Ramsay and Silverman’s Functional Data Analysis is a tremendously useful book that deserves to be more widely known. It’s full of ideas of neat things one can do when part of a dataset can be viewed as a set of 
184 search results for "Anova"
Functional ANOVA using INLA
First impressions of Doing Bayesian Data Analysis
About a month ago I was discussing the approach that I would like to see in introductory Bayesian statistics books. In that post I mentioned a PDF copy of Doing Bayesian Data Analysis by John K. Kruschke and that I … Continue reading →
Blog Statistics with StatCounter & R
If you're interested in analysing your blog's statistics this can easily be done with a web-service like StatCounter (free, only registration needed, quite extensive service) and with R.After implementing the StatCounter script in the html code of a we...
Statistics with R – Lots of R Examples
“Statistics with R” is a great R graphics & stats website. It provides lots of R examples, covering many analytics topics. It is also available as a PDF document to download at the website, as well as the R codes. … Continue reading →
Bayesian modeling using WinBUGS
Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the more recent Bayesian 
Migrating from SPSS/Excel to R, Part 3: Preparing your Data
In this post, I describe how to prepare your data for migrating between SPSS/Excel and R. This is the third …
Longitudinal analysis: autocorrelation makes a difference
Back to posting after a long weekend and more than enough rugby coverage to last a few years. Anyway, back to linear models, where we usually assume normality, independence and homogeneous variances. In most statistics courses we live in a … Continue reading →
Teaching with R: the switch
There are several blog posts, websites (and even books) explaining the transition from using another statistical system (e.g. SAS, SPSS, Stata, etc) to relying on R. Most of that material treats the topic from the point of view of i- … Continue reading →
Linear mixed models in R
A substantial part of my job has little to do with statistics; nevertheless, a large proportion of the statistical side of things relates to applications of linear mixed models. The bulk of my use of mixed models relates to the … Continue reading →
Assumptions of the Linear Model
Linear Assumptions from the Analysis Factor – Assumptions of linear regression (and ANOVA) are about the residuals, not the normality or independence of the response variable (Y). If you don’t know what this means be sure to read this brief … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
