Modeling the 2018 PISA Dataset based on PCA and Clustering

A series of interactive tutorials introducing principle component analysis, clustering, linear modelling and cross-validation for large datasets.

QQplot

This Shiny app is meant to let you play around with a few different distributions (Normal, Skew Normal, Cauchy, Skew Cauchy), and check what the effect of different variables (scale, location and shape) on their qq-plot is.

The qq-plots are all a comparison with a default normal distribution (mean=0, sd=1). There is a large slider you can use to track specific percentiles on all graphs. This Shiny app was made for teaching purposes.

Distributions

This app illustrates the effect of violations of assumptions for the F distribution. It is an update of a TquanT app.

Modeling PISA Data by Clusters

An illustration of statistical learning and visualisation techniques using real-world data (PISA).

Properties of Relations

This app allows you to define an arbitrary binary relation on a set of five items and shows you whether certain properties are fulfilled.

Conditional Testing

There is a PDF Document available for download which contains information about the app that is also available through the app’s help buttons.

LexOPS

LexOPS finds characteristics of words from large corpora to be used in lexical studies.

Learnr Tutorial on Linear Regression

Validating BLIM-Simulated Data

This app does a series of BLIM simulations and shows the dependence of the Discrepancy Index from the noise. This app was inspired by a student app from the 2019 seminar.