# 1268 search results for "latex"

## The law of small numbers

January 28, 2013
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$N$

In insurance, the law of large numbers (named loi des grands nombres initially by Siméon Poisson, see e.g. http://en.wikipedia.org/…) is usually mentioned to legitimate large portfolios, because of pooling and diversification: the larger the pool, the more ‘predictable’ the losses will be (in a given period). Of course, under standard statistical assumption, namely finite expected value, and independence (see http://freakonometrics.blog.free.fr/…....

## My template for controlling publication quality figures

January 28, 2013
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The following is a template that I usually start with when producing figures for publication. It allows me to control:The overall size of the figure (in inches) (WIDTH, HEIGHT)The layout of figure subplots (using the layout() function) (LO)The resoluti...

## My template for controlling publication quality figures

January 28, 2013
By

The following is a template that I usually start with when producing figures for publication. It allows me to control:The overall size of the figure (in inches) (WIDTH, HEIGHT)The layout of figure subplots (using the layout() function) (LO)The resolution of the figure (for a .png file) (RESO)I define the overall dimensions of...

## Regression tree using Gini’s index

January 27, 2013
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$Y$

In order to illustrate the construction of regression tree (using the CART methodology), consider the following simulated dataset, > set.seed(1) > n=200 > X1=runif(n) > X2=runif(n) > P=.8*(X1<.3)*(X2<.5)+ + .2*(X1<.3)*(X2>.5)+ + .8*(X1>.3)*(X1<.85)*(X2<.3)+ + .2*(X1>.3)*(X1<.85)*(X2>.3)+ + .8*(X1>.85)*(X2<.7)+ + .2*(X1>.85)*(X2>.7) > Y=rbinom(n,size=1,P) > B=data.frame(Y,X1,X2) with one dichotomos varible (the variable of interest, ), and two continuous ones (the explanatory ones  and ). > tail(B) Y...

## Learning R using a Chemical Reaction Engineering Book: Part 3

January 26, 2013
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$Learning R using a Chemical Reaction Engineering Book: Part 3$

In case you missed previous parts, the links to them are listed below. Part 1 Part 2 In this part, I tried to recreate the examples in section A.2.3 of the computational appendix in the reaction engineering book (by Rawlings and … Continue reading →

## Learning R using a Chemical Reaction Engineering Book: Part 2

January 26, 2013
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$Learning R using a Chemical Reaction Engineering Book: Part 2$

In case you missed part 1, you can view it here. In this part, I tried to recreate the examples in section A.2.2 of the computational appendix in the reaction engineering book by Rawlings and Ekerdt. Solving a nonlinear system of equations … Continue reading →

## Learning R using a Chemical Reaction Engineering Book: Part 1

January 25, 2013
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$Learning R using a Chemical Reaction Engineering Book: Part 1$

Chemical Reactor Analysis and Design Fundamentals by J.B. Rawlings and J. G. Ekerdt is a textbook for studying Chemical Reaction Engineering. The popular open source package Octave has its origins to the reaction engineering course offered by Prof. Rawlings. This book … Continue reading →

## No more ascii-art

January 24, 2013
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At least fourfive R packages will turn your regression models into pretty latex tables: texreg, xtable, apsrtable, memisc, and stargazer.  This is very nice if you happen to be a latex document or its final reader, but it’s not so great if you’re making those models to start with. What if you wanted to see

## Management of Research Data – a Shell+Python+Excel+R Approach

January 23, 2013
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I am a computer science researcher, usually working on both Windows and Linux system. Windows is the place where I do the document work, like reading paper, browsing the internet, writing papers with LaTex… Linux is where I run and generate experimental results. The Chaos After years of messy data management and recent data chaos,

## Binomial Confidence Intervals

January 22, 2013
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$Binomial Confidence Intervals$

This stems from a couple of binomial distribution projects I have been working on recently.  It’s widely known that there are many different flavors of confidence intervals for the binomial distribution.  The reason for this is that there is a coverage problem with these intervals (see Coverage Probability).  A 95% confidence interval isn’t always (actually