Blog.

A problem with R is that its tables are not good enough to share with non-R users, both in terms of visual attractiveness and ease…

This post introduces the key concepts in designing experiments for choice-based conjoint analysis (also known as choice modeling). I use a simple example to describe…

While choice-based conjoint analysis represents one of the more sophisticated techniques used in market research, presentation of its results commonly consists only of a simulator,…

Modern tools for analyzing conjoint analysis, such as hierarchical Bayes, produce rich data showing preferences for each person in a market. The main deliverable from…

Indifference curves are a way of showing relative preferences for quantities of two things (e.g., preferences for price versus delivery times for fast food). This…

Working out the sample size required for a choice-based conjoint study is a mixture of art and science. What makes it tricky is that the…

I love the formattable package, but I always struggle to remember its syntax. A quick Google search reveals that I’m not alone in this struggle.…

You can have a video playing on a page in Displayr, as it’s very simple to set up. The video needs to be hosted on…

If you’ve studied a bit of statistics or machine learning, there is a good chance you have come across logistic regression (aka binary logit). It…

Feature engineering refers to the process of manipulating predictor variables (features) with the goal of improving a predictive model. In this post I outline some of the…

When creating a predictive model, there are two types of predictors (features): numeric variables, such as height and weight, and categorical variables, such as occupation…

When building a predictive model, it is often practical to improve predictive performance by modifying the numeric variables in some way. In statistics, this is…

A correlation matrix is a table of correlation coefficients for a set of variables used to determine if a relationship exists between the variables. The…