Monthly Archives: February 2010

The Power to … What did you say?

February 2, 2010
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The Power to … What did you say?

It is just about a year ago (exactly January 6th, 2009) that a New York Times article on R did fuel the dispute on what statistical analysis tool is “the best”. One of the highlight of the article was a quote from SAS’ Anne H. Milley: “I think it addresses a niche market for high-end

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Ensemble Prediction

February 2, 2010
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Ensemble Prediction

Weather is unpredictable. Small differences in initial conditions can develop into big differences in the pattern of circulation, in the timing and location of cyclones, rainfall etc. This is true no matter how good the initial observing system is. The approach taken by organisations such as ECMWF or NCEP is to re-run numerical forecast models

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Practical Implementation of Neural Network based Time Series (Stock) Prediction – PART 3

February 1, 2010
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Practical Implementation of Neural Network based Time Series (Stock) Prediction – PART 3

Ok, now that we have seen how well the perfect sine wave signal was learned, let's turn it up a notch and see how well the complex sine wave was learned.Fig 1. Summary of Actual Vs. Predicted out of sample complex sine waveformUh Oh. What happened, the...

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InfoWorld: SAS and SPSS rise to R opportunity

February 1, 2010
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At InfoWorld's "Open Source" blog Salvio Rodrigues found R co-inventor Robert Gentleman's appointment to the REvolution Computing board "a great impetus for me to look at R again". He notes that both SAS and SPSS have recognized the opportunity presented by R: I suspect that SPSS and SAS made their individual decisions based on three factors. First, they likely...

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R Tutorial Series: Regression With Categorical Variables

February 1, 2010
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R Tutorial Series: Regression With Categorical Variables

Categorical predictors can be incorporated into regression analysis, provided that they are properly prepared and interpreted. This tutorial will explore how categorical variables can be handled in R.Tutorial FilesBefore we begin, you may want to download the sample data (.csv) used in this tutorial. Be sure to right-click and save the file to your R working directory....

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