In this app you will explore examples of the Chi-Square Test of Independence; you will also be able to test your own dataset. The Fill in the Blanks exercise would serve to test your understandings about this topic.
![](https://sites.psu.edu/shinyapps/files/2018/12/75990f5811f638b92b0577c1d7e5fff6d9c8cf61-it-1yyhyn1.png)
In this app you will explore examples of the Chi-Square Test of Independence; you will also be able to test your own dataset. The Fill in the Blanks exercise would serve to test your understandings about this topic.
This app illustrates R code for data wrangling, reshaping and combining data.
This app focuses on tests for proportions in a sports context (basketball free throw shooting). The app illustrates how a change in the population caused by filtering can change the results and also illustrates the difference between p, p̂, and p0.
The app requires students to engage with inference scenarios for a mean (or difference in means) in context. Users will explore the behavior of confidence intervals for a single mean and two sample tests for the difference in means as the level and sample size changes.
The app requires students to engage with inference scenarios for a proportion (or difference in proportions) in context. Users will explore the behavior of confidence intervals for a single proportion and two sample tests for the difference in proportions as the level and sample size changes.
This app allows students to explore the k -means Clustering algorithm.
This app explores the use of the log transformation in real datasets to make data more symmetric, more interpretable, or closer to a linear relationship with another variable.
This app explores confidence intervals for odds ratios and their use in the meta-analysis of real data.