Skip to main content

Full Discussion Board Idea #1: Repurposing gently-used, second-hand data during times of crisis

I can't be the only one teaching online statistics this Spring. Last fall, I refreshed ALL of my discussion boards for my online version of Psychological Statistics. I haven't done so since 2020, and my students responded well to my new discussion topics, all of which are centered in statistical literacy and improving problem-solving with data.

My first one is based on this old blog post about how residents of Houston used a Whataburger location map to figure out which parts of Houston were without electricity following Hurricane Meryl.

Here is how I presented it to my students:


You never know where valuable data visualizations will come from!


For instance, following Hurricane Beryl, Texans used the Whataburger app to track power outages across Houston. Whataburger is a popular restaurant chain in the South. Its app has a feature where users can quickly see open or closed locations. Normally, this is used by hungry people to find the closest, open location. HOWEVER: There are SO MANY Whataburgers in Houston that users could use it to track power outages since locations without power (in gray) would indicate that a neighborhood didn't have power, while a location in orange indicates that a neighborhood did have power.


See the Tweet below for an example fo what it looked like during hurricane recovery in Houston:

https://x.com/BBQBryan/status/1810509150842974308


This information from the app was never intended to help American citizens recover from a hurricane. Personally, as a data nerd, I love this. I love that immediate aid was given to people in Texas, and no one had to bother the electric company for updates, and it is just a clever repurposing of data.


MY DISCUSSION FOR YOU THIS WEEK:


  1. Here is a map of all Whataburger locations:

https://www.scrapehero.com/location-reports/Whataburger-USA/




What other natural disasters happen in the Whataburger portions of the country? Under what other circumstances could the poweroutage map help out Americans?


2. Think about our part of the country. What stores/restaurants could be used to track power outages in NW PA? If you want to talk about stores/restaurants in your hometown, go ahead and do so (just let us know where you are from).


3.Who else could benefit from this sort of data? Aside from individual people just trying to track power availability in their town, how else could this, or similar data, be used by larger organizations?


Popular posts from this blog

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr.  Molly Metz maintains a curated list of hilarious "How you doing?" scales.  2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.  So, I decided to create a post about three ways to use these scales in your stats classes:  1) Teaching the basics of variables. 2) Nominal vs. ordinal scales.  3) Daily check-in with your students.  1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created  a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/