The table above is what I call an old-school crosstab. If you squint, and have seen one of these before, then you can probably read it. The basic design of these has been around since the 1960s. Originally,…

In this post I explore two different methods for computing the relative importance of predictors in regression: Johnson’s Relative Weights and Partial Least Squares (PLS) regression. Both techniques solve a problem with Multiple Linear Regression, which can perform poorly when there are correlations…

Partial Least Squares (PLS) is a popular method for relative importance analysis in fields where the data typically includes more predictors than observations. Relative importance analysis is a general term applied to any technique used for…

A key driver analysis investigates the relative importance of predictors against an outcome variable, such as brand preference. Many techniques have been developed for key driver analysis, to name but a few: Preference Regression, Shapley Regression,…

Partial Least Squares (PLS) is a popular method for relative importance analysis in fields where the data typically includes more predictors than observations. Relative importance analysis is a general term applied to any technique used for…

This post describes the single biggest time saving technique that I know about for highlighting significant results on a table. The table below, which shows the results of a MANOVA, illustrates the trick. The coloring…

Sometimes it is helpful if one Displayr document can refer to information in another document. For example, one document may contain an analysis of sales data, and you may want to include some aspect of…

Gradient boosting is a technique attracting attention for its prediction speed and accuracy, especially with large and complex data. Don’t just take my word for it, the chart below shows the rapid growth of Google…

Support vector machines (SVMs) are a great machine learning tool for predictive modeling. In this post, I illustrate how to use them. For most problems SVMs are a black box: you select your outcome variable and…

This post compares various approaches to analyzing max-diff data using a method known as cross-validation. Before you read this post, make sure you first read How max-diff analysis works, which describes many of the approaches mentioned in…

This post discusses a number of options that are available in R for analyzing data from max-diff experiments, using the package flipMaxDiff. For a more detailed explanation of how to analyze max-diff, and what the outputs…

This post discusses a number of options that are available in Displayr for analyzing data from max-diff experiments. For a more detailed explanation of how to analyze max-diff, and what the outputs mean, you should read the…