Despite winter rain, I was delighted to head uptown last week to Skills Matter on the old Goswell Road for the first ever London Julia meetup. The first thing I learnt was that Julia’s friends are called Julians. If you … Continue reading →

I’ve recently been dabbling with parallel processing in R and have found the foreach package to be a useful approach to increasing efficiency of loops. To date, I haven’t had much of a need for these tools but I’ve started working with large datasets that can be cumbersome to manage. My first introduction to parallel

Prediction is difficult, especially of the future (Mark Twain) Let me start with two important premises. First of all, I am not into football so I do not support any team. Second, this post is just an opinion based on mathematics but football, as all of you know, is not an exact science. Football is

(The photo was from our first offering of R classes) We are going to offer our Data Science by R (beginner level) course again in February. The goal of this class is to get students to a point where they are self-sufficient in R, are proficient at analyzing data and can take these skills back to their full-time jobs....

Stephen tweets: Quilt Plots: A Simple Tool for the #Visualisation of Large Epidemiological Data buff.ly/1doSx4X— Stephen Rudd (@SAGRudd) January 15, 2014 Quilt plots. Sounds interesting. The link points to a short article in PLoS ONE, containing a table and a figure. Here is Figure 1. If you looked at that and thought “Hey, that’s a

Today I want to advocate weighted nonlinear regression. Why so? Minimum-variance estimation of the adjustable parameters in linear and non-linear least squares requires that the data be weighted inversely as their variances . Only then is the BLUE (Best Linear Unbiased Estimator) for linear regression and nonlinear regression with small errors (http://en.wikipedia.org/wiki/Weighted_least_squares#Weighted_least_squares), an important fact

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