exploratorydataanalysis.blogspot.com exploratorydataanalysis.blogspot.com

exploratorydataanalysis.blogspot.com

Exploratory Data Analysis

Wednesday, April 8, 2009. To motivate the issues on the next EDA topic, I collected the ages of all 651 pitchers who played major league baseball during the 2008 season. I have a vector age that contains the ages in years for these pitchers. A histogram is a standard way of graphing this data.  Before I graph, I should select reasonable bins; I could use the default selection of bins chosed by the R hist command, but I typically like more control over my graphical displays. What do I see in this display?

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Exploratory Data Analysis | exploratorydataanalysis.blogspot.com Reviews
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Wednesday, April 8, 2009. To motivate the issues on the next EDA topic, I collected the ages of all 651 pitchers who played major league baseball during the 2008 season. I have a vector age that contains the ages in years for these pitchers. A histogram is a standard way of graphing this data.  Before I graph, I should select reasonable bins; I could use the default selection of bins chosed by the R hist command, but I typically like more control over my graphical displays. What do I see in this display?
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1 binning baseball ages
2 hist age breaks=cutpoints
3 hist age breaks=cutpoints freq=false
4 posted by
5 jim albert
6 40 comments
7 fit=medpolish web hits
8 fit$overall
9 fit$row
10 saturday
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binning baseball ages,hist age breaks=cutpoints,hist age breaks=cutpoints freq=false,posted by,jim albert,40 comments,fit=medpolish web hits,fit$overall,fit$row,saturday,fit$col,fit$residuals,1 comment,smoothing on r,smooth 3rssh twice=function data
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Exploratory Data Analysis | exploratorydataanalysis.blogspot.com Reviews

https://exploratorydataanalysis.blogspot.com

Wednesday, April 8, 2009. To motivate the issues on the next EDA topic, I collected the ages of all 651 pitchers who played major league baseball during the 2008 season. I have a vector age that contains the ages in years for these pitchers. A histogram is a standard way of graphing this data.  Before I graph, I should select reasonable bins; I could use the default selection of bins chosed by the R hist command, but I typically like more control over my graphical displays. What do I see in this display?

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exploratorydataanalysis.blogspot.com exploratorydataanalysis.blogspot.com
1

Exploratory Data Analysis: November 2008

http://exploratorydataanalysis.blogspot.com/2008_11_01_archive.html

Friday, November 21, 2008. Some of you appear to be confused on this "binning" homework.  Let's outline what you are supposed to do in this assignment. 1  First you bin your data and construct a histogram.  This is easy (I hope).  The histogram gives you counts of each bin. By the way, a rootogram is just like a histogram, but you are graphing the root counts against the bins.  (Why do you do this? 160;Look at the notes.). S$counts are the observed bin counts. S$expected are the expected counts. 160;Well...

2

Exploratory Data Analysis: Smoothing Free Throw Percentages

http://exploratorydataanalysis.blogspot.com/2009/03/smoothing-free-throw-percentages.html

Monday, March 16, 2009. Smoothing Free Throw Percentages. I hope you had a nice spring break. My son's tennis team is flying to Florida this week - I wish I could join him. Since March Madness is starting, I thought it would be appropriate to talk about basketball data. There was an interesting article about free-throw shooting that recently appeared in the New York Times. See the article here. Is that really true? Here are the shooting percentages graphed as a function of year. What patterns do we see?

3

Exploratory Data Analysis: October 2008

http://exploratorydataanalysis.blogspot.com/2008_10_01_archive.html

Thursday, October 30, 2008. My Smoothing Tribute to the Phillies. Last night was an exciting night.  My team, the Philadelphia Phillies, won the World Series for only the second time in their history that started in 1883. So it seems appropriate to give a statistical tribute to the Phillies. I collected the Phillies winning percentage (percentage of games won) for each season in their history.  Below I plot the winning percentage against the season year. Plot(Year,Win.Pct,pch=19,col="red"). 2  But t...

4

Exploratory Data Analysis: April 2009

http://exploratorydataanalysis.blogspot.com/2009_04_01_archive.html

Wednesday, April 8, 2009. To motivate the issues on the next EDA topic, I collected the ages of all 651 pitchers who played major league baseball during the 2008 season. I have a vector age that contains the ages in years for these pitchers. A histogram is a standard way of graphing this data.  Before I graph, I should select reasonable bins; I could use the default selection of bins chosed by the R hist command, but I typically like more control over my graphical displays. What do I see in this display?

5

Exploratory Data Analysis: January 2009

http://exploratorydataanalysis.blogspot.com/2009_01_01_archive.html

Saturday, January 31, 2009. Letter values by group. In the next topic, we are talking about comparing groups.  The first step is to construct some graph (such as a stemplot or boxplot or dotplot) across groups and the next step is to compute summaries (such as letter values) for each group.  Here are some comments about using R to do this stuff. By the way, this data frame is organized with two variables, response (here time) and. Boxplot(time age,ylab="Time",xlab="Group"). 2  SUMMARIES BY GROUP. To use ...

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Exploratory Data Analysis

Wednesday, April 8, 2009. To motivate the issues on the next EDA topic, I collected the ages of all 651 pitchers who played major league baseball during the 2008 season. I have a vector age that contains the ages in years for these pitchers. A histogram is a standard way of graphing this data.  Before I graph, I should select reasonable bins; I could use the default selection of bins chosed by the R hist command, but I typically like more control over my graphical displays. What do I see in this display?

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