The confidence interval output will appear in the session window. Do the same thing for the Second variable (menneus data, for us), that is, type the Sample size, Mean, and Standard deviation in the appropriate boxes. Then, for the First variable (deinopis data, for us), type the Sample size, Mean, and Standard deviation in the appropriate boxes. In the pop-up window that appears, select Summarized data. ![]() ![]() Under the Stat menu, select Basic Statistics, and then select 2-Sample t.: Since we've already learned how to ask Minitab to calculate a confidence interval for \(\mu_X-\mu_Y\) for both of those data arrangements, we'll take a look instead at the case in which the data are already summarized for us, as they are in the spider and prey example above. The 1 sample t-test is used to determine if the mean of normally distributed data is equal to a target value, x. As a rule of thumb, if the ratio of the larger variance to the smaller variance is less than 4 then we can assume the variances are approximately equal and use the Student’s t-test. Again, the commands required depend on whether the data are entered in two columns, or the data are entered in one column with a grouping variable in a second column. We simply skip the step in which we click on the box Assume equal variances. Otherwise, they'll use the two-sample pooled \(t\)-interval.Īsking Minitab to calculate Welch's \(t\)-interval for \(\mu_X-\mu_Y\) require just a minor modification to the commands used in asking Minitab to calculate a two-sample pooled \(t\)-interval. Then they'll use Welch's \(t\)-interval for estimating \(\mu_X-\mu_Y\). The formula for the T value (0.92) shown above is calculated using the following formula in Minitab: The output from the 1-sample t test above gives us all the information we need to plug the values into our formula: Sample mean: 43.43 Sample standard deviation: 34. Substituting in what we know, the degrees of freedom are calculated as: Let's calculate Welch's \(t\)-interval to see what we get. ![]() do those sample variances differ enough to lead us to believe that the population variances differ? If so, we should use Welch's \(t\) -interval instead of the two-sample pooled \(t\)-interval in estimating \(\mu_X-\mu_Y\).
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