An International Journal on the Teaching and Learning of Statistics
JSE Volume 20, Number 3 Abstracts
Statistics and probability have become an integral part of mathematics
education. Therefore it is important to understand whether curricular
materials adequately represent statistical ideas. The Guidelines for
Assessment and Instruction in Statistics Education (GAISE) report
(Franklin, Kader, Mewborn, Moreno, Peck, Perry, & Scheaffer, 2007), endorsed
by the American Statistical Association, provides a two-dimensional (process
and level) framework for statistical learning. This paper examines whether
the statistics content contained in the NSF funded elementary curricula
Investigations in Number, Data, and Space, Math Trailblazers,
and Everyday Mathematics aligns with the GAISE recommendations.
Results indicate that there are differences in the approaches used as
well as the GAISE components emphasized among the curricula. In light
of the fact that the new Common Core State Standards have placed little
emphasis in statistics in the elementary grades, it is important to ensure
that the minimal amount of statistics that is presented aligns well with
the recommendations put forth by the statistics community. The results
in this paper provide insight as to the type of statistical preparation
students receive when using the NSF funded elementary curricula. As the
Common Core places great emphasis on statistics in the middle grades,
these results can be used to inform whether students will be prepared
for the middle school Common Core goals.
Key Words: Reform curricula; Elementary mathematics curricula; Everyday
Mathematics; Trailblazers; Investigations; Curriculum studies; Alignment; GAISE alignment.
This manuscript discusses the common confusion between the terms probability and odds.
To emphasize the importance and responsibility of being meticulous in the dissemination of
information and knowledge, this manuscript reveals five cases of sources of inaccurate
statistical language imbedded in the dissemination of information to the general public.
The five cases presented are: Texas Lottery, Texas PowerBall, the Discovery Education Website,
ScienceNews, and the Oregon State website.
Key Words: Statistical Literacy; Statistical Competence; Odds; Probability.
Lawrence M. Leemis, Daniel J. Luckett, Austin G. Powell, and Peter E. Vermeer
Univariate Probability Distributions
We describe a web-based interactive graphic that can be used as a resource
in introductory classes in mathematical statistics. This interactive graphic
presents 76 common univariate distributions and gives details on (a) various
features of the distribution such as the functional form of the probability
density function and cumulative distribution function, graphs of the probability
density function for various parameter settings, and values of population
moments; (b) properties that the distribution possesses, for example,
linear combinations of independent random variables from a particular
distribution family also belong to the same distribution family; and (c)
relationships between the various distributions, including special cases,
transformations, limiting distributions, and Bayesian relationships. The
interactive graphic went on-line on 11/30/12 at the URL
Key Words: Continuous distributions; Discrete distributions; Distribution properties; Limiting distributions; Special Cases; Transformations;
Service-learning can mean different things and look quite different in varying statistics
curricula that may include undergraduates, graduates, majors and non-majors across a wide array
of higher institutions. The terms community engagement, volunteerism, community-based projects
and service-learning are tossed around on various institutions' websites. The purpose of this
article is two-fold. First is to provide an historical review of the evolution of
service-learning activities to try to unify and define the terminology as one might use this
pedagogy for statistics instruction. Second is to present some examples of how a first and
second course in business statistics can step up from service-learning and move up the continuum towards reaping the reciprocal benefits of SERVICE-LEARNING (SL). In this article, service learning (note the omission of a hyphen) is a valued classroom service activity that separates the activity from the learning goals of the class, while service-learning (note the presence of a hyphen) is a teaching methodology in which the service and learning goals are carefully given equal weight in the development of the project so that classroom goals and service outcomes enhance each other providing a reciprocal experience for all participants (Sigmon 1994). When this careful design is a "method of teaching
through which students apply newly acquired academic skills and knowledge to address real-life
needs in their own communities" (ASLER 1994), SL unifies what students are currently learning
in the classroom with the service they are simultaneously providing in the community. Careful
design opens the door to provide opportunities of SL in an introductory, non-majors statistics
Key Words: Service-Learning; Community engagement; Authentic assessment.
In 2005, the Guidelines for Assessment and Instruction in Statistics
Education (GAISE) college report described several recommendations
for teaching introductory statistics. This paper discusses how a large
multi-section introductory course was redesigned in order to implement
these recommendations. The experience described discusses the key sections
of the GAISE report and sheds light on the challenges that must be overcome
in putting them in place. The result is a course which addresses both
the "how to" and big picture of statistics.
Key Words: Training graduate instructors; Capstone experience; Authentic assessment.
Interviews with Statistics Educators
Michelle Everson is Senior Lecturer in the Department of Educational Psychology at the
University of Minnesota. She served as Program Chair for the Statistical Education Section of
the American Statistical Association in 2012 and also for the inaugural e-COTS (Electronic
Conference on Teaching Statistics). The 2011 recipient of the ASA's Waller Education Award,
she is the incoming editor of JSE. The following interview took place via email on
August 20 - 31, 2012.
We located 46 articles that have been published from January 2012 through November 2012 that
pertained to statistics education. In this column, we highlight a few of these articles that
represent a variety of different journals that include statistics education in their focus. We
also provide information about the journal and a link to their website so that abstracts of
additional articles may be accessed and viewed.
In each issue of JSE, we like to highlight new activities and resources
from CAUSEweb (www.causeweb.org)
Data Sets and Stories
Undergraduate research experiences can be a powerful tool that statistics educators can use
to give students an in-depth look at real data analysis as it occurs in multiple professional
and academic settings. This article has two goals. The first is to introduce two large and
fascinating datasets that are freely available, interesting in content to students, and widely
used in current studies. The second is to outline an undergraduate research project that
utilized these data. This project was undertaken by four undergraduates over the course of a
semester. The phases of the project are discussed as well as example results from the students.
There are many possible modifications to the project that can be made at various levels of
complexity. Appendices provide relevant R code and descriptions of the merged data available for
Key Words: Class Project; Chi-Square; ANOVA; Bootstrap Analysis.
The Height and Shoe Size dataset contains information on height (in inches), dress shoe size, and
gender for 408 college students. The information was collected to provide an interesting initial example
for the study of correlation and regression in a business statistics class. Students don't mind providing
this information (unlike weight, about which they are often more sensitive) and seem to enjoy seeing how
accurate the resulting prediction is for their particular height or shoe size. Once each semester's values
are added to the file, it is posted and used for a series of assignments. We begin with correlation, move
on to a series of simple linear regression assignments, and finish by incorporating a dummy variable for gender.
This data set could also be used for frequency distributions, histograms, or inference.
Key Words: Indicator Variables; Histograms; Inference.