Anna E. Bargagliotti
How well do the NSF Funded Elementary Mathematics Curricula align with the GAISE report recommendations?

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.

Lawrence V. Fulton, Francis A. Mendez, Nathaniel D. Bastian, and R. Muzaffer Musal
Confusion Between Odds and Probability, a Pandemic?

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 www.math.wm.edu/leemis/chart/UDR/UDR.html.

Key Words: Continuous distributions; Discrete distributions; Distribution properties; Limiting distributions; Special Cases; Transformations; Univariate distributions.

Amy L. Phelps
Stepping from service-learning to SERVICE-LEARNING pedagogy

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 class.

Key Words: Service-Learning; Community engagement; Authentic assessment.

Roger Woodard and Herle McGowan
Redesigning a Large Introductory Course to Incorporate the GAISE Guidelines

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.

Allan Rossman and Michelle Everson
Interview with Michelle Everson

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.

Audbjorg Bjornsdottir and Joan Garfield
Teaching Bits: Statistics Education Articles from 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.

Michelle Everson and Ellen Gundlach
Teaching Bits: What's New with CAUSEweb and MERLOT?

In each issue of JSE, we like to highlight new activities and resources from CAUSEweb (www.causeweb.org) and MERLOT(www.merlot.org).

Darcie A.P. Delzell
African Conflict and Climate Data for an Undergraduate Research Project

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 download.

Key Words: Class Project; Chi-Square; ANOVA; Bootstrap Analysis.

Constance H. McLaren
Using the Height and Shoe Size Data to Introduce Correlation and Regression

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.