2206 search results for "regression"

Guidebook for growth curve analysis

March 3, 2014
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Guidebook for growth curve analysis

I don't usually like to use complex statistical methods, but every once in a while I encounter a method that is so useful that I can't avoid using it. Around the time I started doing eye-tracking research (as a post-doc with Jim Magnuson), people were ...

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Exploratory data analysis techniques

March 3, 2014
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Exploratory data analysis techniques

In my previous blog post I have explained the steps needed to solve a data analysis problem. Going further, I will be discussing in-detail each and every step of Data Analysis. In this post, we shall discuss about exploratory Analysis.What is Exploratory Analysis?“Understanding data visually”Exploratory Analysis means analyzing the datasets to summarize their main characteristics,...

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Predicting movie ratings with IMDb data and R

March 2, 2014
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Predicting movie ratings with IMDb data and R

It’s Oscars season again so why not explore how predictable (my) movie tastes are. This has literally been a million dollar problem and obviously I am not gonna solve it here, but it’s fun and slightly educational to do some number … Continue reading →

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Simple Pharmacokinetics with Jags

March 2, 2014
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Simple Pharmacokinetics with Jags

In this post I want to analyze a first order pharmocokinetcs problem: the data of study problem 9, chapter 3 of Rowland and Tozer (Clinical pharmacokinetics and pharmacodynamics, 4th edition) with Jags. It is a surprising simple set of data, but still there is enough to play around with.Data, model and analysisThe data is simple enough. One subject's concentration...

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Oldies but Goldies: Statistical Graphics Books

March 1, 2014
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Oldies but Goldies: Statistical Graphics Books

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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Using CART for Stock Market Forecasting

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” Course – Milano, October 24-25, 2013

"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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Foundations of Statistical Algorithms [book review]

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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Example 2014.3: Allow different variances by group

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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Easily generate correlated variables from any distribution

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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