The power, and danger, of visualizations

August 7, 2013
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The power, and danger, of visualizations

I recently posted about visualizing the voting patterns of senators. In the post, I scraped voting data for each senator on every vote in the 113th Congress from the Senate website, and then assigned a code of 0 for a no vote on a particular issue, 1 ...

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Are your data normal? Hint: no.

August 7, 2013
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Are your data normal?  Hint: no.

One of the frequently-asked questions over at the statistics subreddit (reddit.com/r/statistics) is how to test whether a dataset is drawn from a particular distribution, most often the normal distribution.There are standard tests for this sort of thin...

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How to use the Autocorreation Function (ACF)?

August 7, 2013
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How to use the Autocorreation Function (ACF)?

The Autocorrelation function is one of the widest used tools in timeseries analysis. It is used to determine stationarity and seasonality. Stationarity: This refers to whether the series is “going anywhere” over time. Stationary series have a constant value over time. Below is what a non-stationary series looks like. Note the changing mean. And below […]

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Trends in the Analytics Job Market

August 7, 2013
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Trends in the Analytics Job Market

Tracking the job market for statistics, analytics, data mining and the like used to be a major undertaking. However, on November 10, 2011 the world’s largest web site for job postings, Indeed.com, released a tool that allows you to examine trends … Continue reading →

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When you’re planning on fitting a model, build up to it by fitting simpler models first. Then, once you have a model you like, check the hell out of it

August 7, 2013
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In response to my remarks on his online book, Think Bayes, Allen Downey wrote: I [Downey] have a question about one of your comments: My [Gelman's] main criticism with both books is that they talk a lot about inference but not so much about model building or model checking (recall the three steps of Bayesian […]The post When you’re planning on fitting a model, build up to it by fitting…

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Simply Statistics #JSM2013 Picks for Wednesday

August 7, 2013
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Sorry for the delay with my session picks for Wednesday. Here's what I'm thinking of: 8:30-10:20am: Bayesian Methods for Causal Inference in Complex Settings (CC-520a) or Developments in Statistical Methods for Functional and Imaging Data (CC-522bc) 10:30am-12:20pm: Spatial Statistics for Environmental Health Studies … Continue reading →

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Stutter steps, and functional legends

August 7, 2013
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Stutter steps, and functional legends

Dona Wong asked me to comment on a project by the New York Fed visualizing funding and expenditure at NY and NJ schools. The link to the charts is here. You have to click through to see the animation. Here...

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The power, and danger, of visualizations

August 7, 2013
By
The power, and danger, of visualizations

I recently posted about visualizing the voting patterns of senators. In the post, I scraped voting data for each senator on every vote in the 113th Congress from the Senate website, and then assigned a code of 0 for a no vote on a particular issue, 1 for a yes vote, 2 for abstention, and 3 if the senator was not in office at the time of the vote (ie,…

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Alternate ways to simulate multinomial data

August 7, 2013
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Alternate ways to simulate multinomial data

My previous post described the multinomial distribution and showed how to generate random data from the multinomial distribution in SAS by using the RANDMULTINOMIAL function in SAS/IML software. The RANDMULTINOMIAL function is simple to use and implements an efficient algorithm called the sequential conditional marginal method (see Gentle (2003), p. [...]

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JSM 2013 – Tuesday

August 7, 2013
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JSM 2013 – Tuesday

The Joint Statistical Meeting in Montreal has proven to be very good.   Here are a few highlight from Tuesday’s sessions.  There is one major problem that exists and that is there are too many good sessions to attend.  During one time block I had six session that I wanted to go to.  Unfortunately, it […]

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What Means Mean

August 7, 2013
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What Means Mean

Data is often reported as a single number. Unemployment rates, housing prices, crime, etc. are all boiled down to single numbers that average over a large population. But averages, or means, hide much of the richness of the underlying data, and without at least a sense of the spread of the data values, are largely meaningless.

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What did Nate Silver just say? Blogging the JSM

August 6, 2013
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What did Nate Silver just say? Blogging the JSM

Nate Silver gave his ASA Presidential talk to a packed audience (with questions tweeted[i]). Here are some quick thoughts—based on scribbled notes (from last night). Silver gave a list of 10 points that went something like this (turns out there were 11): 1. statistics are not just numbers 2. context is needed to interpret data […]

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Cronbach to the future

August 6, 2013
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Cronbach to the future

One fascinating thing about working in the area of psychological statistics is how hard it is to move people away from reliance on bad, inefficient or otherwise problematic methods. My own view - informed to some extent by the literature, by experience...

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JSM 2013 – Monday

August 6, 2013
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JSM 2013 – Monday

I am currently attending the 2013 Joint Statistical Meeting in Montreal. I will try to share a few of the things that I take away each day. Last night (Monday) I attended the JSM keynote speaker with Nate Silver and it proved to be a very interesting discussion.  Silver is best known for his work […]

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Read the source code

August 6, 2013
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Read the source code

The other day, there was a bit of a twitter conversation about qqline in R. It made me think: how exactly is the line produced by qqline chosen? I seemed to recall that the line was through the first and third quartiles. An advantage of R is that you can just type the name of […]

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G8 and Open Data

August 6, 2013
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G8 and Open Data

Open Data is a top priority now: G8 leaders signed the Open Data Charter on 18 June 2013. ‘The Open …Continue reading »

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New words of 1917

August 6, 2013
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I happened to come across a little book, “Current Glossary: Words Coined Since the War.” Here are some of them: as’pi-rin, n. A white compound used as a drug in the cure of headaches and rheumatism. bob’go, n. A kind of antelope in Africa; its meat is good for food. cat’ta-lo, n. A cross between […]The post New words of 1917 appeared first on Statistical Modeling, Causal Inference, and Social…

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Create your own fine print

August 6, 2013
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Create your own fine print

Note: The winner of the Book Quiz Round 2 was announced on my book blog. Congratulations to the winners. You can get your own copy of Numbersense here. *** A common advice for anyone living in the U.S. is "read...

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Book quiz update, podcast

August 6, 2013
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The second winner of the Book Quiz is Nancy, from Washington DC. Congratulations! Both winners wrote back saying they feel like buying a lottery ticket. The odds of winning that is much lower, be warned. *** Joe Dager, from Business901, and I chatted about Numbersense recently, and the interview will show up soon as a podcast. If you didn't see it last December, he did a very nice interview with…

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Simply Statistics #JSM2013 Picks for Tuesday

August 6, 2013
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It seems like Monday was a big hit at JSM with Nate Silver's talk and all. Rafa estimates that there were about 1 million people there (+/- 1 million). Ramnath Vaidyanathan has a nice summary of the talk and the Q&A … Continue reading →

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Setting axes limits with googleVis

August 6, 2013
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Setting axes limits with googleVis

I posted about the various googleVis axis options for base charts, such as line, bar and area charts earlier, but I somehow forgot to mention how to set the axes limits.Unfortunately, there are no arguments such as ylim and xlim. Instead, the Google Ch...

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Random Variables from a non-Parametric distribution know their limits

August 6, 2013
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Random Variables from a non-Parametric distribution know their limits

You produce a non-parametric distribution. Then you obtain, say, 10 random variables (RV) from this non-parametric distribution- much the same way as you would obtain random variables from a (parametric) normal distribution with stated mean and variance. But unlike the parametric distribution, where our RVs would occur around the mean (our parameter), RVs from a […]

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Google Developers R Programming Video Lectures

August 5, 2013
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Google Developers R Programming Video Lectures

Google Developers recognized that most developers learn R in bits and pieces, which can leave significant knowledge gaps. To help fill these gaps, they created a series of introductory R programming videos. These videos provide a solid foundation for p...

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