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,...
Aaron Koblin’s Sheep Market visualization is an awesome use of Mechanical Turk. But it’d be even more awesome if the grid were ordered, so inspired by the use of eigenfaces in facial recognition, I decided to try projecting the sheep … Continue reading →
When the sample sizes within the levels of our independent variables are not equal, we have to handle our ANOVA differently than in the typical two-way case. This tutorial will demonstrate how to conduct a two-way ANOVA in R when the sample sizes withi...
When the sample sizes within the levels of our independent variables are not equal, we have to handle our ANOVA differently than in the typical two-way case. This tutorial will demonstrate how to conduct a two-way ANOVA in R when the sample sizes withi...
We have seen yesterday that finding an optimal strategy to publish is not that simple. And actually, it can be even more difficult in the case the journal rejects the paper (not because it is not correct, but because "it does not fit" with the stan...
I have been sitting on this post for some time now and wanted to get it out there. The goal is to simply show how easy it is to pull live data from the web into R, massage it, and perform some analytics on it. I am not sure how useful this analysis really is 
ast year I posted an animation created in R to celebrate the new year, and this year I’ve got a more fabulous animation. Unfortunately our Lord of CRAN (Kurt) has been out of office for several days, so I’m unable to publish my animation package on CRAN as scheduled. Anyway, for those who are curious
In a recent post (here, by @teramonagi), Teramonagi mentioned the use of PCA to model yield curve, i.e. to obtain the three factor, "parallel shift", "twist" and "butterfly". As in Nelson & Siegel, if m is maturity, is the yield of the cu...
Describing multivariate data is not easy. Especially, if you think that statisticians have not developed any new tools after the ANOVA and principal component analysis (PCA). For social and experimental scientists the most important new technique are structural equation models that combine measurement models (that substitute reliability analysis and PCA) and structural models (that substitute
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
