R Data Analysis Examples: Logit Regression
Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. This page uses the following packages. Make sure that you can load them before trying to run the examples on this page. If you do not have a package installed, run: install.packages("packagename"), or if you see the version is out of date, run: update.packages(). library(aod)library(ggplot2)
Finding the Best Subset of a GAM using Tabu Search and Visualizing It in R
The famous probabilist and statistician Persi Diaconis wrote an article not too long ago about the "Markov chain Monte Carlo (MCMC) Revolution." The paper describes how we are able to solve a diverse set of problems with MCMC. The first example he gives is a text decryption problem solved with a simple Metropolis Hastings sampler. I was always stumped by those cryptograms in the newspaper and thought it would be pretty cool if I could crack them with statistics. So I decided to try it out on my own. The example Diaconis gives is fleshed out in more details by its original authors in its own article.

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Released 15th March, 2016 Windows Windows XP and above Mac OS X
Beamer theme gallery
Welcome to the beamer (latex) theme gallery. Every time I wanted to pick a theme for a presentation I ended up spending a lot of time. That was because I didn't know the themes and for each one I had to recompile the whole presentation and see how it looked. I searched for samples of those themes but found none. That's why I did this.
The caret Package
This page shows a network diagram of all the models that can be accessed by caret's train function. See the Revolutions blog for details about how this visualization was made (and has this page has updated code using thenetworkD3 package). In summary, the package annotates each model by a set of tags (e.g. "Bagging", "L1 Regularization" etc.).

Formulae in R: ANOVA and other models, mixed and fixed
R’s formula interface is sweet but sometimes confusing. ANOVA is seldom sweet and almost always confusing. And random (a.k.a. mixed) versus fixed effects decisions seem to hurt peoples’ heads too.
Free Statistical Software
Updated 02/16/2016 -- OpenStat removed. Hope to se you again soon Updated 03/14/2016 -- Added Marko Lucijanic's Excel worksheet to perform the Log Ranks test on survival data. This page contains links to free software packages that you can download and install on your computer for stand-alone (offline, non-Internet) computing. General Packages: No package does everything, but these programs support a wide variety of statistical analyses. Subset Packages: Each of these programs deals with a specific area of statistics (such as power analysis or mulitvariate analysis), or carries out a specific test or computation.

LeaRning Path on R - Step by Step Guide to Learn Data Science on R
One of the common problems people face in learning R is lack of a structured path. They don’t know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time. To create this R learning path, Analytics Vidhya and DataCamp sat together and selected a comprehensive set of resources to help you learn R from scratch. This learning path is a great introduction for anyone new to data science or R, and if you are a more experienced R user you will be updated on some of the latest advancements.
Linear Algebra Examples
Now we are ready to see how matrix algebra can be useful when analyzing data. We start with some simple example and eventually get to the main one: how to write linear models with matrix algebra notation and solve the least squares problem. To compute the sample average and variance of our data we use these formulas ˉY=1NYi and var(Y)=1N∑Ni=1(Yi−ˉY)2. We can represent these with matrix multiplication.

Using Google Analytics with R - ThinkToStart
For the most part, SMB’s tend to utilize free analytics solutions like Google Analytics for their web and digital strategy. A powerful platform in its own right, it can be combined with the R to create custom visualizations, deep dives into data, and statistical inferences. This article will focus on the usage of R and the Google Analytics API. We will go over connecting to the API, querying data and making a quick time series graph of a metric. To make an API call, you’ll need two things. A Client ID and a Secret ID.