1712 search results for "regression"

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

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

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More ordinal data display

March 30, 2013
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More ordinal data display

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...

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Lots of data != "Big Data"

March 28, 2013
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Lots of data != "Big Data"

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...

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

March 24, 2013
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Does It Make Sense to Segment Using Individual Estimates from a Hierarchical Bayes Choice Model?

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...

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Not all proportion data are binomial outcomes

March 24, 2013
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Not all proportion data are binomial outcomes

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 →

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Predicting who will win a NFL match at half time

March 23, 2013
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Predicting who will win a NFL match at half time

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...

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Modes, Medians and Means: A Unifying Perspective

March 22, 2013
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Modes, Medians and Means: A Unifying Perspective

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

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Plotting lm and glm models with ggplot #rstats

March 22, 2013
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Plotting lm and glm models with ggplot #rstats

Update I followed the advice from Tim’s comment and changed the scaling in the sjPlotOdds-function to logarithmic scaling. The screenshots below showing the plotted glm’s have been updated. Summary In this posting I will show how to plot results from … Weiterlesen →

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Animating neural networks from the nnet package

March 19, 2013
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Animating neural networks from the nnet package

My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model

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Samsung Phone Data Analysis Project

March 19, 2013
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Samsung Phone Data Analysis Project

Below are my findings from the second data analysis project in Dr. Jeffery Leek’s John Hopkins Coursera class. Introduction I used the  “Human Activity Recognition Using Smartphones Dataset” (UCI, 2013) to build a model. This data  was recorded from a Samsung prototype smartphone with a built-in accelerometer. The purpose of my model was to recognize the type

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