Better Life Index 2012

June 3, 2012
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Better Life Index 2012

Play around and enjoy a beautiful data visualisation full of insights.

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NBA Playoff Predictions Update 2 and Results (3-1)

June 3, 2012
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NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm’s record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners...

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NBA Playoff Predictions Update 2 and Results (3-1)

June 3, 2012
By
NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm’s record is now 3-1, as it correctly predicted Boston and Oklahoma City Continue reading →

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NBA Playoff Predictions Update 2 and Results (3-1)

June 3, 2012
By
NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm's record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of the...

Read more »

NBA Playoff Predictions Update 2 and Results (3-1)

June 3, 2012
By
NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm’s record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of th...

Read more »

Question about predictive checks

June 3, 2012
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Klaas Metselaar writes: I [Metselaar] am currently involved in a discussion about the use of the notion “predictive” as used in “posterior predictive check”. I would argue that the notion “predictive” should be reserved for posterior checks using information not used in the determination of the posterior. I quote from the discussion: “However, the predictive [...]

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NBA Playoff Predictions Update 2 and Results (3-1)

June 3, 2012
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NBA Playoff Predictions Update 2 and Results (3-1)

This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm's record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of their past games.Upcoming things to doSadly, I have been a bit busy, and I have not been able to do any work on the algorithm the past…

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US market portrait 2012 week 23

June 3, 2012
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US market portrait 2012 week 23

US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email

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Another retraction

June 2, 2012
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Xian points me to this pitiful story. I hate that these people never just say they’re sorry, for wasting everyone’s time if for nothing else.

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Pasting Excel data into R on a Mac

June 2, 2012
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Pasting Excel data into R on a Mac

When starting out with R, getting data in and out can be a bit of a pain. It should take long to work out a convenient method – depending on what OS you use and what other packages you work with. In my case I prefer to work with Excel spreadsheets (which are versatile and […]

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Pasting Excel data into R on a Mac

June 2, 2012
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Pasting Excel data into R on a Mac

When starting out with R, getting data in and out can be a bit of a pain. It should take long to work out a convenient method – depending on what OS you use and what other packages you work with. In my case I prefer to work with Excel spreadsheets (which are versatile and […]

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Question 23 of my final exam for Design and Analysis of Sample Surveys

June 2, 2012
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23. Suppose you are conducting a survey in which people are asked about their health behaviors (how often they wash their hands, how often they go to the doctor, etc.). There is a concern that different interviewers will get different sorts of responses—that is, there may be important interviewer effects. Describe (in two sentences) how [...]

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Useful for referring–6-2-2012

June 2, 2012
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Useful for referring–6-2-2012

Note: the following 4-7 are from Simply Statistics. A Personal Perspective on Machine Learning The differing perspectives of statistics and machine learning Kernel Methods and Support Vector Machines de-Mystified I love this article in the WSJ about the crisis at JP Morgan. The key point it highlights is that looking only at the high-level analysis and summaries can [...]

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Helpful on happiness

June 2, 2012
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Following on our recent discussion of contradictory findings on happiness, David Austin writes: A pellucid discussion of happiness and happiness research is Fred Feldman, What is This Thing Called Happiness? (Oxford University Press, 2010). And here&#8...

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Visualizing car brand choices in ggplot2

June 2, 2012
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Visualizing car brand choices in ggplot2

I always like to read new posts at chartsnthings as they always inspire me with new ideas for data visualization. Yesterday I have read an article on choices of car brands by members of parliament in Poland in Gazeta.pl. It contains a simple ...

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Distribution of Oft-Used Bash Commands

June 1, 2012
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Distribution of Oft-Used Bash Commands

Browsing commandlinefu.com today, I came across this little one-liner to display which commands I use most often. Here’s what I got: Yep, seems legit. I navigate and look at files a whole bunch (ls, cd, cat), and I do a butt tonne of editing (vim). I sudo like a boss, hop onto various servers (ssh),

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Question 22 of my final exam for Design and Analysis of Sample Surveys

June 1, 2012
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22. A supermarket chain has 100 equally-sized stores. It is desired to estimate the proportion of vegetables that spoil before being sold. Three stores are selected at random and are checked: the percent of spoiled vegetables are 3%, 5%, and 10% in the three stores. Give an estimate and standard error for the percentage of [...]

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Gibbs sampling a Gaussian Markov random field (GMRF) using Java

June 1, 2012
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Gibbs sampling a Gaussian Markov random field (GMRF) using Java

Introduction As I’ve explained previously, I’m gradually coming around to the idea of using Java for the development of MCMC codes, and I’m starting to build up a collection of simple examples for getting started. One of the advantages of Java is that it includes a standard cross-platform GUI library. This might not seem like … … Continue reading →

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Beta distribution parameterized by mode instead of mean

June 1, 2012
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Beta distribution parameterized by mode instead of mean

In this post, I describe how it is easier to intuit the beta distribution in terms of its mode than its mean. This is especially handy when specifying a prior beta distribution. (In a previous post, I explained how it is easier to intuit the gamma...

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Predicting NBA Playoff Games – Results and Update 1

June 1, 2012
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Predicting NBA Playoff Games – Results and Update 1

Game ResultsI recently made a post about developing an algorithm to predict the NBA playoffs, and I concluded with 2 predictions. Although Miami beat the Celtics to make my algorithm 1-0 in terms of predictions, it fell to 1-1 when the Thunder beat t...

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Predicting NBA Playoff Games – Results and Update 1

June 1, 2012
By
Predicting NBA Playoff Games – Results and Update 1

Game Results I recently made a post about developing an algorithm to predict the NBA playoffs, and I concluded with 2 predictions. Although Miami beat the Celtics to make my algorithm 1-0 in terms of predictions, it fell to 1-1 Continue reading →

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Predicting NBA Playoff Games – Results and Update 1

June 1, 2012
By
Predicting NBA Playoff Games – Results and Update 1

Game ResultsI recently made a post about developing an algorithm to predict the NBA playoffs, and I concluded with 2 predictions. Although Miami beat the Celtics to make my algorithm 1-0 in terms of predictions, it fell to 1-1 when the Thunder beat th...

Read more »

Why use Odds Ratios in Logistic Regression

June 1, 2012
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Why use Odds Ratios in Logistic Regression

What that means is there is no way to express in one number how X affects Y in terms of probability. The effect of X on the probability of Y has different values depending on the value of X.

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