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If you are writing a research grant, there are a lot of statistical issues that you need to consider. This guide, prepared by the American Statistical Association, highlights three areas: framing the problem, designing the study, and specifying the data analysis plan. It doesn’t talk enough about data management, but otherwise it is an excellent resource. Continue reading
This page is moving to a new website.
I am using pipes in R (the magrittr package) a lot recently. It reduces the number of errors due to nested functions, among other things. I’ve given a simple example before, and here’s another. Continue reading
I got in touch with a colleague from my days at Bowling Green State University. That was in the 1980s and my life had changed substantially since those days. While I have many professional biographies (such as this one from 2016), none of them covered the more personal aspects of my life. So here’s a brief biography for anyone who is interested in more than just where I worked. Continue reading
When you use R, you are using a program that is constantly evolving. The user-contributed packages are also evolving as well. Normally this is not that big a deal. But sometimes it is. Continue reading
Someone posted a question noting that most of the statistical consulting projects that they worked on finished in a reasonable time frame, a few were outliers. They took a lot longer and required a lot more effort by the statisticians. Were there any common features to these outliers they wondered. So they asked if anyone else had identified methodological features of projects that went overtime. I only had a subjective impression, but thought it was still worth sharing. Continue reading
I’m somewhat new to geocoding. One of the first things you might be interested in, if you have geographic data, is an indicator as to whether a certain address, zip code, or county is urban or rural. This is actually quite a complex topic. This paper outlines some of the basic systems to classifying a location as urban, rural, or something in between (e.g., suburban). Continue reading
This is a nice compilation of issues that you should be concerned. The examples are mostly from things that interest Google, but you will find this advice itself is useful no matter what type of data you work with. The advice is split into three broad categories: technical (e.g., look at your distributions), process (e.g., separate validation, description, and evaluation), and communication (e.g., data analysis starts with questions, not data or a technique). Continue reading
If you were on Mars for the past few days, you may not have noticed that Donald Trump has won the election. There has been a lot of commentary lately about how badly the predictions about the U.S. election have been and someone mentioned that even Nate Silver at the fivethirtyeight website had a predicted probability of a Clinton win at 71%. I wrote a brief comment that predicting an event with 71% probability does not mean that your prediction was “wrong” if the other event occurs. Continue reading
At the Joint Statistical Meetings this year, I learned a lot about recent developments in R, and not so recent developments that I was totally clueless about. One of those developments was the use of pipes in R. I wanted to show a simple example of how pipes can simplify your code. Continue reading
I received a suggestion for the Kansas City R Users Group to use screen sharing tools. I am going to experiment with this a bit. Here are two tools worth trying. Continue reading