Sampling Variation Teaching Visualisation

The VIT (Visual Inference Tools) package creates animations that are used for teaching statistical concepts such as bootstrap confidence interval construction, permutation testing and confidence intervals. One of the animations it can create is used for teaching the concept of sampling variation.

This example differs from the LOESS and ARIMA examples because it requires far more coordination once data has been given to the browser. This additional complexity is reduced immensely when the animaker package is used to describe and apply the animation sequencing. This is a huge advantage that a gridSVG implementation has over VIT. VIT has no way of keeping track of time because it repeatedly draws as quickly as possible. This means that VIT animations vary in length depending on how complex the grid scene is in addition to being “choppy”. These limitations are not present when animating SVG content because we can declaratively say what, when and for how long graphical content is being animated.

The following video demonstrates the sampvar example found in the sjpMScThesis package, which can be downloaded at my Master’s Thesis page. To run this example yourself, open R and run the following command: library(sjpMScThesis) ; thesisExample("sampvar")


This video example is available in both MP4 (x264) and WebM formats: