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

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...

 I enjoy fun ways to refer to the fact that regressions can't be predicted forever. Like, trends have to stop, right? Here is a v. recent one: Thank you, @ronburke! Thank you,  @RomanFolw · https://www.nature.com/articles/431525a/figures/1

  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 .

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 .

 There are already some pretty cool games for guessing linear relationships/regression lines. Dr. Hill's Eyeball Regression Game . The old, reliable Guess the Correlation game. However, I found a new one that has a particularly gorgeous interface, and a few extra features to help your learners. History of Data Science created the Regression game . It provides the player with a scatter plot, then the player needs to guess the y-intercept and slope. See that regression line? It is generated and changes as the entered a and b values change, which is a good learning tool. If played at the "easy-peasy" level, the player can even change those numbers multiple times over the course of 30 seconds, and watch as the corresponding line changes.  I think this game is a nice way to break up the ol' regression lecture and allows students to see the relationship between the scatter plot and the regression line.

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.

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...

 Sophie Hill created a great game that shows students how to "eyeball" regression lines (or just lines) by guessing the y-intercept and the slope.  At the beginning of the game, you get a scatter plot. Then, you need to guess the y-intercept and the slope.   Once you make a guess, it will show you the actual line of best fit...and your line, along with residuals and mean squared error. So, this doesn't just allow for eyeballing the regression line but also how to test the fit of a line. P.S.: If you liked this, you'd love the Guess the Correlation game.

Niv Elis, writing for The Hill, summarized a report created by JP Morgan analyst Jesse Edgerton. The report found a link between in-restaurant spending from three weeks ago and increases in new cases of COVID-19 in different states now. Data for the analysis came from 1) J.P. Morgan/Chase in-restaurant (not online/takeout) credit card purchases and 2) infection data from Johns Hopkins.  How to use in class: 1. Correlation/regression: This graph, which summarizes the main findings from the report, may not include my beloved APA axis labels, but it does include an R2 and is a good example of a scatterplot.  ALSO: The author of The Hill piece was careful to include this information from the study's author, which clarifies that correlation doesn't necessarily equal causation. 2) Creativity in data analysis: Often, in intro psych stats, we use examples rooted in traditional social science research. We should use such an example. But we MUST also use examples that demonstrate how d...

I started this blog with the hope of making life easier for my fellow stats instructors. I share examples and ideas that I use in my own classes in hopes that some other stats instructor out there might be able to incorporate these ideas into their classes. As we crash-landed into the online transition last Spring, I created took some of the blog posts and made them into lengthier class lessons, including Google Slides and, when applicable, data sets shared via my Google Drive. I ended up with four good lessons about the four big inferential tests typically cover in Psych Stats/Intro Stats: T-test, ANOVA, chi-square, and regression. I think these examples serve as great reviews/homework assignments/an extra example for your students as they try to wrap their brain around statistical thinking. As we are preparing for the Fall, and whatever the Fall brings, I wanted to re-share all of those examples in one spot. Love, Jess ANOVA https://notawfulandboring.blogspot.com/2020/04/online-day-6...

Hi! Here is an easy-to-use review of regression, with a regression example. I posted about using this catty tweet to teach regression previously: Right? Do you see the regression? Every wife has two data points: Year she gets married, and the year she was born...and from that, you can perform a regression to predict when Quaid would actually marry someone who has not been born yet (2052). Well, I decided to make it into a whole Google Slides presentation for this example, with links to data, to use as a regression review during the quarantine. Admittedly, the example is ridiculous, and the sample is far too small to run a reliable regression. That being said, I think the example is vivid and sticks. I also think it does an excellent job of illustrating how the equation can be used to make predictions. Additionally, I genuinely find meaning in helping out my fellow statistics instructors in good times, and doubly so during this challenging semester. Feel free to view my pres...

This hilarious quip made me think of regression.   So I created a wee data set ( available here ): It features this scatter plot of the data (r = .99). It also includes JASP output of the regression for this data (a person born in 2020 is predicted to marry Dennis Quaid in 2052).