We have seen that the easiest distribution pattern to analyze is unimodal and symmetric. Sometimes we can make a data distribution more nearly unimodal and symmetric if we change the units of the measurements: that is, transform the measurements. The result is to make the analysis and interpretation of the data distribution pattern easier. Two examples are the measure of acidity known as pH (the negative logarithm of the concentration of hydrogen ions in a solution) and the measure of earthquake magnitudes known as the Richter scale (the logarithm of the energy released by the earthquake).


In this lab, you will be introduced to the family or power transformations, which are often used to transform data to a form more suitable for analysis. You will see how different transformations change the pattern of variation in a set of data. To learn about power transformations, click on the button to the left.