On Saturday August 25, 2018 at 8:08 PM I finally hit Inbox Zero!
I did it by immediately copying the snippets of email I wanted into Evernote notes (my notetaking system). I also attended to to-dos more immediately, or added them to a note of “To-Dos”.
I doubt I will stay at zero emails in my inbox, but I have been at fewer than 10 emails all summer.
I am giving a talk at the 46th Annual Meeting of the Statistical Society of Canada in Montreal on June 05, 2018. The talk is part of an invited session on Teaching Statistics to Graduate Students in the Health and Social Sciences. Information, including the slides, is available below.
Title: Statistical Computing: Non-Ignorable Missingness in the Graduate-Level Social Science Curriculum
Abstract: In 2010, Nolan and Temple Lang pointed out that "computational literacy and programming are as fundamental to statistical practice and research as mathematics".
R Markdown is a great way to integrate R code into a document. An example of the default theme used in R Markdown HTML documents is shown below.
Pre-Packaged Themes There are several other canned themes you can use rather than the default theme. There are 12 additional themes that you can use without installing any other packages: “cerulean”, “cosmo”, “flatly”, “journal”, “lumen”, “paper”, “readable”, “sandstone”, “simplex”, “spacelab”, “united”, and “yeti”.
It feels like this spring has been especially terrible weather-wise. We have gotten a lot of snow and it has been cold. To evaluate whether this is the case or whether I have hindsight bias, I pulled some historical weather data for the month of April from Weather Underground.
library(dplyr) library(forcats) library(ggplot2) library(ggridges) library(readr) library(viridis) # Read in data april = read_csv("~/Documents/github/Public-Stuff/data/april-weather.csv") # Filter dates april = april %>% filter(date <= 11) I grabbed data back to 2008 (avialable at https://raw.
This post is the fourth (and last) in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions that asked me for predictions about the future of statistics teaching and statistics education research.
Before I get into the Q&A, let me just state: Prediction is hard. Leland Wilkinson in The Future of Statistical Computing reminded us of this when he cited a prediction about computers that Andrew Hamilton made in a 1949 issue of Popular Mechanics
My colleague Robert delMas is teaching our doctoral-level research seminar, EPsy 8271 next fall, and it looks to be an interesting topic. The details about the course follow:
EPSY 8271 | Statistics Education Research Seminar: Teaching Statistics from a Modeling Perspective (3 credits) Day/Time: Fridays, 9:00 a.m.–11:30 a.m. (Fall 2018)
Location: 220 Wulling Hall Instructor: Robert delMas, Ph.D.
This seminar will focus on research related to teaching introductory statistics through a modeling approach.
This post is the third in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions related to teaching.
With a flipped classroom, the professor tapes their lecture and has students view the video online. I think the logical extreme of this is that at some point, certain lectures will become immensely popular or polished to the point of surpassing local professors’ lectures.
This post is the second in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions related to several of the projects I have worked on over the years.
Can you tell us a little about your AIMS project? I think it’s not always easy for everyone to apply guidelines in order to change their behavior (in any context), but your AIMS project seems to fill that gap for stats educators-that is, it explicitly creates educational materials aligned with GAISE.
This last Tuesday (03/27/2018) I was invited to go do a Q&A with the students in the EPsy 5271 Becoming a Teacher of Statistics course. This class also met via video-link with a similar course at Penn State. The students asked thoughful (sometimes difficult) questions and I tried to answer them. I asked them if I could blog out their questions and my responses and they kindy said “yes”. Rather than respond to all of them at once, I thought I would use this opportunity to create several blogposts.
In two previous posts, post 1 and post 2, I used Monte Carlo simulation to predict the winner of the 2018 Minnesota State High School Boys Hockey tournament. Now that the tournament is over, I wanted to analyze how the model did and also think about ways to improve the predictions should I want to re-run such a simulation in the future.
Accuracy of the Predictions So, how well did the simulation do in predicting the state tournament champion?