1627 search results for "Excel"

The Problem with Percentiles

September 8, 2013
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The Problem with Percentiles

The Problem with Percentiles Percentiles (or, more accurately, quantiles) are deeply embedded in the psyche of actuaries, statisticians and similar beasts. They are referred to implicitly in the Solvency 2 directive (Article 100, Value at Risk) without explanation. They are so ingrained...

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Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

September 1, 2013
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Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

Last week I attended a workshop on how to run highly parallel distributed jobs on the Open Science Grid (osg). There I met Derek Weitzel who has made an excellent contribution to advancing R as a high performance computing language by developing BoscoR. BoscoR greatly facilitates the use of the already existing package “GridR” by The post Easy...

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MLB Rankings Using the Bradley-Terry Model

August 31, 2013
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MLB Rankings Using the Bradley-Terry Model

Today, I take my first shots at ranking Major League Baseball (MLB) teams. I see my efforts at prediction and ranking an ongoing process so that my models improve, the data I incorporate are more meaningful, and ultimately my predictions are largely accurate. For the first attempt, let’s rank MLB teams using the Bradley-Terry (BT) model. Before we discuss the rankings, we need...

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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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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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Fantastic presentations from R using slidify and rCharts

August 27, 2013
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Fantastic presentations from R using slidify and rCharts

Dr. Ramnath Vaidyanathan of McGill University gave an excellent presentation at a joint Data Visualization DC/Statistical Programming DC event on Monday, August 19 at nclud, on two R projects he leads — slidify and rCharts. After the evening, all I can say … Continue reading → The post Fantastic presentations from R using slidify and rCharts appeared first on

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The Wonders of foreach

August 25, 2013
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The Wonders of foreach

Writing code from scratch to do parallel computations can be rather tricky. However, the packages providing parallel facilities in R make it remarkably easy. One such package is foreach. I am going to document my trail of discovery with foreach, which began some time ago, but has really come into fruition over the last few

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GitHub renders CSV in the browser, becomes even better for social data set creation

August 22, 2013
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GitHub renders CSV in the browser, becomes even better for social data set creation

I've written in a number of places about how GitHub can be a great place to store data. Unlike basically all other web data storage sites (many of which I really like such as Dataverse and FigShare) GitHub enables deep social data set development and f...

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Date Formats in R

August 22, 2013
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Importing DatesDates can be imported from character, numeric, POSIXlt, and POSIXct formats using the as.Date function from the base package.If your data were exported from Excel, they will possibly be in numeric format. Otherwise, they will m...

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Finding Correlations in Data with Uncertainty: Classical Solution

August 13, 2013
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Following up on my previous post as a result of an excellent suggestion from Andrej Spiess. The data are indeed very heteroscedastic! Andrej suggested that an alternative way to attack this problem would be to use weighted correlation with weights being the inverse of the measurement variance. Let’s look at the synthetic data first. This is

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