ABSTRACT
This chapter discusses how to deal with non-normality via transformation of the response data. The most common transformation, the logarithm, is illustrated with a case study. The objective of the hockey experiment is to learn how to shoot a puck for distance with a flexible, 15-centimeter ruler. True hockey fans, particularly at the college level, appreciate the planning and organization of a well-coached team. The table top hockey data demonstrates a very common characteristic—as the response increases, the variance does too. The transformation was amazing, revealing a subset of relatively large effects, including interaction BD. Moreover, as one would expect from seeing such a dramatic half-normal plot, the subsequent analysis of variance indicated that all four chosen effects (B, C, D, and BD) were highly significant. If industrial experimenters do use a transformation, remember to reverse the process by applying the inverse function, such as the antilog for a logged response.
