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<h1>273 andrew gelman stats-2010-09-13-Update on marathon statistics</h1>
<br/><h2>meta infos for this blog</h2><p>Source: <a title="andrew_gelman_stats-2010-273-html" href="http://andrewgelman.com/2010/09/13/update_on_marat/">html</a></p><p>Introduction: Frank Hansen updates  his story  and writes:
  
 Here is a link  to the new stuff.  The update is a little less than half way down the page.


1. used display() instead of summary()


2. include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon.


3. graph actual pace vs. fitted pace (color code newbie proxy)


4. estimate the model separately for newbies and non-newbies.


some incidental discussion of sd of errors.


There are a few things unfinished but I have to get to bed, I'm running the 2010 Chicago Half tomorrow morning, and they moved the start up from 7:30 to 7:00 because it's the day of the Bears home opener too.</p><br/><h2>Summary: the most important sentenses genereted by tfidf model</h2><p>sentIndex sentText sentNum sentScore</p><p>1 Frank Hansen updates  his story  and writes:     Here is a link  to the new stuff. [sent-1, score-0.308]
</p><p>2 The update is a little less than half way down the page. [sent-2, score-0.435]
</p><p>3 include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon. [sent-5, score-1.107]
</p><p>4 There are a few things unfinished but I have to get to bed, I'm running the 2010 Chicago Half tomorrow morning, and they moved the start up from 7:30 to 7:00 because it's the day of the Bears home opener too. [sent-11, score-0.841]
</p>
<br/>
<h2>similar blogs computed by tfidf model</h2><h3>tfidf for this blog:</h3><p>wordName wordTfidf (topN-words)</p>
<p>[('newbies', 0.445), ('pace', 0.319), ('proxy', 0.313), ('chicago', 0.243), ('bears', 0.203), ('opener', 0.203), ('incidental', 0.191), ('newbie', 0.191), ('half', 0.181), ('bed', 0.163), ('hansen', 0.154), ('updates', 0.151), ('non', 0.151), ('tomorrow', 0.14), ('sd', 0.14), ('morning', 0.128), ('separately', 0.127), ('frank', 0.122), ('color', 0.115), ('fitted', 0.113), ('update', 0.11), ('moved', 0.103), ('display', 0.103), ('home', 0.098), ('running', 0.089), ('previous', 0.089), ('code', 0.088), ('summary', 0.087), ('name', 0.081), ('actual', 0.081), ('include', 0.072), ('graph', 0.07), ('start', 0.067), ('day', 0.067), ('instead', 0.065), ('link', 0.065), ('estimate', 0.063), ('little', 0.059), ('whether', 0.058), ('story', 0.056), ('less', 0.053), ('discussion', 0.05), ('find', 0.049), ('used', 0.049), ('things', 0.045), ('model', 0.04), ('new', 0.036), ('way', 0.032), ('writes', 0.031), ('get', 0.029)]</p>
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<p>Introduction: Frank Hansen updates  his story  and writes:
  
 Here is a link  to the new stuff.  The update is a little less than half way down the page.


1. used display() instead of summary()


2. include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon.


3. graph actual pace vs. fitted pace (color code newbie proxy)


4. estimate the model separately for newbies and non-newbies.


some incidental discussion of sd of errors.


There are a few things unfinished but I have to get to bed, I'm running the 2010 Chicago Half tomorrow morning, and they moved the start up from 7:30 to 7:00 because it's the day of the Bears home opener too.</p><p>2 0.15675148 <a title="273-tfidf-2" href="../andrew_gelman_stats-2012/andrew_gelman_stats-2012-10-07-An_%28impressive%29_increase_in_survival_rate_from_50%25_to_60%25_corresponds_to_an_R-squared_of_%28only%29_1%25.__Counterintuitive%2C_huh%3F.html">1524 andrew gelman stats-2012-10-07-An (impressive) increase in survival rate from 50% to 60% corresponds to an R-squared of (only) 1%.  Counterintuitive, huh?</a></p>
<p>Introduction: I was just reading  an old post  and came across this example which I'd like to share with you again:
  
Here's a story of R-squared = 1%. Consider a 0/1 outcome with about half the people in each category. For.example, half the people with some disease die in a year and half live. Now suppose there's a treatment that increases survival rate from 50% to 60%. The unexplained sd is 0.5 and the explained sd is 0.05, hence R-squared is 0.01.</p><p>3 0.13299723 <a title="273-tfidf-3" href="../andrew_gelman_stats-2010/andrew_gelman_stats-2010-08-31-Predicting_marathon_times.html">245 andrew gelman stats-2010-08-31-Predicting marathon times</a></p>
<p>Introduction: Frank Hansen writes:
  
I [Hansen] signed up for my first marathon race. Everyone asks me my predicted time. The predictors online seem geared to or are based off of elite runners. And anyway they seem a bit limited.


So I decided to do some analysis of my own.
      
I was going to put together a web page where people could get their race time predictions, maybe sell some ads for sports gps watches, but it might also be publishable.


I have 2 requests which obviously I don't want you to spend more than a few seconds on.


1. I was wondering if you knew of any sports performance researchers working on performance of not just elite athletes, but the full range of runners. 


2. Can you suggest a way to do multilevel modeling of this. There are several natural subsets for the data but it's not obvious what makes sense. I describe the data below.


3. Phil (the runner/co-blogger who posted about weight loss) might be interested.


I collected race results for the Chicago marathon and 3</p><p>4 0.086519077 <a title="273-tfidf-4" href="../andrew_gelman_stats-2013/andrew_gelman_stats-2013-01-21-Finite-population_Anova_calculations_for_models_with_interactions.html">1686 andrew gelman stats-2013-01-21-Finite-population Anova calculations for models with interactions</a></p>
<p>Introduction: Jim Thomson writes:
  
I wonder if you could provide some clarification on the correct way to calculate the finite-population standard deviations for interaction terms in your Bayesian approach to ANOVA (as explained in your 2005 paper, and Gelman and Hill 2007).


I understand that it is the SD of the constrained batch coefficients that is of interest, but in most WinBUGS examples I have seen, the SDs are all calculated directly as sd.fin<-sd(beta.main[]) for main effects and sd(beta.int[,]) for interaction effects,  where beta.main and beta.int are the unconstrained coefficients, e.g. beta.int[i,j]~dnorm(0,tau).


For main effects, I can see that it makes no difference, since the constrained value is calculated by subtracting the mean, and sd(B[]) = sd(B[]-mean(B[])).


But the conventional sum-to-zero constraint for interaction terms in linear models is more complicated than subtracting the mean (there are only (n1-1)*(n2-1) free coefficients for an interaction b/w factors with n1 a</p><p>5 0.083752558 <a title="273-tfidf-5" href="../andrew_gelman_stats-2013/andrew_gelman_stats-2013-01-20-Ugly_ugly_ugly.html">1684 andrew gelman stats-2013-01-20-Ugly ugly ugly</a></p>
<p>Introduction: Denis Cote sends the  following , under the heading, "Some bad graphs for your enjoyment":
 
 
 
To start with, they don't know how to spell "color."  Seriously, though, the graph is a mess.  The circular display implies a circular or periodic structure that isn't actually in the data, the cramped display requires the use of an otherwise-unnecessary color code that makes it difficult to find or make sense of the information, the alphabetical ordering (without even supplying state names, only abbreviations) makes it further difficult to find any patterns.  It would be so much better, and even easier, to just display a set of small maps shading states on whether they have different laws.  But that's part of the problem—the clearer graph would also be easier to make!  To get a distinctive graph, there needs to be some degree of difficulty.
 
The designers continue with these monstrosities:
 
 
 
Here they decide to display only 5 states at a time so that it's really hard to see any big pi</p><p>6 0.081058249 <a title="273-tfidf-6" href="../andrew_gelman_stats-2010/andrew_gelman_stats-2010-12-16-2500_people_living_in_a_park_in_Chicago%3F.html">469 andrew gelman stats-2010-12-16-2500 people living in a park in Chicago?</a></p>
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<p>Introduction: Frank Hansen updates  his story  and writes:
  
 Here is a link  to the new stuff.  The update is a little less than half way down the page.


1. used display() instead of summary()


2. include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon.


3. graph actual pace vs. fitted pace (color code newbie proxy)


4. estimate the model separately for newbies and non-newbies.


some incidental discussion of sd of errors.


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<p>Introduction: Dan Kahan sends along  this  awesome graph (click on the image to see the whole thing):
 
   
 
and writes:
  
I [Kahan] saw it at  http://www.theatlantic.com/technology/archive/2012/04/the-100-year-march-of-technology-in-1-graph/255573/  , which misidentified the source (not "visual economics";   visualizingeconomics   .com ,  which attributes it  to  Nicholas Felton , who apparently condensed  this version , which I worry could cause a stroke). But it did have a good write-up that (I'm glad) caught my attention.


It made me [Kahan] start to wonder about what sorts of qualities of a technology will influence its dissemination & also about the availability of benchmarks for proliferation of various sorts of things (e.g, fads & trends, health-promoting behaviors, knowledge of a scientific discovery) that one could use to gauge how meaningful the apparent increase in rates of proliferation of these technologies has been over time. That in turn made me wonder whether — indeed, suspect th</p><p>3 0.64185333 <a title="273-lsi-3" href="../andrew_gelman_stats-2012/andrew_gelman_stats-2012-02-13-Help_with_this_problem%2C_win_valuable_prizes.html">1164 andrew gelman stats-2012-02-13-Help with this problem, win valuable prizes</a></p>
<p>Introduction: Corrected equation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This post is by Phil.
 
In the comments to  an earlier post , I mentioned a problem I am struggling with right now. Several people mentioned having (and solving!) similar problems in the past, so this seems like a great way for me and a bunch of other blog readers to learn something. I will describe the problem, one or more of you will tell me how to solve it, and you will win…wait for it….my thanks, and the approval and admiration of your fellow blog readers, and a big thank-you in any publication that includes results from fitting the model.  You can't ask fairer than that!
 
Here's the problem.  The goal is to estimate six parameters that characterize the leakiness (or air-tightness) of a house with an attached garage.  We are specifically interested in the parameters that describe the connection between the house and the garage; this is of interest because of the effect on the air quality in the house  if there are toxic chemic</p><p>4 0.62491119 <a title="273-lsi-4" href="../andrew_gelman_stats-2013/andrew_gelman_stats-2013-10-17-Cool_dynamic_demographic_maps_provide_beautiful_illustration_of_Chris_Rock_effect.html">2065 andrew gelman stats-2013-10-17-Cool dynamic demographic maps provide beautiful illustration of Chris Rock effect</a></p>
<p>Introduction: Robert Gonzalez  reports  on some  beautiful graphs  from John Nelson. Here's Nelson:
 
 
         The sexes start out homogenous, go super segregated in the teen years, segregate for business in the twenty-somethings, and re-couple for co-habitation years.  Then the lights fade into faint pockets of pink.   
 
  
I [Nelson] am using simple tract-level population/gender counts from the US Census Bureau. Because their tract boundaries extend into the water and vacant area, I used NYC's Bytes of the Big Apple zoning shapes to clip the census tracts to residentially zoned areas -giving me a more realistic (and more recognizable) definition of populated areas. The census breaks out their population counts by gender for five-year age spans ranging from teeny tiny infants through esteemed 85+ year-olds.
  
And here's Gonzalez:
  
Between ages 0 and 14, the entire map is more or less an evenly mixed purple landscape; newborns, children and adolescents, after all, can't really choose where the</p><p>5 0.62224615 <a title="273-lsi-5" href="../andrew_gelman_stats-2011/andrew_gelman_stats-2011-07-05-Different_goals%2C_different_looks%3A__Infovis_and_the_Chris_Rock_effect.html">787 andrew gelman stats-2011-07-05-Different goals, different looks:  Infovis and the Chris Rock effect</a></p>
<p>Introduction: Seth writes:
  
 Here's  my candidate for bad graphic of the year:


   


I [Seth] studied it and learned nothing. I have no idea how they assigned colors to locations. I already knew that there were more within-city calls than calls to individual distant locations — for example that there are more SF-SF calls than SF-LA calls. The researchers took a huge rich database and boiled it down to nothing (in terms of information value) — and I have a funny feeling they don't realize how awful this is and what a waste.


I send it to you because it isn't obvious how to do better — at least not obvious to them.
  
My reply:
 
My first reaction is to agree–I don't get anything out of this graph either! But let me step back.
 
I think it's best to understand this using the framework of  my paper with Antony Unwin , by thinking of the goals that are satisfied by different sorts of graphs.
 
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<p>Introduction: Frank Hansen updates  his story  and writes:
  
 Here is a link  to the new stuff.  The update is a little less than half way down the page.


1. used display() instead of summary()


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3. graph actual pace vs. fitted pace (color code newbie proxy)


4. estimate the model separately for newbies and non-newbies.


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 Stan home page:   http://mc-stan.org/ 
   
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Modeling Language
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