This week’s MakeoverMonday is a good one. Without further ado, the original visualization by Philip Bump, appearing in his Washington Post article entitled Nearly a quarter of Americans have never experienced the U.S. in a time of peace:
This graph triggered my pedagogical Pavlovian dog. Not just because it’s easy to malign the poor pie chart (of which this graph has 115!), but because I had a hunch that a redesign would reveal features of the data that are obscured above.
It’s been a while since I’ve posted! For a while I’ve been interested in joining the MakeoverMonday community, a group of data visualizers who come together each week to critique and redesign a visualization provided by Eva Murray and Andy Kriebel. I haven’t, due to busyness mostly, but this semester I finally took the dive and signed up! Call it a 2020 resolution. I hope that this will become a regular opportunity for me to keep developing my visualization critique and design skills.
Yesterday, I was preparing material for STAT 405 (biostatistics) I am teaching this spring, and was on the prowl for something that is an improvement upon the base R summary() function (it doesn’t even give standard deviations!). The ideal package would also improve upon the base R table() method, for which getting row and/or column percents is a huge pain. Base function xtabs() is great for getting arrays of contigency tables, but no percents.
This past semester I taught our STAT 370 (statistical consulting and communication) for the first time. This course gave student experience consulting for real clients from the university and community and focused on communicating with a client as well as report and presentation preparation best practices. Most of the required analyses were simple: paired t-tests, simple linear regression, etc. What struck me was the nontrivality of the data tidying process! While STAT 370 is taken mostly by our statistics majors, so many of the examples we encountered would be beautiful case studies for our introductory DSCI (data science) curriculum.
This July, my colleage Todd Iverson and I had the incredible opportunity to lead a pre-conference workshop at ICOTS 10 in Kyoto, Japan. Our workshop was titled Data visualization: best practices and principles using Tableau Public and Python.
Our workshop began by covering Leland Wilkinson’s grammar of graphics. Most data visualization software (Tableau, Python, R, JMP) employ some version of this grammar, and with a firm understanding it becomes easy to transition between them.
This post describes an activity I developed for Stat 310: Intermediate Statistics. This course is the second course on statistics at Winona State. I like to think of it as our “introduction to modeling” course, and this activity does just that: introduces students to the idea of a statistical model, including model assessment and fitting. The activity actually comes in two parts, administered at different times in the semester. In the first part, I am trying to get students to think about how to assess and compare proposed models using residuals.
Yesterday, several students and I traveled to St. Olaf to illustrate several uses of interesting, publicly-available data that can be used to investigate “social justice” issues. I’ve long been meaning to compile all the data sources and some example projects I’ve developed over the years, and this talk provided just the right motivation. Accordingly, I have compiled a list of some of my favorite data sources and example projects and student work.
Quick one here. As a statistics educator I am always on the lookout for interesting, real, digestable data that illustrate important statistical concepts. That’s a tall order!
One site that I visit again and again is this excellent repository hosted at University of Florida. Here’s the link. I regularly ping this website for classes ranging from intro stats to experimental design to regression analysis. Not only are they varied in scope and organized by topic, they also have brief descriptions and citations of original sources.
When temperatures hit 0°F in Minnesota, what better remedy than to head to Florida and talk data science curriculum! The 2-day workshop was held at the New College of Florida in Sarasota, FL. This post reflects some of the ideas circulated at the workshop that stood out to me.
Multivariate thinking and the introductory statistics and data science course: preparing students to make sense of a world of observational data (Nick Horton) In this talk, Dr.
The past two semesters of teaching our lower-level introductory statistics course here at WSU, I’ve incorporated in-class group homework. I could go on at length about why *I* think group homework is beneficial, but that’s not the point of this post. Rather, this is about what the students think. I think it’s quite striking!
First, though, I do need to provide a couple quick details about how I manage group homework.