2048 search results for "regression"

Teaching linear models

January 2, 2014
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Teaching linear models

I teach several courses every year and the most difficult to pull off is FORE224/STAT202: regression modeling. The academic promotion application form in my university includes a section on one’s ‘teaching philosophy’. I struggle with that part because I suspect I lack anything as grandiose as a philosophy when teaching: as most university lecturers I

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Happy New Year!

January 1, 2014
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It's a brand new year, and the Revolutions blog is now three weeks into its sixth year. Hard to believe that a little over five years ago this was the only R-related blog; now there are more than 450 and the R project and the R community continue to thrive and grow. To bring in the new year, I...

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Generalized linear models for predicting rates

January 1, 2014
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Generalized linear models for predicting rates

I often need to build a predictive model that estimates rates. The example of our age is: ad click through rates (how often a viewer clicks on an ad estimated as a function of the features of the ad and the viewer). Another timely example is estimating default rates of mortgages or credit cards. You Related posts:

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An Animation of the Construction of a Confidence Interval

December 30, 2013
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An Animation of the Construction of a Confidence Interval

I’m playing blog ping-pong with John Kruschke’s Doing Bayesian Data Analysis blog as he was partly inspired by my silly post on Bayesian mascots when writing a nice piece on Icons for the essence of Bayesian and frequentist data analysis. That piece, in turn, inspired me resulting in the following wobbly backhand. The confidence interval...

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Statistics unplugged

December 27, 2013
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Statistics unplugged

How much does statistical software help and how much it interferes when teaching statistical concepts? Software used in the practice of statistics (say R, SAS, Stata, etc) brings to the party a mental model that it’s often alien to students, while being highly optimized for practitioners. It is possible to introduce a minimum of distraction

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How fair is White Elephant?

December 24, 2013
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How fair is White Elephant?

This article was first published on analyze stuff. It has been contributed to Anything but R-bitrary in celebration of its introductory post.By Max Ghenis Welcome to analyze stuff! For our first post, I wanted to reflect on the time of year; after all, ‘tis the season for hams and yams, caroling and sledding, and of...

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Apache Spark for Big Analytics

December 23, 2013
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Apache Spark for Big Analytics

by Thomas Dinsmore, Director of Product Management at Revolution Analytics The emergence of Apache Spark is a key development for Big Analytics in 2013. Spark, an Apache incubator project, is an open source distributed computing framework for advanced analytics in Hadoop. Originally developed as a research project at UC Berkeley's AMPLab, the project achieved incubator status in Apache in...

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Calculating Customer Lifetime Value with Recency, Frequency, and Monetary (RFM)

December 23, 2013
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Calculating Customer Lifetime Value with Recency, Frequency, and Monetary (RFM)

Introducing Customer Lifetime Value (CLV) Customer Lifetime Value is “the present value of the future cash flows attributed to the customer during his/her entire relationship with the company.”1 There are different kinds of formulas, from simplified to advanced, to calculate CLV.  But the following one might be the one being used most commonly:- Where, t

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24 Days of R: Day 20

December 20, 2013
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24 Days of R: Day 20

Some time ago, I was doing some analysis and trying to determine whether or not there was a predictive variable for a binomial response. I ran logistic regressions for about half a dozen variables in different combinations and nothing showed a fit of any significance. Well, almost nothing. I had measured the response against date.

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24 Days of R: Day 19

December 19, 2013
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24 Days of R: Day 19

Carrying on with the multi-level model, I'm going to look at the paid and incurred workers comp losses for a large number of insurance companies. This is a similar exercise to what I did last night, but I'm now working with real, rather than simulated data and the stochastic process is assumed to be different.

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