1516 search results for "Regression"

Support Vector Machines in R (a course by Lutz Hamel)

October 19, 2011
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Support vector machines (SVM’s) are the “big iron” of the data mining world, especially suited for extreme data intensive tasks like image classification, biosequence processing, handwriting recognition, etc. Dr. Lutz Hamel, author of “Knowledge Discovery with Support Vector Machines”, presents his online course “Introduction to Support Vector Machines In R” November 18 – December 16. “Support Vector Machines in...

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On R, bloggers, politics, sex, alcohol and rock & roll

October 19, 2011
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On R, bloggers, politics, sex, alcohol and rock & roll

Yesterday morning at 7 am I was outside walking the dog before getting a taxi to go to the airport to catch a plane to travel from Christchurch to Blenheim (now I can breath after reading without a pause). It … Continue reading →

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How does Matt kemp become Andre Dawson?

October 18, 2011
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How does Matt kemp become Andre Dawson?

While reading this article over at Fangraphs I was inspired to ask myself “what would Matt Kemp have to do between now and then end of his career to be seriously considered for the Hall of Fame?”.  This question comes … Continue reading →

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Principal component analysis : Use extended to Financial economics : Part 1

October 15, 2011
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Principal component analysis : Use extended to Financial economics : Part 1

While working for my Financial economics project I came across this elegant tool called Principal component analysis (PCA)which is an extremely powerful tool when it comes to reducing the dimentionality of a data set comprising of highly correlated var...

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There’s a lot to like about R

October 13, 2011
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I once heard John Chambers (the inventor of the S language, and member of the R Core Group) say, "Show me a programming language no-one complains about, and I'll show you a language no-one uses". The R language has its fair share of complainants, to be sure -- and that's to be expected for a language with more than...

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Simulating data following a given covariance structure

October 12, 2011
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Every year there is at least a couple of occasions when I have to simulate multivariate data that follow a given covariance matrix. For example, let’s say that we want to create an example of the effect of collinearity when … Continue reading →

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understanding computational Bayesian statistics

October 9, 2011
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understanding computational Bayesian statistics

I have just finished reading this book by Bill Bolstad (University of Waikato, New Zealand) which a previous ‘Og post pointed out when it appeared, shortly after our Introducing Monte Carlo Methods with R. My family commented that the cover was nicer than those of my own books, which is true. Before I launch into

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Risk, Return and Analyst Ratings

October 7, 2011
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Risk, Return and Analyst Ratings

Today I want to discuss a connection between Risk, Return and Analyst Ratings. Let’s start with defining our universe of stocks : 30 stocks from Dow Jones Industrial Average (^DJI) index. For each stock I will compute the number of Upgrades and Downgrades, Risk, and Return in 2010:2011. I will run a linear regression and

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All combinations for levelplot

October 7, 2011
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All combinations for levelplot

In a previous post I explained how to create all possible combinations of the levels of two factors using expand.grid(). Another use for this function is to create a regular grid for two variables to create a levelplot or a … Continue reading →

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Assumptions of the Linear Model

October 6, 2011
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Assumptions of the Linear Model

Linear Assumptions from the Analysis Factor – Assumptions of linear regression (and ANOVA) are about the residuals, not the normality or independence of the response variable (Y). If you don’t know what this means be sure to read this brief … Continue reading →

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