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In the 6th Minitab tutorial, we are in the product purchase department of Smartboard Company, and we will experience how the team uses the so-called, two-sample t-test, to evaluate the delivery quality of different screw suppliers. Smartboard Company previously purchased its screws from just one screw supplier. We need to know, that due to the current increase in demand, there have been repeated supply bottlenecks at the screw supplier, which has meant that skateboard production has had to be stopped due to a lack of screws. For this reason, Smartboard Company has recently started to be supplied by another screw manufacturer in addition to the previous supplier, in order to avoid future production bottlenecks. From a quality point of view, it is therefore very important that the mechanical properties of the screws from the new supplier, do not differ significantly from those of the previous supplier. Our task in this Minitab tutorial unit will be, to use the so-called, t-test for two samples, to work out whether there are significant differences in the strength properties between the two suppliers. We will also discuss a number of other useful functions and topics, as part of our two-sample t-test, as well as using dot plots and box plots, and learning about the useful „summary report“ function in this context. We will understand what is meant by the statistical quality parameters kurtosis and skewness, of a data landscape. We will carry out a variance test for two samples, in order to carry out a variance comparison of our data sets, in addition to the mean value comparison. For the variance test, we will familiarize ourselves with the Bonett and Levene procedures, in order to understand how to properly carry out the corresponding hypotheses in the variance test, and interpret them in an understandable way. Finally, we will get to know the so-called, layout tool, in order to summarize the most important analysis graphs and plots together, in one graphical layout.


  • t-test, 2 samples
  • Carrying out the discrimination power analysis to determine the sample size
  • Basic mathematical idea of degrees of freedom
  • Performance and interpretation of the t-test for two samples
  • Formulation of the null hypothesis and alternative hypothesis
  • Box plot „Multiple Y, simple“ as part of the t-test
  • Rescaling of dot plots
  • Creation and interpretation of boxplots of the t-test
  • Working with the „Graphical summary“ option
  • Quality parameters kurtosis and skewness of the data distribution
  • Test for variances, 2 samples
  • Significance values in the variance test according to Bonett & Levene
  • Working with the „Layout Tool“