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 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.