Resources
TEACHING RESOURCES
Part 2
Ginger Holmes Rowell
CAUSEweb: Searching by Pedagocial Method
Pedagogic Resource Library
CAUSEweb, through a partnership with SERC (http://serc.carleton.edu/index.html) has developed a library of
pedagogic modules for statistics educators. Each module features a particular pedagogic methodology. Each
module describes the what, why, and how of the particular teaching method. SERC vets these modules with
pedagogic experts; all pedagogic content is subject to a blind peer review process before it is made live.
A growing collection of classroom activities is included within each pedagogic module. The result is an enhanced
collection that allows users to seamlessly browse between pedagogic content and classroom activities.
Coke vs. Pepsi Taste Test: Experiments and Inference about Cause
This lesson plan and activity are based on material from the NSF-funded AIMS Project (Garfield, delMas and
Zieffler, 2007). For more information contact Joan Garfield at jbg@umn.edu
This material is replicated on a number of sites as part of the SERC Pedagogic Service Project
Summary
The Coke vs. Pepsi Taste Test Challenge has students design and carry out an experiment to determine whether or
not students are able to correctly identify two brands of cola in a blind taste test. In the first stage of the activity
students design and conduct the experiment. In the second part of the activity students use Sampling SIM
software (freely downloadable from http://www.tc.umn.edu/~delma001/stat_tools/) to simulate and gather
information on what would be expected under chance conditions (i.e., if students obtained correct answers only by
guessing). The students then compare the observed results to the chance results and make an inference about
whether a given student can in fact correctly identify Coke and Pepsi in a blind taste test. Finally, the experiment
is critiqued in terms of how well it met the standards for a good experiment.
This activity allows students to gain a better understanding of the experimental process and causality through
considering control, random assignment, and possible confounding variables. The activity also allows students to
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begin to understand the process of hypothesis testing by comparing their observed results of the taste test to the
results obtained through Sampling SIM (which model would be obtained by chance). Students make an inference
about whether particular students in their class can truly tell the difference between Coke and Pepsi by reasoning
about how surprising the observed results are compared to the simulated distribution of correct identifications by
guessing. The activity also provides an opportunity for discussing generalizability to a population.
Learning Goals
 To learn the characteristics of a well defined experiment.
 To learn the difference between an experiment and an observational study.
 To learn to recognize instances of confounding.
 To learn to understand and recognize instances of experimental control.
 To learn that randomizing the assignment of treatments protects against confounding and makes cause and
effect statements possible.
 To build the underpinnings of inference.
 To understand the process of hypothesis testing by comparing the observed results to the results obtained
under chance conditions.
Context for Use
This activity can be used in a unit of introductory statistics on producing data through experiments. It could also
be used in a unit on one sample tests of proportions.
Description and Teaching Materials
This activity takes place in two stages as described below.
Roles: Within each group of four students, assign each student to one of the following roles.
 Tasters--those who think they can tell the difference (blind to test).
 Runners--those who run cups of cola from room to hall (blind to test).
 Recorders--they record results of tasters decisions about whether they are tasting Coke or Pepsi.
 Pourers--remain in the classroom as pourers/observers.
The following materials are needed:
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10 Dixie cups per group for taste testing
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8 additional Dixie cups for clearing the palate
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Coke and Pepsi (4 cans of each is enough for 8 groups)
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Recorder slips for each group
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Coke/Pepsi pourer slips, where each group is given a random order of Coke and Pepsi over 10 trials
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Sampling SIM software loaded on the computer or another simulation program or applet (see Available
Technologies)
Copy of Student Handout (Microsoft Word 43kB Feb25 07)
Stage One of Activity: Designing and Conducting the Experiment
First, students are asked to consider how to design an experiment that will allow them to determine if anyone can
correctly identify two different brands of cola in a blind taste test. After a discussion of various methods, a plan is
introduced to use in conducting a taste test.
Students are asked to self identify who can correctly identify Coke or Pepsi in a blind taste test. Groups are then
formed with one of these students in each group to be the tester. Groups of four work best. Each group member
has one of the following roles:
First, the tasters, the recorders, and the runners will leave the room. The pourers are produce random series of
Coke or Pepsi using a coin to determine what the tasters would taste. The pourers will be the only members of the
group knowledgeable of the condition. The pourers will pour the appropriate drinks into paper cups, and leave
them in a row to be tasted at their table. These people may switch groups when the taste testing begins so they
will not know the order of colas to be tested.
Next, the runners will bring the first Dixie cups with cola to their group taster. The tasters will taste the drink and
make a decision about whether they think it is Coke or Pepsi that they are drinking. The recorder will keep track of
the taster's decision. The tasters will cleanse their palettes in between trials by taking a drink of water.
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Repeat the above process 4 more times for a total of 5 trials. At the end. The results are reveled and the number
of correctly identified colas is tallied for each taster.
Stage Two of Activity: Analyzing the class data
First, students discuss the results, being asked if they think any of the results suggest that a student is doing
better than just guessing. They are asked what kind of data would be expected if they were just guessing. This
leads to simulating data for the situation of guessing (p = .5, n = 5 trials). Use Sampling SIM (or another
simulation program or applet) to simulate the Coke/Pepsi activity, simulating data for 500 trials. Then students
can compare the number of correct guesses to this distribution to see if their score is due to chance (in the
middle) or surprising (in one of the tails). A final discussion involves critiquing the experiment and talking about
what could have made it better (e.g., more tastes).
Teaching Notes and Tips
Try to make sure that you have one person in each group that thinks they can distinguish between Coke and
Pepsi. Try to have students make conjectures about what they would expect before gathering or simulating data.
Assessment
Have students discuss or write answers to the following questions:
 How were three elements of a good experiment (random assignment, control, and replication) included or not
included in this experiment?
 Describe any possible sources of confounding in the experiment.
 Can you generalize the findings of this experiment to all students at this university? Why or why not?
References and Resources
NSF-Funded AIMS Project (J. Garfield, R. delMas, and A. Zieffler, University of Minnesota)
Coke/Pepsi Taste Test – Student Handout
This activity is based on a lesson from the NSF-funded AIMS project (Garfield, delMas and Zieffler).
Part I: Designing the Study
How could you design a study that would determine if someone could actually tell the difference
between Coke and Pepsi? Discuss a plan for this and write it down.
Carrying out the study
The class will share ideas and come up with one design to use in carrying out a taste test. One
person in your group will be selected to taste colas and try to identify them correctly.
How many times did the taster in your group correctly identify the colas? __________
Part II: Critiquing the Study
1. Was this taste test an experiment or an observational study? Explain.
2. Critique the study on each of the three elements of a good experiment (random assignment,
control, and replication). Which were met in our study? Which were not? Explain.
3. What are three ways in which we could improve our study for next time? How would each of these
suggestions improve the study?
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Part III: The Guessing Model
1. What if your taster really couldn’t tell the difference between Coke and Pepsi, but was just
guessing? How many times would you expect him/her to correctly guess the correct cola brand?
Why?
2. If we replicated the experiment many times, and each time a taster was guessing, then each time
the number of correct guesses would be due to change. On average, how many correct guesses
would you expect?
3. What would the distribution of correct identifications look like? Sketch a possible picture of these
results using dots to represent each total number of correct guesses out of five tastes
4. Could a student correctly identify all five tastes just by guessing? Why or why not?
Use Sampling Sim to produce a graph of data for the kinds of results we would get just due to chance
(if a person was just guessing).
5. How does the graph of the results that SamplingSIM produced compare with the sketch you
created earlier?
6. According to the graph, what it he most likely number of correct responses a taster could come up
with just be guessing?
7. What are some unlikely values (the number of correct identifications that don’t occur as often if a
student was only guessing)?
8. Where does your taster’s actual result fit on the graph produced by SamplingSIM? In the tails? In
the middle?
9. Based on your answer to Question 8, is it likely or unlikely that your taster would have gotten the
result he/she did if he/she was just guessing? Explain.
Gallery Walk
Gallery Walk
Compiled by Mark Francek (more info) at Central Michigan University (more info)
What is Gallery Walk? --a discussion technique for active engagement
Gallery Walk gets students out of their chairs and actively involves them in synthesizing important concepts, in
consensus building, in writing, and in public speaking. In Gallery Walk teams rotate around the classroom,
composing answers to questions as well as reflecting upon the answers given by other groups. Questions are
posted on charts or just pieces of paper located in different parts of the classroom. Each chart or "station" has its
own question that relates to an important class concept. The technique closes with a oral presentation or "report
out" in which each group synthesizes comments to a particular question. learn more here
Why use Gallery Walk? --promotes higher order thinking, oral/written presentation
skills, and team building
Gallery Walk is flexible and has many benefits. Gallery Walk can be organized for a simple fifteen minute ice
breaker or for a week long project involving graded oral and written reports. The technique encourages students to
speak and write the language of earth science rather than just hearing it from the instructor. In addition to
addressing a variety of cognitive skills involving analysis, evaluation, and synthesis, Gallery Walk has the
additional advantage of promoting cooperation, listening skills, and team building. learn more here
How to use Gallery Walk? --student teams rotate between posted charts
In Gallery Walk student teams rotate to provide bulleted answers to questions posted on charts arranged around
the classroom. After three to five minutes at a chart or "station" the team rotates to the next question. Gallery
Walk works best with open ended questions, that is, when a problem, concept, issue, or debate can be analyzed
from several different perspectives. In this section find a variety of instructional resources such as preparing
students for this technique, a step by step guide for using Gallery Walk, evaluation rubrics, and challenges in
implementing the technique. learn more here
Gallery Walk examples --a variety of sample questions for a variety of earth science
topics
Find examples of Gallery Walk questions for the following categories: Atmosphere, Biosphere, Climate System,
Earth History and Time, Earth Surface, Energy and Cycles, Human Dimensions, Hydrosphere and Cryosphere,
Oceans, Solar System, Solid Earth. Complete sample exercises are also included for a Gallery Walk involving
weather map analysis and soil morphology. learn more here
References on Gallery Walk
Find journal and web references relating to Gallery Walk. learn more here
TI Activities Exchange
Collections: TI – Activities Exchange
http://education.ti.com/educationportal/activityexchange/activity_list.do
TI Activities Exchange
“Snail Mail” Postal Rate History
Submitted to the TI-Exchange by Ann Conway, retrieved October 5, 2009.
(http://education.ti.com/educationportal/activityexchange/Activity.do?cid=US&aId=8419)
Postal rates have been figured by the ounce since July 1, 1885. Since then, the rates have been:
November 3, 1917
July 1, 1919
July 6, 1932
August 1, 1958
January 7, 1963
January 7, 1968
May 16, 1971
March 2, 1974
December 31, 1975
May 29, 1978
March 22, 1981
November 1, 1981
February 17, 1985
April 3, 1988
February 3, 1991
January 1, 1995
January 10, 1999
January 7, 2001
June 30, 2002
January 3, 2006
3 cents
2 cents
3 cents
4 cents
5 cents
6 cents
8 cents
10 cents
13 cents
15 cents
18 cents
20 cents
22 cents
25 cents
29 cents
32 cents
33 cents
34 cents
37 cents
39 cents
Based on this historical data, develop a model and predict the cost of mailing a one ounce first class
letter in 2015. EXPLAIN your reasoning.
a) Model:
b) Cost
c) Reasoning
Resources
Finding and Using Real Data: Examples of Data Sources
Workshop on Teaching Introductory Statistics
Myrtle Beach, SC
Friday, October 23, 2009
Ginger Holmes Rowell, Middle Tennessee State University
Not “Ready”
 American FactFinder http://factfinder.census.gov/home/saff/main.html?_lang=en
 Bureau of Economic Analysis http://www.bea.gov/
 Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/
 Centers for Disease Control http://www.cdc.gov/
 FedStats http://www.fedstats.gov/
 National Center for Catastrophic Sport Injury Research http://www.unc.edu/depts/nccsi/
 Olympic Games Results http://www.sporting-heroes.net/athleticsheroes/stats_athletics/olympics/olympics.asp
“Ready”
 The Data and Story Library http://lib.stat.cmu.edu/DASL/
 Chance Data Sets http://www.dartmouth.edu/~chance/teaching_aids/data.html
 Examples and Their Data Sets http://www.uvm.edu/~dhowell/StatPages/Examples.html
 JASA Data Sets http://lib.stat.cmu.edu/jasadata/
 JSE Data Archive http://www.amstat.org/publications/jse/jse_data_archive.html
 NIST Data Archives http://www.itl.nist.gov/div898/strd/general/dataarchive.html
 Virtual Laboratories in Probability and Statistics: Data Sets
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http://www.math.uah.edu/stat/data/index.xhtml
Real Time Online Hands-on Activities
http://stat.cst.cmich.edu/statact/real_time/online_analysis/data_download.html
“Ready with Activities”
 DIG Stat Activities http://www.cvgs.k12.va.us/DIGSTATS/Gmain.html
 Statistical Education Resource Kit http://www.stat.psu.edu/old_resources/bydata.htm
 Data Cleaning Assignment http://academic.csuohio.edu/holcombj/clean/cleaningassignment.htm
 Explanatory Style and Athletic Performance
http://www.uvm.edu/~dhowell/StatPages/Swimming/Swimming.html
Resources
Online Resources: A Few Examples by Resource Type
Workshop on Teaching Introductory Statistics
Myrtle Beach, SC
Ginger Holmes Rowell, Middle Tennessee State University
Collection
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Rossman/Chance Applet Collection by A. Rossman, B. Chance, F. Garcia & C. Lima
http://www.rossmanchance.com/applets/
Statistical Java by C. Anderson-Cook, s. Dorai-Raj, T. Robinson, B. Noble
http://www.causeweb.org/repository/statjava/
The Data and Story Library by M. Hutcheson, M. Meyer, C. Olson, P. Velleman, & J. Walker
http://lib.stat.cmu.edu/DASL/
Electronic Encyclopedia of Statistical Examples and Exercises, W.H. Freeman & Co.
http://www.whfreeman.com/eesee/eesee.html
Lecture and Lab Things by D. Howell http://www.uvm.edu/~dhowell/StatPages/More_Stuff/ClassLab.html
Rice Virtual Laboratories in Statistics by D. Lane http://onlinestatbook.com/rvls.html
ARTIST by J. Garfield, B. Chance, R. delMas https://app.gen.umn.edu/artist/
Case Study
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Instructor Reputation and Teacher Ratings by D. Lane
http://www.ruf.rice.edu/~lane/case_studies/instructor_reputation/index.html
Physical Strength and Job Performance Case Study by D. Lane
http://www.ruf.rice.edu/~lane/case_studies/physical_strength/index.html
UCLA Statistics Case Studies by UCLA Department of Statistics http://www.stat.ucla.edu/cases/
Markov vs. Markov: Divorce by the Numbers by C. Rump
http://ublib.buffalo.edu/libraries/projects/cases/markov/markov.html
Engineering Statistics Handbook Case Studies Edited by C. Croarkin and P. Tobias
http://www.itl.nist.gov/div898/handbook/eda/section4/eda42.htm
Drill and Practice or Quizzes
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Assessment Builder by R. DelMas, J. Garfield & B. Chance
https://app.gen.umn.edu/artist/assessment_builder_intro.html
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Biometry: Statistics for Ecology - Self Test by A. Georges
http://aerg.canberra.edu.au/envirostats/bm/selftest.htm
Design Case Study: Planning a Study by A. Rossman & B. Chance
http://statweb.calpoly.edu/chance/inspire/salk.doc
Practice Questions for Business Statistics by B. Schott http://www2.gsu.edu/~dscbms/ibs/qcontent.html
Statistics and Data Analysis for Public Policy and Sociology - Quizzes by D. Stangl
http://www.stat.duke.edu/~dalene/sta110/quizzes/
Reference Material
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Engineering Statistics Handbook Edited by C. Croarkin and P. Tobias
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http://www.itl.nist.gov/div898/handbook/index.htm
Statistical Engineering by C. Annis http://www.statisticalengineering.com/index.html
Statistical Literacy (http://www.statlit.org) Standardizing http://www.statlit.org/Standardizing.htm
Resources
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British Medical Journal: Statistics at Square One, 9th Edition, revised by M. Campbell,
http://bmj.bmjjournals.com/collections/statsbk/
Raw Data
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Short’s Parallax of the Sun Data by J. Short on K. Siegrist’s website
http://www.math.uah.edu/stat/data/Short.xhtml
Histogram of Coastal Waters, original work by Central Virginia Governor's School students A. Sliva (Class
of '01), E. Hildreth, M. O'Callaghan, and A. Yu (Class of 2002).
http://www.cvgs.k12.va.us/DIGSTATS/main/graphic/a_coastal_waters.htm
Diamond Ring Pricing Using Linear Regression by S. Chu,
http://www.amstat.org/publications/jse/v4n3/datasets.chu.html
One-way ANOVA of PTSD Data of Foa et al. (1991) by D. Howell
http://www.uvm.edu/~dhowell/StatPages/FoaFolder/Foa_Anova.html
Tutorials
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Sampling Distribution of the Mean Tutorial by WISE Team, D. Berger, Project Director
http://wise.cgu.edu/sdmmod/index.asp
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Random Variables Chapter of the DAU STAT Refresher by Center for Engineering, George Mason
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University http://cs.gmu.edu/cne/modules/dau/prob/randomvars/randomvars_frm.html
SPSS Tutorial – t tests in SPSS by L. Little http://courses.washington.edu/stat217/218tutorial1.html
Internal Validity Tutorial by D. Polson, C. Ng, and L. Grant. Supported by the Center for Psycholoyg,
Athabasca Univeristy, http://psych.athabascau.ca/html/Validity/index.shtml
Lecture/Presentation
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Do You Hear What I Hear? by NCTM Illuminations
http://illuminations.nctm.org/LessonDetail.aspx?ID=L489
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Probability, Genetics, and the Human Condition (Day 2) by R.Annis, M.Blundin, E.Greiling,
G.Layman, A.Roussy, S.Shirack, S.Harper, M.Timmerman
http://teacherlink.org/content/math/interactive/probability/lessonplans/genetics/day2-gen/home.html
Boxplot Analysis of Cricket Data original work by Central Virginia Governor's School students J. Edgar and
M. King (Class of '99) http://www.cvgs.k12.va.us/DIGSTATS/main/graphic/a_3cri.html
Against All Odds Inside Statistics Online Video Series by the Consortium for Mathematics and Its
Applications and Chedd-Angier http://www.learner.org/resources/series65.html
Statistical Education Resource Kit: Probability (Lecture Notes) by H. McGrath
http://www.stat.psu.edu/old_resources/ClassNotes/hrm_06/index.htm
Lecture Demonstration
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Normal Distribution, Interactive Statistics by Lind, Marchal, Wathen, 12Ed. http://highered.mcgrawhill.com/sites/dl/free/0072868244/124727/NormalDistribution.html
Time-Axis Fallacy and Bayes Theorem by R. Annis, M. Blundin, E. Greiling, G. Layman, A. Roussy, S.
Shirack, S. Harper, M. Timmerman
http://teacherlink.org/content/math/interactive/probability/lessonplans/bayes/home.html
Normal Distribution PowerPoint Slides by L. Simon
http://www.stat.psu.edu/old_resources/ClassNotes/ljs_08/sld001.htm
Games (Money Hall, Poker Hands, Correlations) by the CUWU Statistics Program
http://www.stat.uiuc.edu/courses/stat100/cuwu/Games.html
Resources
Laboratory Activity
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Star Library Resource: Regression, Residuals, Why? by J. Miller
http://www.causeweb.org/repository/StarLibrary/activities/miller2001/
How Fair is Your Die, from Mrs. Smart’s Homepage http://www.lhs.logan.k12.ut.us/~jsmart/fairdice.htm.
ANOVA on Hardness Data original work by Central Virginia Governor's School student S. Sims (Class of '05).
http://www.cvgs.k12.va.us/DIGSTATS/main/inferant/a_viton.htm
Bernoulli Trials by K. Siegrist http://www.math.uah.edu/stat/bernoulli/index.xhtml
Homework Assignment
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Introduction to Hypothesis Testing – The Z-Test by D. Berger, C. Aberson, M. Healy, V. Romero and D.
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Kyle http://wise.cgu.edu/hypothesis/
Project #1: Data Summary by J. Holcomb http://academic.csuohio.edu/holcombj/projects/ncsum.pdf
Practice Problems t-tests by L. Woolf http://www.webster.edu/~woolflm/ttest.html
Data Cleaning Assignment by J. Holcomb http://academic.csuohio.edu/holcombj/clean/cleaningassignment.htm
Data Source
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MSTE Data Archive by the Office for Mathematics, Science and Technology Education, University of Illinois at
Urbana-Champaign http://www.mste.uiuc.edu/malcz/DATA/ARCHIVE.html
Quantitative Environmental Learning Project by G. Langkamp and J. Hull
http://www.seattlecentral.edu/qelp/Data.html
Statistics Course Datasets from UCLA Researchers http://www.stat.ucla.edu/projects/datasets/
Villanova Nursing Research Dataset Archive, maintained by Tom Short
http://www.csc.villanova.edu/~short/MATC/
Simulations/Animations/Applets
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The Central Limit Theorem by C. Anderson-Cook, s. Dorai-Raj, T. Robinson, B. Noble
http://www.causeweb.org/repository/statjava/
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Confidence Intervals, Interactive Statistics 12 Ed. by Lind, Marchal, Wathen http://highered.mcgrawhill.com/sites/dl/free/0072868244/124727/est_conApp.html
Correlation by C. Anderson-Cook, s. Dorai-Raj, T. Robinson, B. Noble
http://www.causeweb.org/repository/statjava/
Power of a Hypothesis Test Applet by R. Ogden http://www.stat.sc.edu/~ogden/javahtml/power/power.html
Buffon’s Needle by C. Stanton http://www.math.csusb.edu/faculty/stanton/probstat/buffon.html
Understanding ANOVA Visually by Thomas Malloy
http://www.psych.utah.edu/stat/introstats/anovaflash.html
Sampling SIM by B.Chance, J. Garfield, B. delMas http://www.tc.umn.edu/~delma001/stat_tools/software.htm
Analysis Tools
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StatCrunch by W. West http://www.statcrunch.com/