We often use logistic regression models in our analyses and we also often need to publish the results in table format. And, we always use MS Word since this is our standard office in our department. So I thought about … Weiterlesen →

I was recently contacted by a reader with two very specific questions and I thought that this would be a good topic to publicity respond to. He would like to simulate his data:I have firm level data and the model is discrete choice with the main expla...

Except for maybe the t test, a contender for the title “most used and abused statistical test” is Pearson’s correlation test. Whenever someone wants to check if two variables relate somehow it is a safe bet (at least in psychology) that the first thing to be tested is the strength of a Pearson’s correlation. Only if that doesn’t...

Maximum-Likelihood Estimation (MLE) is a statistical technique for estimating model parameters. It basically sets out to answer the question: what model parameters are most likely to characterise a given set of data? First you need to select a model for the data. And the model must have one or more (unknown) parameters. As the name

Those that do a lot of nonlinear fitting with the nls function may have noticed that predict.nls does not have a way to calculate a confidence interval for the fitted value. Using confint you can obtain the error of the fit parameters, but how about the error in fitted values? ?predict.nls says: “At present se.fit

Iin insurance pricing, the exposure is usually used as an offset variable to model claims frequency. As explained many times on this blog (e.g. here), and in my notes, if we have to identical drivers, but one with an exposure of 6 months, and the other one of one year, it should be natural to assume that, on average,...

e-mails with the latest R posts.

(You will not see this message again.)