To add output from a 1-sample hypothesis test, go to Add and complete a form.
For example, you can test whether a new setting significantly changes the proportion defective. To see an example, go to Minitab Help: Example of 1 Proportion.
Your data must contain only two categories, such as pass/fail. For details, go to Minitab Help: Data considerations for 1 Proportion.
For example, you can test whether the mean output from the controlled improved process is different from the pre-project mean. To see an example, go to Minitab Help: Example of 1-Sample t.
The data must be continuous and reasonably normal. A 1-sample t-test is robust to violations of the normality assumption, especially if the sample size is large (n > 25). For more details, go to Minitab Help: Data considerations for 1-Sample t.
For example, you can test whether the mean output from the controlled improved process is different from the pre-project mean. To see an example, go to Minitab Help: Example of 1-Sample Wilcoxon.
Your data must be a continuous value for Y (output). The data should come from a symmetric distribution, such as the uniform or Cauchy distributions. If your data do not come from a symmetric distribution, use a 1-sample sign test. For more details, go to Minitab Help: Data considerations for 1-Sample Wilcoxon.
For example, a quality analyst uses a 1 variance test to determine whether the variance of the moisture content in a shipment of unprocessed lumber is too high. To see an example, go to Minitab Help: Example of 1 Variance.
Your data must be continuous Y (output) values. For more details, go to Minitab Help: Data considerations for 1 Variance.
For example, you can test whether the mean output from the controlled improved process is different from the pre-project mean. To see an example, go to Minitab Help: Example of 1-Sample Sign.