One part of our work in QHELP is the creation of educational R Shiny Apps. These apps let the learners experiment how changes in parameters of quantitative procedures influence the results. Below you find the apps written by the consortium partners.
This app does a series of BLIM simulations and shows the dependence of the Distance Agreement Coefficient from the noise. The app was inspired by a student app from the 2019 seminar.
LexOPS finds characteristics of words from large corpora to be used in lexical studies.
There is a PDF Document available for download which contains information about the app that is also available through the app’s help buttons.
This app allows you to define an arbitrary binary relation on a set of five items and shows you whether certain properties are fulfilled.
An illustration of statistical learning and visualisation techniques using real-world data (PISA).
This app illustrates the effect of violations of assumptions for the F distribution. It is an update of a TquanT app.
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.
A series of interactive tutorials introducing principle component analysis, clustering, linear modelling and cross-validation for large datasets.
This app illustrates the concept of learning paths in a knowledge structure. It is an extension of an app from the TquanT project.
How would your house look like if the carpenter building the windows is not fully reliable?
Illustrating neighbourhoods in knowledge structures