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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.…

In recent posts we’ve discussed how you can reference source tables using R in Q to manipulate the statistics into a new custom table. In…

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…

Q has a terrific feature whereby it can automatically compare statistics against the previous period (of time). This is great for tracking scenarios, when you…

Missing values in a data set are “blank” values. They are normally associated with survey skips. Those who skip a variable/question receive a missing value. Missing…

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…