Select a preloaded data set from the below list, upload a data file, or enter the values of a variable.

Upload Options

Note: The file size limit is 5MB. Larger files will take longer to upload. Accepted formats include: .txt, .csv, and .tsv files.


Q-Q plot options

Standardization

App overview

In the app you can render lineups to introduce students to Q-Q plots

  • Choose a data set or upload your own.
  • Specify the response and explanatory variables.
  • Generate a lineup of stacked bar charts or mosaic plots.
  • Inspect the lineups and reveal the data plot.
After inspecting the lineup, you can focus on the observed data in the data plot tab.

Learning goals

This app is intended to introduce students to the logic behind hypothesis tests for an association. After exploring the app, students should understand that identifying the data plot indicates that either the data are systematically different from what would be expected if no association exists, or that they were 'lucky' in their guess. Further, students should understand that variability exists when there is no association, which is why we need to rely on inferential procedures to help us understand whether we observed signal or noise.

Example class usage

I recommend building a guided activity for your students. Before this activity I recommend discussing how Q-Q plots are constructed and read. You can also use the 'Rorschach protocol' (i.e. generating only null plots) to help students see a little variability before applying Q-Q plots to a data-driven problem. I recommend a guided activity with the following steps:

  1. Introduce the data set and problem.
  2. Generate a lineup of normal Q-Q plots. Quickly explain that one panel displays the observed Q-Q plot while the other panels are decoy plots generated under the assumption of normality. Have students choose the plot that is most different and justify their answer before revealing the answer.
  3. Ask students to discuss their decision in light of the 'answer' and what that indicates about the appropriateness of the normal model.
You can repeat this type of activity for a few data situations, providing students with examples of common departures from normality, along with different samples sizes from the normal model.

Additional resources

Below are additional resources to help you learn about visual inference and how it can be used in the classroom:

Author

Adam Loy - aloy.rbind.io

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.