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Posts: 54
Registered: 12/6/04
rossman plus
Posted: Apr 26, 1996 2:10 PM
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If you have already received this, please forgive, and discard.*

There are three entries below in this very long file.
The first is on the Statistics Teacher Network newsletter, history
and how to subscribe (gratis publication).
The next is a review by Joan Garfield on Rossman's Workshop
Statistics book. And the third is a review by Bob Hayden on
Siegel and Morgan's book. Both of these reviews are to appear
in the spring issue of the STN newsletter.

Keep in touch.

Jerry Moreno
John Carroll University


In 1967, the ASA/NCTM Joint Committee on the Curriculum in Statistics
and Probability for grades K-12 was created by the American Statistical
Association and the National Council of Teachers of Mathematics.

It was given four responsibilities:

1) to support a newsletter, The Statistics Teacher Network, for communication
with precollege teachers.
2) to assist in securing funding to support curriuclum efforts for precollege
3) to develop strategies to promote statistics and probability in the
precollege classroom.
4) to provide leadership for the inclusion of statistics and probability
in assessment and curriculum efforts.

The committee consists of 7 members: 3 appointed by ASA, 3 by NCTM, and
one ASA staff person (the Director of Education).

Fred Mosteller was the first chair. Under his leadership, two publications
were created: a four volume series called Statistics by Example (no
longer in print); and,
the book ``Statistics: A Guide to the Unknown'' edited by Judith Tanur
(Wadsworth 1989, 3rd ed.).

In the 1980's, the Joint Committee created the highly successful project
called Quantitative Literacy. QL has grown into several different projects.
The first resulted in the publication of five volumes of hands-on
probability and
statistics materials for middle and high school programs. The Joint Committee
has been involved in other activities as well. For example, the committee
served in an advisory capacity in various facets of the creation of the
Advanced Placement Statistics program (first examination, May 1997).

One of the Joint Committee's responsibilities was to create a newsletter for
precollege teachers. The first issue of the ``Statistics Teacher Network''
newsletter appeared in September 1982
with Ann Watkins as editor. Beth Bryan took over the reigns three years
later with issue 10, followed by Jack Kinney in December 1988 with issue 19.
Jerry Moreno became editor with issue 31, fall 1992.

STN is a free publication whose purpose is to keep K-12 teachers informed of
statistical workshops, programs, and reviews of books, software, and
In addition, articles are included that describe statistical activities
that have been successful in the classroom. There are several thousand
school teachers on the mailing list, including college faculty and others
who are interested in statistical education in grades K-12.
Contributors come from all levels of statistical expertise; the Editor
is responsible for collecting all appropriate information to be

Currently (April 1996), to subscribe to STN, send your name and address
to; STN, c/o American Statistical Association,
1429 Duke St., Alexandria VA 22314-3402; 703-684-1221; fax 703-684-2036.

Past issues are not available. However, it is hoped that, by fall 1996,
selected past articles can be obtained electronically.

Contact Jerry Moreno for further information, including volunteering
to write or review articles.; Dept. of Mathematics
and Computer Science, John Carroll University, University Hts., OH 44118;
216-397-4681; fax 216-397-3033.


Book Review...

Workshop Statistics: Discovery with Data
Allan Rossman, Dickinson College, Pennsylvania
$25, 1995, 452pp., 0-387-94497-4,Jones and Bartlett, 800-832-0034,

Over the past few years I have written articles and given conference
presentations describing ways statistics courses should be redesigned
to better facilitate student learning. I have encouraged the use of
active learning strategies, problem solving using real data sets of
interest to students, small group discussions, writing assignments,
and appropriate technology. Allan Rossman's new introductory text,
Statistics Workshop, appears to have incorporated all of these important
components in a low-cost softcover textbook.

This book is distinguished from other introductory textbooks in several
important ways. There is a focus on "big ideas" rather than a large
collection skills, definitions, explanations, and techniques. Instead
of presenting students with methods of analyzing data and descriptions
of concepts, Workshop Statistics is designed to provoke students to
discover concepts themselves. When skills are presented it is because
there is a reason to use them: to carry out the next step in solving
a problem by analyzing data. Rossman explains that pages of expository
material or solved examples are omitted to emphasize "the idea that
students construct their own knowledge of statistical ideas as they
work through the activities." While most textbooks contain problem
sets at the end of the chapter, each focusing on a different (and
often contrived) set of data, the homework activities in this text
extend the types of activities conducted in class, examining a few
real data sets in more detail. An emphasis on "writing to learn" is
also embedded in the activities by continually asking students to
write about different aspects of data analysis and to summarize what
they have learned about a particular topic.

Workshop Statistics is divided into six units, each consisting of three
to five topics. Over 100 data sets are either generated by students or
included in tables (soon to be available on a data disk). Students
combine data explorations by hand with use of a computer or calculator
to generate graphs and statistics.

The first unit, Topic 1: "Exploring Data: Distributions," illustrates the
hands-on data exploration approach. This unit is divided into five topics,
each appropriate for one class session. On the first day of class students
make predictions and gather data about themselves (gender, political views,
opinions of statistics). They are guided through activities where they learn
how to generate and analyze data to describe the number of states and
countries they have visited. By the end of this first activity students
have learned to distinguish different types of variables and data and how
to construct and describe dotplots and histograms. In the next section
students are asked to compare and examine dotplots, allowing ideas to
emerge of center, spread, shape, and outliers as distinguishing features
of these plots. Topics 3 and 4 introduce more details on measures of center
and spread, and the fifth topic challenges students to integrate and use
these techniques in comparing distributions.

The next unit of the text explores relationship between two variables with
topics on graphing bivariate data, correlation and regression, and tables of
categorical data. Students are first shown scatterplots of data where they
develop their own ideas of association between variables. Later they analyze
data on space shuttle o-ring failures, peanut butter cost and quality, and
cars' fuel efficiency. By examining and manipulating different scatterplots
while viewing correlation coefficients, students construct ideas of how
different factors influence the correlation between variables. In an
activity comparing the average number of television sets per person and
life expectancy for 22 countries, students discover that correlation does
not imply causation.

The third unit, "Randomness", is quite a departure from the typical chapter
on probability in most introductory textbooks. Instead, this unit introduces
sampling distributions and then focuses on the normal distribution and the
central limit theorem. As Rossman explains in the introduction, there is no
formal treatment of probability. Instead, ideas of probability are introduced
in the context of simulation and random variation and as they apply to methods
of statistical inference.

Topic 4 introduces statistical inference first through confidence intervals
for proportions and then through significance tests. Topic 5 includes
important aspects of designing experiments and extends inference to
comparisons of two proportions. In the last unit, Inference From Data:
Measurements, student learn to make inferences involving one or two
population means.

This textbook not only looks exciting, it also works well with students.
I know because I used an earlier version of Workshop Statistics in a
class and have also observed Allan Rossman teaching a class at Dickinson
College. Students typically work in pairs or groups on the data exploration
activities after a whole-class orientation to the day's topic. While Allan
teaches his class in a room equipped with Macintosh computers, allowing
students to easily analyze data using Minitab, the format of the book
allows students to use any type of computer or graphing calculator either
during or outside of class. I have found students to enjoy the activities
and appreciate the varied and interesting data sets. Although students
initially resist writing long verbal descriptions, they eventually learn
how to do this and appear to value the process.

I encourage all statistics educators to seriously consider using this
innovative text. I believe that it provides an exemplary instructional
approach that should enable more students to overcome their fears about
learning statistics and to become statistically literate.

Reviewed by Joan Garfield
University of Minnesota
Minneapolis, Minnesota


Statistics and Data Analysis: An Introduction (2nd ed.)
Andrew F. Siegel and Charles J. Morgan
Wiley, 1995, $68, ISBN 0471574244

This book is the second edition of an underground classic first
published in 1988. The first edition (by Siegel alone) was
reviewed for Volume 26 of STN (December 1990) by Joan Garfield.
She used it for many years at the University of Minnesota, and
colleagues and I used it at Plymouth State College until last
summer. It recently showed up as one of the seven textbooks the
College Board is recommending for the new Advanced Placement Test
in Statistics. Even so, it is no secret that the book was not a
great success in the marketplace. The second edition attempts to
broaden the book's appeal. I think it succeeds, but sometimes at
the expense of muting a few of the virtues that made the first
edition so outstanding.

One of those virtues is the writing. The first edition was by
far the most readable introductory statistics text I have ever
used. It was also written in a warm and friendly tone that
remains unusual in statistics textbooks. The second edition
maintains a high level of readability. The warmth is somewhat

Another thing that set the book apart was its content. Although
the NCTM Standards suggest big changes in how mathematics is
taught, and smaller changes in what mathematics is taught, the
underlying mathematics has not changed much. One and one still
is two, and has been for quite some time. Statistics, on the
other hand, underwent a great revolution in the 1960's, a
revolution often linked with the name of John Tukey. One of the
first things I look for in a statistics textbook is whether there
is any sign that the author has heard about this revolution yet.
Because so many statistics textbooks are written by
non-statisticians, the news has spread very slowly. Andrew
Siegel was part of the Tukey revolution, and I think that is one
reason why the first edition was ahead of its time. All the good
K-12 statistics materials from NCTM and QLP are definitely
post-Tukey, but many college textbooks still are not. Caveat emptor.

One sign of Tukey's influence is the use of stem (and leaf) plots
and box (and whisker) plots. These are a necessary condition
for textbook adoption these days, but alas not a sufficient one.
A few of you may remember the "new math" era, when set ideas were
supposed to unify all of mathematics. We then saw textbooks that
sprouted an obligatory "Chapter 0" where set notation (not ideas)
was introduced, and then forgotten, and certainly never used to
unify the rest of the content. Similarly, we have reached the
point where most textbooks now mention the stem and leaf or
boxplot, but many really don't know what they are for, and so
never use them for anything.

Another way people characterize the Tukey revolution is in terms
of the "three R's" of post-Tukey statistics


Residuals are usually first encountered in the context of fitting
lines to data. There they are the (signed) distances between the
points and the fitted line. Analyzing them helps us to evaluate
how well our straight line model fits the data. Siegel and
Morgan introduce residuals very early -- the deviations from the
mean that figure in the computation of variance and standard
deviation are presented as residuals. Toward the end of the book
there is a masterful example of the use of residuals in
regression analysis. Data is presented on the average heights of
girls for ages 2-11. Height versus age looks like a nearly
perfect straight line, and the correlation is 0.997. Yet a graph
of the residuals shows pronounced curvature in the relationship,
something you would never see without examining the residuals!
(Although the book does not mention it, fitting a quadratic to
the data gives a residual plot that clearly indicates a cubic
component!) This is an example of one of the great strengths of
this book -- it not only shows you the latest techniques, it shows
them to you in examples that indicate what the technique does for
you and why it is important, rather than with examples that merely
show you the mechanics of carrying out the technique. Without
the "why", the "how" is useless.

Reexpression is more often called "transformation". Perhaps the
most traditional example of that is the fact that some
relationships are better plotted on logarithmic or
semilogarithmic graph paper. The TI-82 calculator uses such
transformations (taking logs of x or y or both) to fit a variety
of models to two-variable data. The first edition of Siegel
contains the best elementary introduction to the use of
transformations in statistics. They are introduced early in the
book and used in both the analysis of variance and regression
chapters. The second edition contains the second-best elementary
introduction to the use of transformations in statistics. The
initial coverage is cut about in half, and the applications to
regression have disappeared. This is especially unfortunate for
use in the high schools, where the logarithmic and exponential
curve fitting features have found many uses in mathematics and
science classes, and raised a lot of questions and confusion
among teachers about what is going on there. In this instance, I
think Wiley has stepped backward too far. While the first
edition may have been (too far?) ahead of its time, much has
changed since 1988, and in this area the second edition is behind
the times -- though still ahead of most other textbooks!

The third R, robustness, refers to the the ability of a statistical
measure or technique to resist the effects of errors and outliers
in the data, or violations of the assumptions underlying the
technique. The traditional mean and standard deviation are not
very robust to outliers, and so the more robust median and
interquartile range are preferred in many situations. (Note that
the boxplot is based on them.) Siegel and Morgan introduce these
robust measures first, and present them as the standard tools.
The mean and standard deviation are then introduced as
specialized tools particularly appropriate to normally
distributed data. This makes it clear that the Tukey revolution
really was a revolution -- it not only introduced additional
techniques, but changed the way statisticians regard the older
techniques. Siegel and Morgan understand this, but many other
textbook authors do not.

The new edition extends the coverage of nonparametric techniques.
These are techniques that make fewer assumptions than the
traditional techniques, and generally handle outliers better.
They may be less efficient if your data really are drawn from a
normal distribution, but safer if they are not, or if you cannot
tell, as with small samples.

One serious flaw in the first edition was the very small number
of problems for students. I would estimate that the new edition
has three to five times as many. There are answers to about half
of these, and the answers contain more words than numbers. The
words deal with interpretation of the data, which, after all, is
what statistics is all about. There is also an Instructor's
Solution Manual in the works with more detailed solutions. (I
put it on reserve in the library for student use.)

Another criticism of the first edition was that it contained
hardly any formulas. Calculations were explained in a manner
resembling instructions for filling out your income tax forms.
Personally, I saw this as an asset. I teach a general education
statistics course to first and second year students at a small
former state teachers' college. For most of these students,
formulas would be a barrier to understanding rather than a path
to understanding. However, if you are a high school teacher
trying to show students the use of algebra in statistics, you
will want to see formulas. If you are a high school teacher
doing an AP Statistics course, you will want to keep your
students' algebra skills reasonably fresh for when they take the
SAT and go on to college. Indeed, the sample questions
distributed for AP Statistics require much more algebraic
facility than most of my college students have. For those who
like a little algebra in their statistics, the second edition of
this book is now bilingual.

Indeed, the second edition is trilingual. The steps of carrying
out a procedure are given in words, in formulas, and in commands
for the Minitab statistical software. The computer examples do
not replace a manual for the software; often you see just the
final steps of an analysis, without any explanation of how they
set up the database or how they got to the last step. At least
the examples get you started and provide some experience in
interpreting computer printout in situations where no computer is
available. I think the choice of the Minitab software package is
a good one. There are versions of Minitab for DOS, Windows,
and the Macintosh. The software was originally designed for
educational purposes, and is probably the most widely used
software in college statistics courses, yet it is also used by a
majority of the Fortune Top 50 companies in the US. It was also
one of the first packages to reflect the Tukey revolution. A
disk containing most of the data sets from the book is promised.
The draft disk I examined had some bugs in it but there were
about 100 data sets, some of them definitely too large to ask
students to type in.

There is no mention of calculators in either edition of the book.
That does not bother me, since a computer is a much more
appropriate tool for statistics, but it may bother some high
school teachers for whom graphing calculators are more familiar
and accessible to both themselves and their students. While we
all have to do the best we can with what we have, I hope prior
comfort levels with calculators will not divert teachers from
pressing for more appropriate technology.

One of the limitations of calculators in statistics is their
limited data storage capacity. This book "recycles" many of its
data sets over and over, using them to illustrate a number of
different points. Sometimes a question raised in one chapter is
not fully answered until a later chapter when the same data is
examined again. I think this is a good technique, but I would
hate to have to constantly be retyping or reloading the data into
a calculator.

My biggest disappointment with this text is that it does not do a
very good job of convincing the student that statistics is
important. There are many real data sets, and they are often
extremely well chosen to illustrate the techniques, but the
techniques are not often used to answer any real question of
interest. For example, the areas of important islands in the
Atlantic Ocean are used as an example of transforming data. It
is a wonderful example for that purpose. If you plot the data on
a linear scale you get Greenland at one end of the graph and a
big smudge including all the other islands at the other end. You
can not even get a legible graph without transforming this data.
However, no reason is ever given as to why we might want to study
the areas of these islands. We come away from the example
knowing more about statistics, but we do not know any more about
islands. This is sad, because statistics is primarily a tool to
answer real questions in areas outside of statistics. I should
make it clear that the present book is not outstandingly bad in
this regard. It is actually somewhat above average. However, it
is a failing of most textbooks that has come to bother me more
and more each year. You will need to supplement this one (and
most others) with some more motivating and realistic examples.

One potential supplement would be _Statistics by Example_ by
Sincich (Dellen). This book contains a huge number of problems
based on real studies. Often the background is too sketchy or
too technical, and sometimes we get only summary statistics
rather than raw data, but there are so many problems that it is
still a worthwhile resource. (The book is pretty ordinary
otherwise, with only slight signs of Tukey-awareness.)

My colleague Bill Roberts and I are currently half way through an
introductory statistics course using the Siegel and Morgan text,
and we are quite happy with it. I urge anyone looking for a
textbook to adopt to look at it. Those wanting to learn more
about statistics themselves might want to try to dig up a copy of
the first edition.

Reviewed by Robert Hayden
Plymouth State College
Plymouth, New Hampshire


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