My last four posts have dealt with boxplots and some useful variations on that theme. Just after I finished the series, Tal Galili, who maintains the R-bloggers website, pointed me to a variant I hadn’t seen before. It's called a bee...

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Applied R for the quantitative social scientist is a manual on R written specifically as an introduction for the quantitative social scientist. To my opinion, R-Project is a magnificent statistical program, ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a sense of closeness...

While I had mentioned in my last post that I will cover logistic regression in my next post, I decided that a quick interlude in working with bubble plots would be fun. Bubble plots have become pretty popular recently, especially with all of the Visualization Challenges I've seen around the internet (by the way, I...

While I had mentioned in my last post that I will cover logistic regression in my next post, I decided that a quick interlude in working with bubble plots would be fun. Bubble plots have become pretty popular recently, especially with all of the Visualization Challenges I've seen around the internet (by the way, I...

Introduction Neil Kodner recently got me interested again in analyzing Canabalt scores statistically by writing a great post in which he compared the average scores across iOS devices. Thankfully, Neil’s made his code and data freely available, so I’ve been revising my original analyses using his new data whenever I can find a free minute.

In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...

In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...

I received an email today with the following comment: I’m using ARIMA with Intervention detection and was planning to use your package to identify my initial ARIMA model for later iteration, however I found that sometimes the auto.arima function returns a model where AR/MA coefficients are not significant. So my question is: Is there a