Collinearity, or excessive correlation among explanatory variables, can complicate or prevent the identification of an optimal set of explanatory variables for a statistical model. For example, forward or backward selection of variables could produce inconsistent results, variance partitioning analyses may be unable to identify unique sources of variation, or parameter estimates may include substantial amounts 
(see John Wilkins’ article on the – interesting – history of that phrase http://scienceblogs.com/evolvingthoughts/…). We will see, this week in class, several smoothing techniques, for insurance ratemaking. As a starting point, assume that we do not want to use segmentation techniques: everyone will pay exactly the same price. no segmentation of the premium And that price should be related to...
A couple of weeks ago, I posted about how much we can expect to learn about the state of the world on the basis of a statistical significance test. One way of framing this question is: if we’re trying to come to scientific conclusions on the basis of statistical results, how much can we update 
Let's get back to the age-value relationship from my last post. I did some more plotting to see on which position this inversed U-shaped relationship is strongest. Please note, that I use a dataframe called eu.players throughout this post, which holds ...
Following the roundabout tour of F1Stats – A Prequel to Getting Started With Rank Correlations, here’s a walk through of my attempt to replicate the first part of A Tale of Two 
This morning, Stéphane asked me tricky question about extracting coefficients from a regression with categorical explanatory variates. More precisely, he asked me if it was possible to store the coefficients in a nice table, with information on the variable and the modality (those two information being in two different columns). Here is some code I did to produce the...
It's been some time since my last post on football. And we're talking about european soccer here.So I finally managed to write some functions which allow me to extract player stats from www.transfermarkt.de. The site tracks lots of stats in the world o...
In my last post I showed how one can easily summarize the outcome of a logistic regression. Here I want to show how this really depends on the data-points that are used to estimate the model. Taking a cue from the evolution of a correlation I have plotted the estimated Odds Ratios (ORs) depending on
the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on th...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
