis for your analysis.

Enter your data

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 One or more samples, each in a column.
Enter the column of data that you want to analyze. The column must contain two distinct values, such as True and False.
Tip
Click in the empty field under the data arrangement list to see the available data columns for your chart.
In Event, select a value to define the event. For example, if a response of 0 indicates a failure and a response of 1 indicates survival, select 0 to estimate the proportion of failures. Alternatively, select 1 to estimate the proportion of survivals.

In this worksheet, Purchase indicates whether a household made a purchase after receiving an advertisement.

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 events of interest. For example, to determine the proportion of defective parts, enter the number of defective parts.
In Number of trials, enter the total number of observations. For example, to determine the proportion of defective parts, enter the total number of parts from the sample.

If you want to calculate a p-value to determine whether the proportion differs from a hypothesized proportion, you must perform a hypothesis test.

Use a hypothesis test to determine whether the population proportion (denoted as ρ), differs significantly from the hypothesized value (denoted as ρ₀) 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. For more information, go to What is a hypothesis test?.
The Hypothesized proportion defines your null hypothesis (H₀: ρ = ρ₀). 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% (H₀: p = 0.043).