# Introducing Rank Data Analysis with Arkham Horror Data

## Introduction

Last week I analyzed player rankings of the Arkham Horror LCG classes. This week I explain what I did in the data analysis. As I mentioned, this is the first time that I attempted inference with rank data, and I discovered how rich the subject is. A lot of the tools for the analysis I had to write myself, so you now have the code I didn’t have access to when I started.

# Introduction

Now here is a blog post that has been sitting on the shelf far longer than it should have. Over a year ago I wrote an article about problems I was having when estimating the parameters of a GARCH(1,1) model in R. I documented the behavior of parameter estimates (with a focus on $\beta$) and perceived pathological behavior when those estimates are computed using fGarch. I called for help from the R community, including sending out the blog post over the R Finance mailing list.

# Beyond Univariate, Single-Sample Data with MCHT

## Introduction

I’ve spent the past few weeks writing about MCHT, my new package for Monte Carlo and bootstrap hypothesis testing. After discussing how to use MCHT safely, I discussed how to use it for maximized Monte Carlo (MMC) testing, then bootstrap testing. One may think I’ve said all I want to say about the package, but in truth, I’ve only barely passed the halfway point!

# Materials for Teaching Applied Statistics

Today is the first day of the new academic year at the University of Utah. This semester I am teaching MATH 3070: Applied Statistics I, the fourth time I’ve taught this course.

# Replication Intervals

At the University of Utah I’ve taught MATH 1070 and MATH 3070. Both are introductory statistics classes, but I call MATH 1070 “Introductory Statistics for People Who Don’t Like Math” while MATH 3070 is “Introductory Statistics for People Who Do Like Math”, since the latter requires calculus and uses far more probability. In both classes, though, students need to learn what confidence intervals (CIs) say and don’t say, and I spend a lot of time debunking common misconceptions for what a confidence interval says.

# Learn Basic Python and scikit-learn Machine Learning Hands-On with My Course: Training Your Systems with Python Statistical Modelling

This post is actually months late, but like with my last video course announcement, it’s better late than never. And besides, of my video courses, I had the most fun writing this one.

# Where to Go from Here? Tips for Building Up R Experience

At the University of Utah, I teach the R lab that accompanies MATH 3070, “Applied Statistics I.”” None of my students are presumed to have any programming experience, and they never hesitate to remind me of that fact, especially when they are starting out. When I create assignments and pick problems, I often can write a one- or three-line solution in thirty seconds that students will sometimes spend four hours trying to solve. They then see my solution and slap their foreheads at its simplicity. I can be tricky with my solutions. For example, suppose you wish to find the sample proportion for a certain property. A common approach (or at least the one used in the textbook our course uses, Using R for Introductory Statistics by John Verzani) looks like this: