Under Residuals for Plots, select either Regular or Standardized. Specify the response and the predictor(s). In reality, we are going to let Minitab calculate the F* statistic and the P-value for us. Select Stat > Regression > Regression.> Fit Regression Model. Since Ppk is the lesser of these two values, the Ppk is 0.80. If that doesn't suit you, our users have ranked more than 50. In Minitab, we use Calc > Calculator to enter the formulas and store them in the worksheet: The formulas above give us a PPU of 0.922719 and PPL of 0.800701.
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It's not free, so if you're looking for a free alternative, you could try ggraptR or Six Sigma Calculator. The best Online alternative is Number Analytics. The P-value is determined by comparing F* to an F distribution with 1 numerator degree of freedom and n-2 denominator degrees of freedom. Minitab is not available as a web app but there are some alternatives that runs in the browser with similar functionality. \(MSE=\dfrac\).Īs always, the P-value is obtained by answering the question: "What is the probability that we’d get an F* statistic as large as we did if the null hypothesis is true?" We already know the " mean square error ( MSE)" is defined as:
Let's tackle a few more columns of the analysis of variance table, namely the " mean square" column, labeled MS, and the F-statistic column labeled F. The sums of squares add up: SSTO = SSR + SSE.And the degrees of freedom add up: 1 + 47 = 48. The degrees of freedom associated with SSE is n-2 = 49-2 = 47.
The degrees of freedom associated with SSTO is n-1 = 49-1 = 48. The degrees of freedom associated with SSR will always be 1 for the simple linear regression model.Recall that there were 49 states in the data set.
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