# 1928 search results for "regression"

## What’s New in Release 6.2: Additional ScaleR Features

April 2, 2013
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by Thomas Dinsmore Revolution R Enterprise Release 6.2 is in track for General Availability on April 22. In previous posts, I've commented on support for open source R 2.15.3 and Stepwise Regression. Today I'll wrap this series with a summary of some of the other new features supported in this release. Parallel Random Number Generation For analysts seeking to...

## Introducing the healthvis R package – one line D3 graphics with R

April 2, 2013
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We have been a little slow on the posting for the last couple of months here at Simply Stats. That’s bad news for the blog, but good news for our research programs! Today I’m announcing the new healthvis R package … Continue reading →

## p-values are (possibly biased) estimates of the probability that the null hypothesis is true

March 31, 2013
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$p-values are (possibly biased) estimates of the probability that the null hypothesis is true$

Last week, I posted about statisticians’ constant battle against the belief that the p-value associated (for example) with a regression coefficient is equal to the probability that the null hypothesis is true, for a null hypothesis that beta is zero or negative. I argued that (despite our long pedagogical practice) there are, in fact, many

## How do Dew and Fog Form? Nature at Work with Temperature, Vapour Pressure, and Partial Pressure

In the early morning, especially here in Canada, I often see dew – water droplets formed by the condensation of water vapour on outside surfaces, like windows, car roofs, and leaves of trees.  I also sometimes see fog – water droplets or ice crystals that are suspended in air and often blocking visibility at great

## More ordinal data display

March 30, 2013
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The past two weeks I made a post regarding analyzing ordinal data with R and JAGS. The calculations in the second part made me realize I could actually get top two box intervals out of R. This demonstrated here. For that I needed the inv...

## Lots of data != "Big Data"

March 28, 2013
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by Joseph Rickert When talking with data scientists and analysts — who are working with large scale data analytics platforms such as Hadoop — about the best way to do some sophisticated modeling task it is not uncommon for someone to say, "We have all of the data. Why not just use it all?" This sort of comment often...

## Does It Make Sense to Segment Using Individual Estimates from a Hierarchical Bayes Choice Model?

March 24, 2013
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I raise this question because we see calls for running segmentation with individual estimates from hierarchical Bayes choice models without any mention of the possible complications that might accompany such an approach.  Actually, all the calls seem to be from those using MaxDiff to analyze the data from incomplete block designs.  For example, if one were to...

## Not all proportion data are binomial outcomes

March 24, 2013
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It really is trivial. Not every proportion is frequency. There are things that have values  bounded between 0 and 1 and yet they are neither probabilities, nor frequencies. Why do I even bother to write this? Because some kinds of…Read more →

## Predicting who will win a NFL match at half time

March 23, 2013
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It was great to have a little break, Spring break, although the weather didn’t feel like spring at all! During the early part of the break I worked on my final project for Jeff Leek’s data analysis class, which we call 140.753 here. Continuing my previous posts on the topic, this time I’ll share the results of my...

## Modes, Medians and Means: A Unifying Perspective

March 22, 2013
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Introduction / Warning Any traditional introductory statistics course will teach students the definitions of modes, medians and means. But, because introductory courses can’t assume that students have much mathematical maturity, the close relationship between these three summary statistics can’t be made clear. This post tries to remedy that situation by making it clear that all