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.

One of my favorite upper-level elective courses to teach is Positive Psychology. I recently came across a comprehensive account of various facets of how positive psychology can be assessed in nations:  https://ourworldindata.org/happiness-and-life-satisfaction . Like, the website is just great. Below is an example of the data you can explore, in various formats, animation options, and you can download the data. It is great! From this website, I download loaded and compiled two data sets that caputure GDP, Cantrill Ladder Score, and life span data for hella countries. You can perform a variety of significant and non-significant correlations and regressions using this data. Additionally, the countries are divided into six regions, allowing you to conduct some one-way ANOVAs with your students.  Here is the data, compiled by my awesome RA, Maddie:  https://docs.google.com/spreadsheets/d/129NQcPdFwZjyzZAJdX6odKC7KiFk_Q1Lqa-SD4kk5FQ/edit?usp=sharing

Many thanks to Dr. Sara Appleby for sharing this data with me!! I really enjoy silly data, like this  one from Jayson Jenks, writing for  The Athletic,  which shows how many Uncrustables each team eats per  week. Well, data from the teams that elected to participate and/or didn't make their own PB and Js. The whole article is fun, so give it a read. It makes sense that hungry athletes would go for a quick, calorie-dense, nostalgic snack containing protein.  Here is the data visualization:  Damn, Denver.  I entered this data into a spreadsheet for all of us. Spoiler alert: The number of Uncrustables eaten per week does not vary by league (independent t -test example), and the number of wins in 2023 does not correlate with the number of Uncrustables eaten per week in 2023 (correlation/regression example). Also, for my own curiosity, I re-ran the data after deleting Denver, and it wasn't enough of a difference to achieve significance.  

We need more nonsignificant but readily understood examples in our classes. This correlation/regression example from Information is Beautiful  demonstrates that the calories in delicious caffeinated drinks do not correlate with the calories in the drink. Caffeine has zero calories. The things that make our drinks creamy and sweet may have calories. Easy peasy, readily understood, and this example gives your students a chance to think about and interpret non-significant, itty-bitty effect size findings.  Click here for the data. Aside: Watch your language when using this example. We need calories to stay alive and none of these drinks, in and of themselves, are good or bad. Our students are exposed to way too much of that sort of language and thinking about food and their bodies. What they choose to drink or eat is none of our business. When I share this visual, I omit the information on the far right (exercise) and far left (calorically equivalent foods). It distracts from the...

  The Science of Scare project, conducted by MoneySuperMarket.com, recorded heart rates in participants watching fifty horror movies to determine the scariest of scary movies. Below is a screenshot of the original variables and data for 12 of the 50 movies provided by MoneySuperMarket.com: https://www.moneysupermarket.com/broadband/features/science-of-scare/ https://www.moneysupermarket.com/broadband/features/science-of-scare/ Here is my version of the data in Excel format . It includes the original data plus four additional columns (so you can run more analyses on the data): -Year of Release -Rotten Tomato rating -Does this movie have a sequel (yes or no)? -Is this movie a sequel (yes or no)? Here are some ways you could use this in class: 1. Correlation : Rotten Tomato rating does not correlate with the overall scare score ( r = 0.13, p = 0.36).   2. Within-subject research design : Baseline, average, and maximum heart rates are reported for each film.   3. ...

I have shared  AMPLE examples for teaching correlations . Because I've got you, boo. Like, I have shared days' worth of lecture material with you, my people. I am adding one more example. I have used this example in my positive psychology course for years, and it really illustrates what can happen en masse when marketing departments and less-savory pop-psych elements try to establish causal relationships with features (stereotypes?) of happy countries and individuals' subjective well-being. I like this one because it is math-free, UG-accessible, and not terribly long. Joe Pinsker, writing for the Atlantic, argues that... https://www.theatlantic.com/family/archive/2021/06/worlds-happiest-countries-denmark-finland-norway/619299/ TL;DR: Just because Northern European nations consistently score the highest on global happiness data doesn't mean that haphazardly adopting practices from those countries will make you happy. Correlation doesn't equal causation. H ere is the ...

  The unstoppable pop of Taylor Swift (reuters.com) Here are some ideas for using this to teach statistics: Data visualizations and visualization guides: With cats, y'all. And the Taylor Swift handwriting font. I love the whole vibe of this as well as how they explain their data visualizations. Operationalizing things: The page describes three Spotify metrics for music: Acousticness, danceability, and emotion. The data visualization contains a numeric value for each metric and a description of the metric's meaning. DATA!: Okay. This is an excellent example of things already. And it is delightful. Then I thought, "Oh, wouldn't it be fun if this was in spreadsheet form!" (I think that A LOT, friends). But, as I write a book and my syllabi, I don't have time for that,  BUT A REDDITOR DID HAVE TIME FOR THAT . Dr. Doon created a spreadsheet with 18 columns of Spotify data for each son. It doesn't include the Midnights data but is still a fantastic amount of dat...

I think it is important to emphasize how regression can be used to make future predictions using trends in existing data. Most psychology books use psychology examples to illustrate this, which makes sense. Still, I think explaining how regression is widely used in business to make financial decisions, and predictions is important. But that can be boring. But I found one example that uses the Marvel Comic Universe to do this. I already blogged about this , but I'm sharing exactly how I used this in class presently. ASIDE: This data is being regularly updated! Here is a Google Drive folder with 1) my version of the data (CSV and I turned all the percentages to decimal points for JASP) and 2) my PPT . Which includes photos of the scientists of the MCU. ALSO: While your students are doing their exercise, totes play the soundtrack from Guardians of the Galaxy. Do it. 

Conflict of interest statement: I am slightly shorter than the average American woman. But I'm adorable, so I score low on the Dark Triad?? This blog post started with me giggling at click-bait headlines, but THEN I realized this is one of those rare articles that use data analyses that we teach in Psych Stats. The journey began when I saw this on Twitter: Hilarious, right? Not to be outdone, the NY Post ALSO needed to cover this study:   https://www.google.com/amp/s/nypost.com/2023/02/27/short-people-more-likely-to-be-psychopaths-study/amp/ I'm wheezing. Immediately, this was a great example of clickbait reporting. The research used The Dark Triad as the theoretical underpinning, and The Dark Triad is like what Mindfulness was 10 years ago in psych research. It is just everywhere. BUT...then I realized this is a very easy-to-read study that you could share with advanced UGs, no problem. What does the original research state? https://www.sciencedirect.com/science/article/pii/S...

I love InformationIsBeautiful . They created my favorite data visualization of all tim e.  They also created an interactive scatterplot with all sorts of information about Marvel Comic Universe  films. How to use in class: 1. Experiment with the outcome variables you can add to the X and Y axes: Critical response, budget, box office receipts, year of release, etc. There are more than that; you can add them to either the X or Y axes. So, it is one website, but there are many ways to assess the various films. 2. Because of interactive axes, there are various correlation and regression examples. And these visualizations aren't just available as a quick visual example of linear relationships...see item 3... 3. You can ask your students to conduct the actual data analyses you can visualize because  the hecking data is available . 4. The website offers exciting analyses, encouraging your students to think critically about what the data tells them. 5. You could also squeeze Simp...

ASIDE: THIS IS MY 500th POST. PLEASE CLAP. Efficacy data about a mental health intervention? Yes, please. The example has so much potential in a psych stats classroom. Or an abnormal/clinical classroom, or research methods. Maybe even human factors, because three numbers are easy to remember than 10? This post was inspired by an NPR story  by Rhitu Chatterjee. It is all about America's mental health emergency hotline's switch from a 10-digit phone number to the much easier-to-remember three digits (988), and the various ways that the government has measured the success of this change. How to use this (and related material) in class: 1) Assessment. In the NPR interview, the describe how several markers have improved: Wait times, dropped calls, etc.  Okay, so the NPR story sent me down a rabbit hole of looking for this data so we can use it in class. Here is the federal government's website about  988  and a link to their specific  988  performance data,...

Stats nerds: I'm warning your right now. This website is a rabbit hole for us, what with the interactive, customizable data visualizations. Please don't click on the links below if you need to grade or be with your kids or drive.  At a recent conference presentation, I was asked where non-Americans can find examples like the ones I share on my blog. I had a few ideas (data analytic firms located in other countries, data collected by the government), but wanted more from my answer.  BUT...I recently discovered this interactive from Our World in Data. It visualizes international data on human height, y'all  with so many different examples throughout. I know height data isn't the sexiest data, but your students can follow these examples, they can be used in a variety of different lessons, and you can download all of the data from the beautiful interactive charts. 1. Regressions can't predict forever. Trends plateau.  I'm using this graph to as an example of how a r...

Not all of our class examples should reject the null. Sometimes, you just need some non-significant data, small effect size data that doesn't detect a linear relationship. Such is the linear relationship between the number of calories and mg of caffeine in these 29 different treats provided by InformationIsBeautiful. InformationIsBeautiful provides that data , as do I .

 I sneak attack my students with this correlation example. I ask them to analyze this data as a correlation and create a report describing their data. This is what the data looks like: I'll be honest, I mostly do this for my own amusement. HOWEVER: It does demonstrate that scatter plots are helpful when making sure that a correlation analysis/scatter plot may contain a non-linear relationship (see: Datasaurus ). If you want to make your own silly scatter plot for data analysis, I recommend Robert Grant's DrawMyData website for doing so.

Sometimes, you just need a new, simple example for a homework question or a class warm-up.   I eyeballed and entered the   data here  ( r   = -.55). Enjoy. I use this little example to explain to use the regression formula to make a prediction. Here are my slides .

Ok, so this from Reddit: This, of course, inspired me to cook up an example for Psych Stats, in the catty spirit of this very judgmental regression about the life and love of Dennis Quaid . Here is a Google Sheet that contains ALL of the data featured above, as well as a sheet that contains JUST the GF's age when they first started dating. Maybe this example is a little better for our younger students who haven't heard of Dennis Quaid. Anyway, enjoy.

 You have to walk before you can run, right? The basics we teach in Psych Stats help our students walk, but they are not typical of published psychology research. It is difficult for Psych Stat instructors to find good examples of our analyses in recently published research (for an exception, check out Open Stats Lab ). A recent publication caught my eye because I love sending people mail ( scroll down to find my list of recommended, envelope-friendly surprises ).  Liu, P. J., Rim, S., Min, L., & Min, K. E. (2022). The surprise of reaching out: Appreciated more than we think. Journal of Personality and Social Psychology , No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/pspi0000402 Spoiler alert: People love being surprised by mail. Like, more than the sender thinks the receiver will be surprised. I was delighted to discover that this interesting paper consists of multiple studies that use what we teach in Psych Stats. Check out this article s...

TL;DR: This cool, interactive website asks you to participate in a replication. It also explains how a researcher decision on how to define "randomness" may have driven the main effect of the whole study. There is also a scatter plot and a regression line, talk of probability, and replication of a cognitive example. Long Version:  This example is equal parts stats and RM. I imagine that it can be used in several different ways: -Introduce the replication crisis by participating in a wee replication -Introduce a respectful replication based on the interpretation of the outcome variable  -Data visualization and scatterplots -Probability -Aging research Okay, so this interactive story from The Pudding is a deep dive into how one researcher's decision may be responsible for the study's main effect.  Gauvrit et al. (2017 ) argue that younger people generate more random responses to several probability tasks. From this, the authors conclude that human behavioral complexity...

Sometimes, I post whole class lessons. Sometimes, I post short little example nuggets. Today I share the latter.  This one is a brief, easy-to-understand example of why AI only learns what we teach it and how even a smarty pants computer can get a little confused about correlations and what they mean. A great way to introduce ML, AI, problems with both, and even discuss correlation and predictions and regression. https://twitter.com/hoalycu/status/1507770891786096643...in my head, I imagine that AI was just judging comic-sans font. The text in this tweet was from a MIT Technology Review article by WIll Douglas Heaven: https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/ If you want to go deeper with this example, I strongly recommend reading Dr. Cat Hicks's thread about this post:  https://twitter.com/grimalkina/status/1508095358693302275 .

Many thanks to my colleague, Andrew Caswell, for sharing this Reddit post with me: https://www.reddit.com/r/dataisbeautiful/comments/s75sm7/oc_us_life_expectancy_vs_of_taco_bell_locations/ So, this alone is an excellent example of correlation and the third variable problem. But...more delightfully, the Redditor who created this graph also shared where he found this data (https://www.nicerx.com/fast-food-capitals/, https://worldpopulationreview.com/state-rankings/life-expectancy-by-state). BETTER STILL: I downloaded and organized all of the fast-food data and mortality data and put it in one spreadsheet for you all. Do All The Correlations! Teach your students about Bonferroni corrections! Figure out the fast-food restaurant that correlates the most strongly with mortality!   PS: Did you know that there is an option to download data from a website in Excel?  The fast-food data was presented in an embedded, scrolly table, and that Excel option made it easy-peasy to do...