For Free


In the 13th Minitab tutorial, we find ourselves in the maintenance and servicing department of Smartboard Company.  This department is responsible for all improvement measures to ensure the best possible process availability, process efficiency, and quality output, throughout the entire skateboard production process. In order to be able to evaluate the process availability, process efficiency and quality output of skateboard production with just one key figure, Smartboard Company uses the industry-proven key performance indicator, O.E.E., as a measure of overall equipment effectiveness. The higher the O.E.E. figure, the better the overall equipment effectiveness of skateboard production. The maintenance and servicing department has identified performance fluctuations in skateboard production, based on the O.E.E. indicator, and suspects that these fluctuations may be due to the different product variants. The aim of this Minitab tutorial is to find out, whether the different product variants, such as longboard, e-board, or mountainboard, have a statistically significant influence on the overall equipment effectiveness O.E.E. The so-called quality parameters of our variance model will be very important in this training unit, as they will give us an indication of how well our variance model can explain the proportion of the total variance. In this context, we will work in particular with the quality parameter for example, adjusted R-squared value, in order to assess whether our blocked variance model actually has a high model quality. And we will take this opportunity to familiarize ourselves with the useful table Adjustments and evaluations for unusual observations, which provides us with a compact compilation of conspicuous non-descriptive scatter components, known as so-called residuals. In order to be able to assess this residual dispersion graphically, we will get to know the very useful graphic option called, 4-in-1-diagram. We will also use the very helpful factor diagrams, and Tukey’s pairwise comparison test, to specifically identify which factor levels have significant, or non-significant effects on our response variable, overall equipment effectiveness, O.E.E. Based on the corresponding grouping letters and the corresponding so-called graphic tukey simultaneous test of means, we will finally be able to make a 95 % certain recommendation to the management of Smartboard Company, as to which targeted optimization measures should be implemented, depending on the respective product typ.


  • Blocked ANOVA, fundamentals
  • Interpretation of the quality measures R-sq and R-sq(adj)
  • Residual analysis within the framework of the blocked ANOVA
  • Fits and diagnostics for unusual observations
  • Factor diagrams within the framework of the blocked ANOVA
  • tukey simultaneous test of means
  • Residual analysis as part of the blocked ANOVA
  • Tukey simultaneous test for differences in mean
  • Tukey simultaneous 95% confidence interval chart for differences in means