Posts Tagged ‘ regression ’

Testing for…

October 31, 2011
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Testing for…

Input Output Testing for regression Input: advertising=c(1,2,3,4,5) sales=c(1,1,2,2,4) sales.Reg=lm(sales~advertising) summary(sales.Reg) Output: > advertising=c(1,2,3,4,5) > sales=c(1,1,2,2,4) > > sales.Reg=lm(sales~advertising) > summary...

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Support Vector Machine with GPU

August 27, 2011
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Support Vector Machine with GPU

Most elementary statistical inference algorithms assume that the data can be modeled by a set of linear parameters with a normally distributed noise component. A new class of algorithms called support vector machine (SVM) remove such constraint. rea...

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Model Validation: Interpreting Residual Plots

July 18, 2011
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Model Validation: Interpreting Residual Plots

When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model. There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions. However, there is little general acceptance of any of the statistical tests. Generally...

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Because it’s Friday: French Press Heat Retention

May 13, 2011
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Because it’s Friday: French Press Heat Retention

While responding to this thread on Reddit I made a rough guess as to the heat retention of my french press when completely full of coffee. When I went to bed I realized there was no good reason why I … Continue reading →

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Multivariate probit regression using (direct) maximum likelihood estimators

May 11, 2011
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Multivariate probit regression using (direct) maximum likelihood estimators

Consider a random pair of binary responses, i.e. with taking values 1 or 2. Assume that probability can be function of some covariates . The Gaussian vector latent structure A standard model is based a latent Gaussian structure, i.e. there exi...

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R workshop in Hamilton, Ontario, May 24 and 25

April 25, 2011
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John Fox will be teaching a two-day introductory R workshop at McMaster University in Hamilton, Ontario, on May 24 and 25. The workshop will largely be based on materials from Fox and Weisberg, An R Companion to Applied Regression, Second Edition (Sage, 2011). Further information about the workshop is available at: https://www.socialsciences.mcmaster.ca/registrations/john-fox-introduction-to-r/fg_base_view_p3. A few spaces in the workshop are reserved for non-McMaster attendees and made...

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Machine Learning Ex3 – Multivariate Linear Regression

March 29, 2011
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Machine Learning Ex3 – Multivariate Linear Regression

Part 1. Finding alpha. The first question to resolve in Exercise 3 is to pick a good learning rate alpha. This require making an initial selection, running gradient descent and observing the cost function. Read More: 221 Words Totally

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A Short Return to the Age-Earnings Profile

March 8, 2011
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A Short Return to the Age-Earnings Profile

Two posts ago I mentioned the age-earnings profile but did not provide a regression of log earnings on wage. I also offered, without evidence, that fitting a simple linear regression would be inappropriate. How do I know that? How could … Continue reading →

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More on logging the outcome

March 4, 2011
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More on logging the outcome

This one does my head in. I do it fairly regularly, lots of people do, but I find everytime it comes to interpreting the results I have to slow it right down and go step by step. Answer: When you log the outcome, then on the original scale, for all else constant, Y will be

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The cranky guide to trying R packages

February 13, 2011
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The cranky guide to trying R packages

This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data. Suppose you want to try out a novel statistical technique? A good fraction of the time R is your best bet for Related posts:

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