# 59 search results for "ecdf"

## From spreadsheet thinking to R thinking

January 7, 2014
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Towards the basic R mindset. Previously The post “A first step towards R from spreadsheets” provides an introduction to switching from spreadsheets to R.  It also includes a list of additional posts (like this one) on the transition. Add two columns Figure 1 shows some numbers in two columns and the start of adding those The post From...

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## 2013 Summary

January 6, 2014
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2013 was a tough year. Trading was tough, with one of my strategies experiencing a significant drawdown. Research was tough – wasted a lot of time on machine learing techneques, without much to show for it. Also made some expensive mistakes, so all in all – it was a year I’d prefer I had avoided.

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## Using R to replicate common SPSS multiple regression output

December 4, 2013
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(This article was first published on Jeromy Anglim's Blog: Psychology and Statistics, and kindly contributed to R-bloggers) The following post replicates some of the standard output you might get from a multiple regression analysis in SPSS. A copy of the code in RMarkdown format is available on github. The post was motivated by this previous post that discussed using...

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## Maximum Likelihood versus Goodness of Fit

November 8, 2013
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$\{X_1,\cdots,X_n\}$

Thursday, I got an interesting question from a colleague of mine (JP). I mean, the way I understood the question turned out to be a nice puzzle (but I have to confess I might have misunderstood). The question is the following : consider a i.i.d. sample of continuous variables. We would like to choose between two (parametric) families for...

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## Post 4: Sampling the person ability parameters

October 8, 2013
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The previous post outlined the general strategy of writing a MH within Gibbs sampler by breaking the code into two levels: a high level shell and a series of lower-level samplers which do the actual work. This post discusses the … Continue reading →

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## The Problem with Percentiles

September 12, 2013
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The Problem with Percentiles Percentiles (or, more accurately, quantiles) are deeply embedded in the psyche of actuaries, statisticians and similar beasts. They are referred to implicitly in the Solvency 2 directive (Article 100, Value at Risk) without explanation. They are so ingrained...

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## TV Ratings Myths

August 28, 2013
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TV Show Cancellations: Myths and Models TV shows are amazing ways to waste time and, on occasion, the story is so good that you actually start to care. The problem is that some shows get cancelled before they jump the shark. Classic examples are shows like

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## Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

$Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R$

Introduction Continuing my recent series on exploratory data analysis (EDA), and following up on the last post on the conceptual foundations of empirical cumulative distribution functions (CDFs), this post shows how to plot them in R.  (Previous posts in this series on EDA include descriptive statistics, box plots, kernel density estimation, and violin plots.) I

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## Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Introduction Continuing my recent series on exploratory data analysis (EDA), this post focuses on the conceptual foundations of empirical cumulative distribution functions (CDFs); in a separate post, I will show how to plot them in R.  (Previous posts in this series include descriptive statistics, box plots, kernel density estimation, and violin plots.) To give you

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## Monitoring des médias 2

May 6, 2013
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(This article was first published on Learning Data Science , and kindly contributed to R-bloggers) Petit monitoring de notre observatoire des médias sur Twitter. Chez Mediapart : Le Monde Le Figaro Le parisien Vue globale Le code pour réaliser ce post : To leave a comment for the author, please follow the link and comment on their blog: Learning...

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