1420 search results for "regression"

"R": Predicting a Test Set (Gasoline)

February 9, 2012
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"R": Predicting a Test Set (Gasoline)

> data(gasoline)> #60 spectra of gasoline (octane is the constituent) > #We divide the whole Set into a Train Set and a Test Set.> gasTrain<-gasoline> gasTest<-gasoline> #Let´s develop the PLSR with the Tain Set ...

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GARCH estimation using maximum likelihood

February 9, 2012
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In my previous post I presented my findings from my finance project under the guidance of Dr Susan Thomas. The results in my paper suggested that there are macroeconomic variables, particularly the INR/USD exchange rates, that help us understand the dynamics of stock returns. Although the results that I obtained were significant at 5%...

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Example 9.20: visualizing Simpson’s paradox

February 7, 2012
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Example 9.20: visualizing Simpson’s paradox

Simpson's paradox is always amazing to explain to students. What's bad for one group, and bad for another group is good for everyone, if you just collapse over the grouping variable. Unlike many mathematical paradoxes, this arises in a number of real...

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Workshop on Mixed and Multilevel Modelling with R in Toronto

February 7, 2012
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Summer Program In Data Analysis (SPIDA): May 24th – June 1st, 2012 In its thirteenth season this year, ISR’s Summer Program in Data Analysis focuses on linear models, beginning with “standard” regression through generalized linear models, and extending to mixed or multilevel models, linear and non-linear and generalized, which incorporate two or more hierarchical levels of data or longitudinal...

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General Bayesian estimation using MHadaptive

February 6, 2012
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General Bayesian estimation using MHadaptive

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets build

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Comparing correlations: independent and dependent (overlapping or non-overlapping)

February 5, 2012
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Comparing correlations: independent and dependent (overlapping or non-overlapping)

In Chapter 6 (correlation and covariance) I consider how to construct a confidence interval (CI) for the difference between two independent correlations.  The standard approach uses the Fisher z transformation to deal with boundary effects (the squashing of the distribution and increasing asymmetry as r approaches -1 or 1). As zr is approximately normally distributed

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Multiple Factor Model – Building Fundamental Factors

February 4, 2012
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Multiple Factor Model – Building Fundamental Factors

This is the second post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Fundamental Data, and I will show how to build Fundamental factors described in the CSFB Alpha Factor Framework. For details of the CSFB Alpha Factor Framework please read

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Why don’t we hear more about Adrian Dantley on ESPN? This graph makes me think he was as good an offensive player as Michael Jordan.

February 3, 2012
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Why don’t we hear more about Adrian Dantley on ESPN? This graph makes me think he was as good an offensive player as Michael Jordan.

In my last post I complained about efficiency not being discussed enough by NBA announcers and commentators. I pointed out that some of the best scorers have relatively low FG% or TS%. However, via the comments it was pointed out that top scorers need ...

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On linear models with no constant and R2

February 2, 2012
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On linear models with no constant and R2

In econometrics course we always say to our students that "if you fit a linear model with no constant, then you might have trouble. For instance, you might have a negative R-squared". So I tried to find databases on the internet such that, when we ...

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Analytic applications are built by data scientists

February 1, 2012
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Ventana Research analyst David Menninger was on the judging panel for the Applications of R in Business contest. In a post on the Ventana research blog, he offers his perspectives on the contest, noting that R, as a statistical package, includes many algorithms for predictive analytics, including regression, clustering, classification, text mining and other techniques. The contest submissions supported...

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