# Blog Archives

## R and SAS in the curriculum: getting students to "think with data"

January 6, 2016
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We're pleased to announce that a special issue of the American Statistician on "Statistics and the Undergraduate Curriculum" (November, 2015) is available at http://amstat.tandfonline.com/toc/utas20/69/4. Johanna Hardin (Pomona College) and Nick were the guest editors. There are a number of excellent and provocative papers that reinforce the importance of computing using tools...

## 2015.2: Did the New England Patriots experience a decrease in fumbles starting in 2007?

February 1, 2015
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Here's a timely guest entry from Jeffrey Witmer (Oberlin College). As the “Deflate Gate” saga was unfolding, Warren Sharp analyzed “touches per fumble” for NFL teams before and after 2006, when a rule was changed so that teams playing on the road could provide their own footballs (http://www.sharpfootballanalysis.com/blog/). Sharp noted that, for whatever reason, the...

## Example 2014.13: Statistics doesn’t have to be so hard! Resampling in R and SAS

November 17, 2014
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A recent post pointed us to a great talk that elegantly described how inferences from a trial could be analyzed with a purely resampling-based approach. The talk uses data from a paper that considered the association between beer consumption and mosqu...

## The Statistical Sleuth (second edition) in R

August 14, 2012
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For those of you who teach, or are interested in seeing an illustrated series of analyses, there is a new compendium of files to help describe how to fit models for the extended case studies in the Second Edition of the Statistical Sleuth: A Course in...

## Example 9.38: dynamite plots, revisited

July 16, 2012
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Dynamite plots are a somewhat pejorative term for a graphical display where the height of a bar indicates the mean, and the vertical line on top of it represents the standard deviation (or standard error). These displays are commonly found in many scientific disciplines, as a way of communicating group differences in means. Many...

## Example 9.37: (Mis)behavior of binomial confidence intervals

July 9, 2012
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While traditional statistics courses teach students to calculate intervals and test for binomial proportions using a normal or t approximation, this method does not always work well. Agresti and Coull ("Approximate is better than "exact' for interval estimation of binomial proportions". The American Statistician, 1998; 52:119-126) demonstrated this and reintroduced an...

## Example 9.30: addressing multiple comparisons

May 7, 2012
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We've been more sensitive to accounting for multiple comparisons recently, in part due to work that Nick and colleagues published on the topic. In this entry, we consider results from a randomized trial (Kypri et al., 2009) to reduce problem drinking ...

## Example 9.29: the perils of for loops

April 30, 2012
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A recent exchange on the R-sig-teaching list featured a discussion of how best to teach new students R. The initial post included an exercise to write a function, that given a n, will draw n rows of a triangle made up of "*", noting that for a beginner, this may require two for loops. For example,...

## Example 9.28: creating datasets from tables

April 23, 2012
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RThere are often times when it is useful to create an individual level dataset from aggregated data (such as a table). While this can be done using the expand.table() function within the epitools package, it is also straightforward to do directly within R.Imagine that instead of the individual level data, we had only the 2x2 table for the...

## Example 9.22: shading plots and inequalities

March 1, 2012
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A colleague teaching college algebra wrote in the R-sig-teaching list asking for assistance in plotting the solutions to the inequality x^2 - 3 > 0. This type of display is handy in providing a graphical solution to accompany an analytic one. RThe plot...