I’m interested in Python a lot, mostly because it appears to be wickedly fast. The downside is that I don’t know it nearly as well as R, so any speed gain in computation time is more than offset by Google … Continue reading →

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,...

So I have been having difficulty getting my Stata code to look the way I want it to look when I post it to my blog. To alleviate this condition I have written a html encoder in R. I don't know much about html so it is likely to be a little ...

If you’re a regular reader of my blog you’ll know that I’ve spent some time dabbling with neural networks. As I explained here, I’ve used neural networks in my own research to develop inference into causation. Neural networks fall under two general categories that describe their intended use. Supervised neural networks (e.g., multilayer feed-forward networks)

Data mining techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Random Forest, and Logistic Regression are “most commonly used for predicting a specific outcome such as response / no-response, high / medium / low-value customer, likely to buy / not buy.”1 In this article, we will demonstrate how to use R

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