283 search results for "anova"

Generalized Linear Mixed Models in Ecology and in R

March 12, 2014
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Generalized Linear Mixed Models in Ecology and in R

I had a nice workshop two weeks ago in Tübingen (south-germany) concerning Generalized Linear Mixed Models (GLMM) in R. The course was given by two ecologist: Dr. Pius and Fränzi Korner-Nievergelt  that spend now half of their time doing statistical consulting (http://www.oikostat.ch/navigation_engl.htm). Nice reference concerning GLMMs are: the 2009 Bolker paper (paper),  the 2007 book

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Consecutive Numbers in Lottery Draws

March 2, 2014
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A historian, a data scientist, a programmer, a mathematician, and a philosopher discuss the question, how likely it is that a lottery draw (6 out of 49) contains two consecutive numbers. The historian The historian argues that from 1955 up to 2011, there were 5026 lottery draws in Germany, every Saturday, and from 2000 on, two draws every...

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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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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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Quick and dirty notes on General Linear Mix Models

February 24, 2014
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Quick and dirty notes on General Linear Mix Models

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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A Significantly Improved Significance Test. Not!

February 12, 2014
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A Significantly Improved Significance Test. Not!

It is my great pleasure to share with you a breakthrough in statistical computing. There are many statistical tests: the t-test, the chi-squared test, the ANOVA, etc. I here present a new test, a test that answers the question researchers are most anxious to figure out, a test of significance, the significance test. While a test like the two...

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Unit Root Tests

February 12, 2014
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Unit Root Tests

This week, in the MAT8181 Time Series course, we’ve discussed unit root tests. According to Wold’s theorem, if is  (weakly) stationnary then where is the innovation process, and where  is some deterministic series (just to get a result as general as possible). Observe that as discussed in a previous post. To go one step further, there is also the...

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Bayesian analysis of sensory profiling data

February 2, 2014
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Bayesian analysis of sensory profiling data

I looked at Bayesian analysis of sensory profiling data in May and June 2012. I do remember not being totally happy with the result and computations taking a bit more time than I wanted. But now it is 2014, I can use STAN and I have been thinking about...

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Expected overestimation of Cohen’s d under publication bias

January 27, 2014
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Expected overestimation of Cohen’s d under publication bias

In this post I will use the theoretical and empirical sampling distribution of Cohen's d to show the expected overestimation due to selective publishing. I will look at the overestimation for various sample sizes when the population effect is 0, 0.2, 0.5 and 0.8. The conclusion is that you should be weary of effect sizes from small samples, and...

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A short taxonomy of Bayes factors

January 21, 2014
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A short taxonomy of Bayes factors

I am starting to familiarize myself with Bayesian statistics. In this post I’ll show some insights I had concerning Bayes factors (BF). What are Bayes factors? Bayes factors provide a numerical value that quantifies how well a hypothesis predicts the empirical data relative to a competing hypothesis. For example, if the BF is 4, this

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