1157 search results for "latex"

Predictors, responses and residuals: What really needs to be normally distributed?

February 18, 2013
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Predictors, responses and residuals: What really needs to be normally distributed?

Introduction Many scientists are concerned about normality or non-normality of variables in statistical analyses. The following and similar sentiments are often expressed, published or taught: "If you want to do statistics, then everything needs to be normally distributed." "We normalized…Read more →

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Contribute to The R Journal with LyX/knitr

February 17, 2013
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Contribute to The R Journal with LyX/knitr

(This paragraph is pure rant; feel free to skip it) I have been looking forward to the one-column LaTeX style of The R Journal, and it has arrived eventually. Last time I mentioned "it does not make sense to sell the cooked shrimps"; actually there is ...

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Veterinary Epidemiologic Research: Linear Regression

February 14, 2013
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Veterinary Epidemiologic Research: Linear Regression

This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If

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Large claims, and ratemaking

February 13, 2013
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Large claims, and ratemaking

During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...

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Unknown Variance Two-Tailed Test of Population Mean

February 11, 2013
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Unknown Variance Two-Tailed Test of Population Mean

Question The mean safety audit score of ACME Co. stores in New York (n=200) was 74.3pts February last year.  Suppose we decided to sample 22 out of the 200 stores one year later. We find that the sample mean is 78.6pts and the sample standard deviation is 3.2pts.  Can we reject the null hypothesis that

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Exposure with binomial responses

February 9, 2013
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Exposure with binomial responses

Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: , the number of claims  on the period  is unobserved the number of...

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Learning R Using a Chemical Reaction Engineering Book: Part 4

February 8, 2013
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Learning R Using a Chemical Reaction Engineering Book: Part 4

The links to previous parts are listed here. (Part 1, Part 2, Part 3). In this part, I tried to recreate the examples in sections A.3.1 of the computational appendix in the reaction engineering book (by Rawlings and Ekerdt). Solving a … Continue reading →

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Pills, half pills and probabilities

February 8, 2013
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Pills, half pills and probabilities

Yesterday, I was uploading some old posts to complete the migration (I get back to my old posts, one by one, to check links of pictures, reformating R codes, etc). And I re-discovered a post published amost 2 years ago, on nuns and Hell’s Angels in an airplaine. It reminded me an old probability problem (that might be known...

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packed off!!!

February 8, 2013
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packed off!!!

Deliverance!!! We have at last completed our book! Bayesian Essentials with R is off my desk! In a final nitty-gritty day of compiling and recompiling the R package bayess and the LaTeX file, we have reached versions that were in par with our expectations. The package has been submitted to CRAN (it has gone back

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Collinearity and stepwise VIF selection

February 5, 2013
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Collinearity and stepwise VIF selection

Collinearity, or excessive correlation among explanatory variables, can complicate or prevent the identification of an optimal set of explanatory variables for a statistical model. For example, forward or backward selection of variables could produce inconsistent results, variance partitioning analyses may be unable to identify unique sources of variation, or parameter estimates may include substantial amounts

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