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Source: Statistics Blog

Source: Statistics Blog

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Source: Xian Blog

Source: Statistics Blog

Source: Statistics Blog

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Above is my talk at the 2017 New York R conference. Look, no slides!
The talk went well. I think the video would be more appealing to listen to if they’d mixed in more of the crowd noise. Then you’d hear people laughing at all the right spots.
P.S. Here’s my 2016 NYR talk, and my 2015 NYR talk.
Damn! I’m giving away all my material for free. I’ll have to come up with some entirely new bits when they call [...]

Fri, May 26, 2017Source: Statistics Blog

(This article was originally published at Statistics – Win-Vector Blog, and syndicated at StatsBlogs.)
When working with big data with R (say, using Spark and sparklyr) we have found it very convenient to keep data handles in a neat list or data_frame.
Please read on for our handy hints on keeping your data handles neat.
When using R to work over a big data system (such as Spark) much of your work is over "data handles" and not actual data (data handles are objects that control access to remote data).
Data handles are a lot like sockets or file-handles in that they can [...]

Fri, May 26, 2017Source: Statistics Blog

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Paul Campos writes:
Thought these data were extreme enough to be of general interest.
OK, before you click on the link, here’s the story: Campos looked up the presidential campaign contributions at 11 top law firms. (I’m not sure where his data came from; maybe the same source as here?) Guess what percentage of contributions went to Mitt Romney in 2012? What about Donald Trump in 2016?
Make your guesses, then click on the link above to find out the answer.
The numbers are [...]

Fri, May 26, 2017Source: Statistics Blog

An incomprehensible (and again double) Le Monde mathematical puzzle (despite requests to the authors! The details in brackets are mine.):
A [non-circular] chain of 63 papers clips can be broken into sub-chains by freeing one clip [from both neighbours] at a time. At a given stage, considering the set of the lengths of these sub-chains, the collection of all possible sums of these lengths is a subset of {1,…,63}. What is the minimal number of steps to recover the entire set {1,…,63}? And what is the maximal length L of a chain of paper clips that allows this recovery in 8 [...]

Thu, May 25, 2017Source: Xian Blog

(This article was originally published at Psychological Statistics, and syndicated at StatsBlogs.)
In my Serious Stats blog I have a new post on providing CIs for a difference between independent R square coefficients.You can find the post there or go direct to the function hosted on RPubs. I have been experimenting with knitr but can't yet get the html from R Markdown to work with my blogger or wordpress blogs.
Please comment on the article here: Psychological Statistics
The post Serious Stats blog: CI for differences in independent R square coefficients appeared first on All About Statistics. [...]

Thu, May 25, 2017Source: Statistics Blog

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
ShinyStan is great, but I don’t always use it because when you call it from R, it freezes up your R session until you close the ShinyStan window.
But it turns out that it doesn’t have to be that way. Imad explains:
You can open up a new session via the RStudio menu bar (Session >> New Session), which should have the same working directory as wherever you were prior to running launch_shinystan(). This will let you work on whatever you need to work on [...]

Thu, May 25, 2017Source: Statistics Blog