# 2089 search results for "Regression"

## Oldies but Goldies: Statistical Graphics Books

March 1, 2014
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I just wanted to plug for three classical books on statistical graphics that I really enjoyed reading. The books are old (that is, older than me) but still relevant and together they give a sense of the development of exploratory graphics in general and the graphics system in R specifically as all three books were written at Bell Labs...

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## Using CART for Stock Market Forecasting

February 28, 2014
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There is an enormous body of literature both academic and empirical about market forecasting. Most of the time it mixes two market features: Magnitude and Direction. In this article I want to focus on identifying the market direction only. The goal I set myself, is to identify market conditions when the odds are significantly biased

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## “Statistical Models with R” Course – Milano, October 24-25, 2013

February 28, 2014
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"Statistical Models with R" CourseMarch 27 and 28, 2014 Course description This two-day course shows a wide variety of statistical models with R ranging from Linear Models (LM) to Generalized Linear Models (GLM) modelling, in order to provide a broad … Continue reading →

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## Foundations of Statistical Algorithms [book review]

February 27, 2014
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There is computational statistics and there is statistical computing. And then there is statistical algorithmic. Not the same thing, by far. This 2014 book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by

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## Example 2014.3: Allow different variances by group

February 27, 2014
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One common violation of the assumptions needed for linear regression is heterscedasticity by group membership. Both SAS and R can easily accommodate this setting. Our data today comes from a real example of vitamin D supplementation of milk. Four sup...

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## Easily generate correlated variables from any distribution

February 27, 2014
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In this post I will demonstrate in R how to draw correlated random variables from any distributionThe idea is simple.  1. Draw any number of variables from a joint normal distribution. 2. Apply the univariate normal CDF of variables to derive pro...

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## Quick and dirty notes on General Linear Mix Models

February 24, 2014
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My datasets tend to have random factors. I try to stick to general models whenever I can to avoid dealing with both random factors and complex error distributions (not always possible). I am compiling some notes here to avoid visiting … Continue reading →

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## Pimping your forest plot

February 23, 2014
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In order to celebrate my Gmisc-package being on CRAN I decided to pimp up the forestplot2 function. I had a post on this subject and one of the suggestions I got from the comments was the ability to change the default box marker to something else. This idea had been in my mind for a while and I therefore...

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## A survival guide to Data Science with R, from Graham Williams

February 21, 2014
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Graham Williams is the Lead Data Scientist at the Australian Taxation Office, and the creator of Rattle, an open-source GUI for data mining with R. (Check out some recent reviews/demos of Rattle on this blog here and here.) Dr Williams continues his many contributions to the R community with One Page R, a "Survival Guide to Data Science with...

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## Voting Twice in France

February 19, 2014
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$P_i\sim\mathcal{B}(N_i,p_i)$

On the Monkey Cage blog, Baptiste Coulmont (a.k.a. @coulmont) recently uploaded a post entitled “You can vote twice ! The many political appeals of proxy votes in France“, coauthored with Joël Gombin (a.k.a. @joelgombin), and myself. The study was initially written in French as mentioned in a previous post. Baptiste posted additional information on his blog (http://coulmont.com/blog/…) and I also wanted to post some lines of code,...

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