![]() Minitab Statistical Software does following statistical tests: To differentiate between two papers, how many samples are needed if the average thickness of paper differs from one supplier to another? How many times should an experiment be replicated to have at least an 85% chance of detecting the factors that significantly affect a manufacturing process? The answers to these questions become easy with Minitab’s Power and Sample Size tools. For example, testing DVD players requires a low degree of certainty as compared to testing critical airplane parts that demand a higher degree of certainty. Statistical power refers to the probability that your hypothesis test identify a significant difference or effect when one truly exists. Gathering fewer data restricts the reliability of an analysis, but gathering too much data leads to wastage of resources. In order to be more precise, Minitab can assess the statistical power of tests that have already been run and estimate the sample size. Minitab’s Power and Sample Size tools collect enough data to conduct a thorough analysis. It can efficiently determine exactly how much data is needed to be sure about the results of an analysis. Minitab’s Power and Sample Size tools enable users to balance these issues. While performing the statistical test, you have to consider the precision and have to be confident with your results to meet your goals. Minitab, designed to meet specific needs for six sigma professionals, is statistical software that provides an effective and efficient way to input and control numerical data, recognize trends and patterns, and derive insights. You don’t use a sword to cut vegetables or a knife in a battle-field because neither provides you the desired outcome. The confidence interval output will appear in the session window.For any type of work, you need a specific tool that offers the right amount of power. 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. 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 \(\mu_X-\mu_Y\). 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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