Monthly Archives: August 2013

New Video: Credit Scoring & R: Reject inference, nested conditional models, & joint scores

August 29, 2013
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This post shares the video from the talk presented in August 2013 by Ross Gayler on Credit Scoring and R at Melbourne R Users. Credit scoring tends to involve the balancing of mutually contradictory objectives spiced with a liberal dash … Continue reading →

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Increasing Repeat Purchase Rate by Analyzing Customer Latency

August 28, 2013
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Increasing Repeat Purchase Rate by Analyzing Customer Latency

For online businesses, Repeat Purchase Rate is one of the critical metrics of the business performance. Higher repeat purchase rate means more active members, and thus leads to higher profit. “Customer Latency refers to the average time between customer activity events, for example, making a purchase, calling the help desk, or visiting a web site”1,

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TV Ratings Myths

August 28, 2013
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TV Ratings Myths

TV Show Cancellations: Myths and ModelsTV shows are amazing ways to waste time and, on occasion, the story is so good that you actually start to care. The problem is that some shows get cancelled before they jump the shark. Classic examples are shows like

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Applications of Interactivity to Finance

August 28, 2013
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Applications of Interactivity to Finance

Of the nearly infinite ways of using crossfilter and dc.js in finance, the 2 that immediately came to my mind are signal analysis in system building and money manager analysis in due diligence.  My first very basic experiment explores a commonly k...

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Bayesian Estimation of Correlation – Now Robust!

August 28, 2013
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Bayesian Estimation of Correlation – Now Robust!

So in the last post I showed how to run the Bayesian counterpart of Pearson’s correlation test by estimating the parameters of a bivariate normal distribution. A problem with assuming normality is that the normal distribution isn’t robust against outliers. Let’s see what happens if we take the data from the last post with the finishing times...

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Calculating the Highest Value to Bid on Each Player in an Auction Draft: The Bid Up To Value

August 27, 2013
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In this post, I calculate the highest value you should bid on each player in an auction draft—what I refer to as the “bid up to” value.  In a previous The post Calculating the Highest Value to Bid on Each Player in an Auction Draft: The Bid Up To Value appeared first on Fantasy Football Analytics.

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Calculating the Highest Value to Bid on Each Player in an Auction Draft: The Bid Up To Value

August 27, 2013
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In this post, I calculate the highest value you should bid on each player in an auction draft—what I refer to as the "bid up to" value.  In a previous post, I showed how to determine the best starting lineup to draft using an optimizer tool.  The "bid up to" value is calculated by finding the highest cost...

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An R function like “order” from Stata

August 27, 2013
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A while ago, there was a question on Stackoverflow, Is there an equivalent R function to Stata ‘order’ command?. There isn’t really, and for the most part, you don’t really need one, but I decided that, for fun, I would write one anyway. Instead of operating directly on the data.frames, I decided to just work

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Visualizing the Forbes-CCAP University Rankings using ggplot2, rCharts, googleVis, and the shiny server

August 27, 2013
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Visualizing the Forbes-CCAP University Rankings using ggplot2, rCharts, googleVis, and the shiny server

President Obama is pushing for higher education reform and the development of a rating system for Universities is a critical component of it. These ratings are likely to be based on several measures, such as graduation rates, earnings of graduates, and...

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Multicore (parallel) processing in R

August 27, 2013
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Multicore (parallel) processing in R from Wallace Campbell on Vimeo. If you're not programming in parallel, you're only using a fraction of your computer's power! I demonstrate how to run "for" loops in parallel using the mclapply function from the multicore library. The code can be scaled to any number of available cores.

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