723 search results for "Register"

Upcoming Microsoft R Webinars

January 21, 2016
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A quick heads-up that I'll be presenting a live webinar on Thursday January 28, Introduction to Microsoft R Open. If you know anyone that should get to know R and/or Microsoft R Open, send 'em along. Here's the abstract: Data Science is a strategic initiative for most companies today, who seek to understand the wealth of data now available...

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Microsoft Launches Its First Free Online R Course on edX

January 14, 2016
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Today, Microsoft and DataCamp launched an exciting new course on edX.org covering the basics of the statistical programming language R. This four week course is free for everyone, and no prior knowledge in programming or data science is required.Make sure to watch the course promotion video: What sets this Introduction to R course apart from...

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Power analysis for default Bayesian t-tests

January 14, 2016
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Power analysis for default Bayesian t-tests

One important benefit of Bayesian statistics is that you can provide relative support for the null hypothesis. When the null hypothesis is true, p-values will forever randomly wander between 0 and 1, but a Bayes factor has consistency (Rouder, Speckman, Sun, Morey, & Iverson, 2009), which means that as the sample size increases, the Bayes Factor will tell you...

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EARL Conference 2016 – London Dates announced.

January 11, 2016
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EARL Conference 2016 – London Dates announced.

          Following the overwhelming success of the EARL 2015 Conference and the feedback received, the 2016 London Conference will again be held in the Tower Hotel and the dates are: 13th – 15th September 2016. EARL … Continue reading →

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iptools 0.3.0 (“Violet Packet”) Now on CRAN with Windows Support!

January 8, 2016
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iptools is a set of tools for working with IP addresses. Not just work, but work fast. It’s backed by Rcpp and now uses the AsioHeaders package by Dirk Eddelbuettel, which means it no longer needs to link against the monolithic Boost libraries and works on Windows! What can you do with it? One thing

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satRdays are coming

January 8, 2016
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satRdays are coming

It's been only around 2 months since the idea of community-driven R conferences was born, when Steph Locke first talked publicly about this cool conception, but I am pretty sure we will be able to attend at least one or two satRdays in 2016 -- as the project received many and very positive feedback on GitHub,...

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Climatic Change At A Glance

January 7, 2016
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Climatic Change At A Glance

Mmm. Lost a planet, Master Obi-Wan has. How embarrassing (Yoda, Attack Of The Clones) Some time ago I published this post in KDnuggets in which I analyze historical temperatures to show how we are gradually heading toward a warmer planet. Simple data science to obtain a simple (and increasingly accepted) conclusion: the global warming is … Continue reading...

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Error Control in Exploratory ANOVA’s: The How and the Why

January 1, 2016
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(This article was first published on The 20% Statistician, and kindly contributed to R-bloggers) In a 2X2X2 design, there are three main effects, three two-way interactions, and one three-way interaction to test. That’s 7 statistical tests.The probability of making at least one Type 1 error in a single ANOVA is 1-(0.95)^7=30%. There are earlier blog posts on this, but...

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Fraud Detection with R and Azure

December 22, 2015
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Fraud Detection with R and Azure

Detecting fraudulent transactions is a key applucation of statistical modeling, especially in an age of online transactions. R of course has many functions and packages suited to this purpose, including binary classification techniques such as logistic regression. If you'd like to implement a fraud-detection application, the Cortana Analytics gallery features an Online Fraud Detection Template. This is a step-by...

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Prediction Intervals for Poisson Regression

December 20, 2015
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Prediction Intervals for Poisson Regression

Different from the confidence interval that is to address the uncertainty related to the conditional mean, the prediction interval is to accommodate the additional uncertainty associated with prediction errors. As a result, the prediction interval is always wider than the confidence interval in a regression model. In the context of risk modeling, the prediction interval

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