1285 search results for "latex"

R 3.0.2 and RStudio 0.9.8 are released!

September 26, 2013
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RRRRRRRRRRRRRRRRstudiofavicon

R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging …Read more »

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Waiting in One Line or Multiple Lines

September 23, 2013
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Waiting in One Line or Multiple Lines

Whenever I go to the grocery store it always seems to be a lesson in statistics. I go get the things I need to buy and then  I try to select the checkout register that will decrease the amount of time I have to wait. Inevitably, I select the one line where there is some

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Exploratory Data Analysis: Quantile-Quantile Plots for New York’s Ozone Pollution Data

Exploratory Data Analysis: Quantile-Quantile Plots for New York’s Ozone Pollution Data

Introduction Continuing my recent series on exploratory data analysis, today’s post focuses on quantile-quantile (Q-Q) plots, which are very useful plots for assessing how closely a data set fits a particular distribution.  I will discuss how Q-Q plots are constructed and use Q-Q plots to assess the distribution of the “Ozone” data from the built-in

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Church numerals in R (or how to prove the existence of natural numbers using the lambda calculus)

September 18, 2013
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Church numerals in R (or how to prove the existence of natural numbers using the lambda calculus)

One area of math that I’ve always been enamored with is the proof of numbers. The simplicity of the starting …Continue reading »

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Forecasting with daily data

September 16, 2013
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Forecasting with daily data

I’ve had several emails recently asking how to forecast daily data in R. Unless the time series is very long, the simplest approach is to simply set the frequency attribute to 7. y <- ts(x, frequency=7) Then any of the usual time series forecasting methods should produce reasonable forecasts. For example library(forecast) fit <- ets(y) fc <- forecast(fit) plot(fc)...

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Profile Likelihood for New Jersey U.S. Senate Special Election

September 16, 2013
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Profile Likelihood for New Jersey U.S. Senate Special Election

As it stands right now Cory Booker has a very good chance of winning the New Jersey Special U.S. Senate election on October 16 to replace Frank Lautenberg and fill the remainder of his term for the next 15 months.  So with the election only about a month away I took advantage of some of

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Census Atlas Japan

September 15, 2013
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Census Atlas Japan

The 2011 Census Open Atlas project has been put on hold recently as various other research projects have intervened - more on these soon. However, over the summer  Chris Brunsdon and I have taken a research trip to Ritsumeikan University (Japan) where we visited Keiji Yano and Tomoki Nakaya. As part of this trip I began developing...

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Census Atlas Japan

September 14, 2013
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Census Atlas Japan

The 2011 Census Open Atlas project has been put on hold recently as various other research projects have intervened - more on these soon. However, over the summer  Chris Brunsdon and I have taken a research trip to Ritsumeikan University (Japan) where we visited Keiji Yano and Tomoki Nakaya. As part of this trip I began developing...

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Monty Hall (oh no, not again)

September 13, 2013
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Monty Hall (oh no, not again)

Quite frequently, someone on the internet discovers the Monty Hall paradox, and become so enthusiastic that it becomes urgent to publish an article – or a post – about it. The latest example can be http://www.bbc.co.uk/news/magazine-24045598. I won’t blame them, I did the same a few years ago (see http://freakonometrics.hypotheses.org/776, or http://freakonometrics.hypotheses.org/775, in French). My point today is that the...

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Non-observable vs. observable heterogeneity factor

September 11, 2013
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Non-observable vs. observable heterogeneity factor

This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple example. Let  denote the height of a person. Consider the following dataset >...

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