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In the 12th Minitab tutorial, we accompany the quality team at Smartboard Company as they examine the material strength of skateboard decks using the so-called 2-Way ANOVA. Incidentally North American maple wood is always used as the base material for high-quality skateboard decks, as this type of wood is particularly stable and resistant due to its slow growth. To produce the skateboard decks, two layers of maple wood are first pressed together under pressure with water-based glue, and a special epoxy resin mixture, in an automated laminating process. The connection of the first two layers of wood in the core, is particularly crucial for the cohesion of the entire laminated composite. The quality of this core lamination is tested randomly in a tensile shear test, in which the two laminated layers of wood are pulled apart, by applying a force parallel to the joint surface, until the laminate joint tears open. In principle, the higher the maximum tensile shear strength of the laminate joint achieved in the laboratory test, the better. In this Minitab tutorial, we will be dealing with two categorical factors, each of which is available in three categorical factor levels. The core objective of this training unit will be to draw a statistically indirect conclusion about the production population on the basis of a sample, as to whether the corresponding factors have a significant influence on the tensile shear strength. We will also analyze, whether there are any so-called interaction effects, between the influencing factors, which may indirectly influence each other, and thus also indirectly significantly influence our response variable tensile strength. However we need to carry out some data management in advance, as the structure of the measurement protocol makes it necessary to restructure the data first. The aim of data management is to get to know the very useful option, Stacking of column blocks. To get a first impression of the trends and tendencies of our data, we will then work with boxplots before starting with variance analysis, 2-way ANOVA. Well prepared, we will then move on to the actual 2-Way analysis of variance in order to assess the significance of the trends and tendencies identified in the boxplots. In this context, we will also get to know the very useful main effects- and interaction plots. And learn how to interpret main and interaction diagrams. Finally, we will be able to use the so-called Tukey’s significance test, and the associated grouping letters, to work out which of the parameter constellations can actually be declared as significant.


  • 2-Way ANOVA, fundamentals
  • Data management in the preview window for data import
  • Stacking of column blocks within the framework of ANOVA
  • Boxplot analysis within the framework of ANOVA
  • Adjust interquartile ranges graphically
  • Include reference lines as part of the boxplot analysis
  • Definition of the „general linear model (ALM, GLM)“
  • Interpretation and evaluation of the variance and residual analysis
  • Working with the „marking palette“ in the context of residual analysis
  • Interpretation of the ANOVA model quality
  • Working with the histogram in the context of residual analysis
  • Factor diagrams in the context of ANOVA
  • Interpretation and editing of interaction diagrams
  • Tukey’s pairwise comparisons test