332 search results for "boxplot"

Finding outliers in numerical data

Finding outliers in numerical data

One of the topics emphasized in Exploring Data in Engineering, the Sciences and Medicine is the damage outliers can do to traditional data characterizations.  Consequently, one of the procedures to be included in the ExploringData package is FindOutliers, described in this post.  Given a vector of numeric values, this procedure supports four different methods for identifying possible outliers.Before...

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Variability of predicted portfolio volatility

February 11, 2013
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Variability of predicted portfolio volatility

A prediction of a portfolio’s volatility is an estimate — how variable is that estimate? Data The universe is 453 large cap US stocks. The variance matrices are estimated with the daily returns in 2012. Variance estimation was done with Ledoit-Wolf shrinkage (shrinking towards equal correlation). Two sets of random portfolios were created.  In both … Continue reading...

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F1Stats – Correlations Between Qualifying, Grid and Race Classification

February 9, 2013
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F1Stats – Correlations Between Qualifying, Grid and Race Classification

Following directly on from F1Stats – Visually Comparing Qualifying and Grid Positions with Race Classification, and continuing in my attempt to replicate some of the methodology and results used in A Tale of Two Motorsports: A Graphical-Statistical Analysis of How Practice, Qualifying, and Past SuccessRelate to Finish Position in NASCAR and Formula One Racing, here’s

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Analyze web traffic data with Google Analytics and R

February 7, 2013
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Analyze web traffic data with Google Analytics and R

If you run an e-commerce site, blog or other web property there's a good chance you use Google Analytics to monitor traffic, look at visitor sources, and measure conversions. And while Google Analytics is quite powerful at looking at historic activity on your site, it lacks much in the way of predictive analytics. That's where R shines of course,...

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"I don’t wanna grow up": Age / value relationships for football players

February 1, 2013
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"I don’t wanna grow up": Age / value relationships for football players

Let's get back to the age-value relationship from my last post. I did some more plotting to see on which position this inversed U-shaped relationship is strongest. Please note, that I use a dataframe called eu.players throughout this post, which holds downloaded football player information from transfermarkt.de.But first, let us get back to the original graph.

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A slightly different introduction to R, part II

January 27, 2013
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A slightly different introduction to R, part II

In part I, we looked at importing data into R and simple ways to manipulate data frames. Once we’ve gotten our data safely into R, the first thing we want to do is probably to make some plots. Below, we’ll make some simple plots of the made-up comb gnome data. If you want to play

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(not provided): Using R and the Google Analytics API

January 11, 2013
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(not provided): Using R and the Google Analytics API

For power users of Google Analytics, there is a heavy dose of spreadsheet work that accompanies any decent analysis.  But even with Excel in tow, it’s often difficult to get the data just right without resorting to formula hacks and manual table formatting.  This is where the Google Analytics API and R can come very (not provided): Using...

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Why Do the New Orleans Saints Lose? Data Visualization II

December 26, 2012
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Why Do the New Orleans Saints Lose? Data Visualization II

I’m going to continue with my ‘making data visually appealing to the masses’ kick. I happen to like graphics and graphing data. I also happen to like American football (For the record, however, I’m a soccer player first, a rugby … Continue reading →

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Miles of iles

December 24, 2012
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Miles of iles

An explanation of quartiles, quintiles deciles, and boxplots. Previously “Again with variability of long-short decile tests” and its predecessor discusses using deciles but doesn’t say what they are. The *iles These are concepts that have to do with approximately equally sized groups created from sorted data. There are 4 groups with quartiles, 5 with quintiles … Continue reading...

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Generation of E-Learning Exams in R for Moodle, OLAT, etc.

December 20, 2012
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Generation of E-Learning Exams in R for Moodle, OLAT, etc.

(Guest post by Achim Zeileis) Development of the R package exams for automatic generation of (statistical) exams in R started in 2006 and version 1 was published in JSS by Gr?n and Zeileis (2009). It was based on standalone Sweave exercises, that can be combined …Read more »

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