This article describes a dataset on pop songs that charted on the Billboard Top 40 and/or at one or more of five radio stations, three in Chicago, Illinois, and two in Grand Rapids, Michigan, from the early 1960s through 1970. The dataset includes 5746 observations and 26 variables. In the body of the paper article, we describe how the cleaned version of the dataset can be used in an introductory or second-level statistics course to investigate questions of race and gender bias and the role of radio consultants in Top 40 radio airplay in the 1960s. The richness of the dataset requires students to think about relationships among multiple variables. In an appendix, we briefly describe how a raw, uncleaned version of the dataset can be used in an R programming course to illustrate data management and data entry error detection.

Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This research identifies and discusses four misconceptions prevalent in student understanding of histograms. In addition, it presents pre- and post-test results on an instrument designed to measure the extent to which the misconceptions persist after instruction. The results presented indicate not only that the misconceptions are commonly held by students prior to instruction, but also that they persist after instruction. Future directions for teaching and research are considered.

rights reserved. This text may be freely shared among individuals, but it may not be republished in any medium without express written consent from the authors and advance notification of the editor. To commemorate the 20-year anniversary of the Journal of Statistics Education (JSE), former editors of JSE were asked to share some of their thoughts, reflections, and memories. I remember the early days of JSE as both an exciting and a scary time; it really felt like we were entering uncharted territory. I'm sure that starting any new journal would be somewhat intimidating, but our decision to use the electronic medium at a time when there were very few examples of successful electronic journals made the enterprise seem that much more daring. We started JSE to provide a publication outlet for scholarly work in statistics education, but we also introduced other innovations of which I am very proud. In addition to being the first electronic journal in statistics, I believe that we were the first statistics journal to use double-blind refereeing. We also implemented the practice of sharing referees' reports (anonymously) among all referees in an effort to improve the quality of those reports. From the start, we encouraged participation in the journal by a wide and international audience by making widespread appeals for referees, authors, and readers, and by introducing technical innovations carefully and slowly so that the journal would remain accessible to a broad audience. It is amazing and gratifying to see that, 20 years later, JSE is still thriving, growing, and innovating.

When I started to handle new papers in September 2009, I had many questions about the process. Past JSE Editors were very kind in providing insight, especially Bob Stephenson and Bill Notz. Bill’s help with getting out my first issue as Editor in March 2010 was much appreciated. The job of Editor is made much easier by having a group of dedicated Associate Editors who bring their expertise to finding referees and reviewing submissions. I appreciate all the time and effort that you have taken on behalf of JSE. In-coming Editor Michelle Everson has authorized me to double your annual stipend . The American Statistical Association supports JSE both monetarily and with the expertise of staff. Thank you Ryan Bell and Eric Sampson. Eric, I’m constantly amazed at how fast you turn around my many requests. I was very fortunate when I took over as Editor to have a continuing, excellent Editor of the Data Sets and Stories department of JSE. Thank you Dex Whittinghill for handling all the data sets and associated papers. JSE is better for having had your leadership contributions for the past six years. The most vital person to the success of my tenure (and by success I mean maintained sanity) was the JSE Editorial Coordinator Jean Scott. Jean handles the processing of all submissions and is in regular contact with authors leading up to publication. She does a million and one tasks that need doing to keep JSE an online, free resource for the statistics education community. I lack the words to adequately express my thanks. I will miss our pre-publication phone calls. Lastly, and most importantly, I want to thank all the authors that contributed to and readers of JSE. I learned so much from my interactions with all of you. I appreciate your comments and questions.

Summary This article describes an interactive activity that revolves around the dice‐based golf game GOLO. The activity illustrates descriptive statistics and graphical analyses for univariate data.

The introductory applied statistics course taken by many thousands of undergraduate students has undergone a transformation over the past 25 years. Changes in what we teach, how we teach, and how we assess have impacted introductory statistics courses at institutions worldwide. In this article we shift focus from what we teach and how we teach to when we teach. We propose changes to the sequence in which core statistical concepts are presented in an introductory applied statistics course. The proposed ordering of topics repeats the sequence of descriptive summaries-probability theory-statistical inference several times throughout the course in various contexts.

This paper describes an interactive activity that revolves around the dice-based golf game GOLO. The GOLO game can be purchased at various retail locations or online at igolo.com. In addition, the game may be played online free of charge at igolo.com. The activity is completed in four parts. The four parts can be used in a sequence or they can be used individually. Part 1 illustrates the binomial distribution. Part 2 illustrates the sampling distribution of the sample proportion. Part 3 illustrates confidence intervals for a population proportion. Part 4 illustrates hypothesis tests for a population proportion. Extensions of the activity can be used to illustrate discrete probability distributions (including the geometric, hypergeometric, and negative binomial) and the distribution of the first order statistic. The activity can be used in an AP statistics course or an introductory undergraduate statistics course. The extensions of the activity can be used in an intermediate undergraduate statistics course or a mathematical statistics course. Each extension is self-contained and can be carried out without having worked through other extensions or any of the four parts of the main activity.

In 2007, the American Statistical Association published the Guidelines for Assessment and Instruction in Statistics Education report (GAISE). This project was motivated by statistics educators seeking to clarify the nature and role of statistics within the school mathematics curriculum. Specifically, the report describes statistics as a problem-solving process, suggests three developmental levels for learning statistics, and discusses some of the unique aspects of statistical thinking.

This article describes an activity that revolves around a data set that students help create. The students use data about occurrences of letters in English text to study the relationship between the relative frequency of letters and the percent of Scrabble game tiles for the letter and the relationship between the relative frequency of a letter and the letter's Scrabble game-tile point value.

This article describes an activity that revolves around a data set that students help create. The students use data about occurrences of letters in English text to study the relationship between the relative frequency of letters and the percent of Scrabble game tiles for the letter and the relationship between the relative frequency of a letter and the letter's Scrabble game-tile point value.

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an intermediate applied statistics course.

A datum is considered spatial if it contains location information. Typically, there is also attribute information, whose distribution depends on its location. Thus, error in location information can lead to error in attribute information, which is reflected ultimately in the inference drawn from the data. We propose a statistical model for incorporating location error into spatial data analysis. We investigate the effect of location error on the spatial lag, the covariance function, and optimal spatial linear prediction (that is, kriging). We show that the form of kriging after adjusting for location error is the same as that of kriging without adjusting for location error. However, location error changes entries in the matrix of explanatory variables, the matrix of co-variances between the sample sites, and the vector of covariances between the sample sites and the prediction location. We investigate, through simulation, the effect that varying trend, measurement error, location error, range of spatial dependence, sample size, and prediction location have on kriging after and without adjusting for location error. When the location error is large, kriging after adjusting for location error performs markedly better than kriging without adjusting for location error, in terms of both the prediction bias and the mean squared prediction error.

Polar orbiting satellites remotely sense the earth and its atmosphere, producing datasets that give daily global coverage. For any given day, the data are many and measured at spatially irregular locations. Our goal in this article is to predict values that are spatially regular at different resolutions; such values are often used as input to general circulation models (GCMs) and the like. Not only do we wish to predict optimally, but because data acquisition is relentless, our algorithm must also process the data very rapidly. This article applies a multiresolution autoregressive tree-structured model, and presents a new statistical prediction methodology that is resolution consistent (i.e., preserves "mass balance" across resolutions) and computes spatial predictions and prediction (co)variances extremely fast. Data from the Total Ozone Mapping Spectrometer (TOMS) instrument, on the Nimbus-7 satellite, are used for illustration.

this article is to predict values that are spatially regular atdifferent resolutions; such values are often used as input to general circulation models(GCMs) and the like. Not only do we wish to predict optimally, but because dataacquisition is relentless, our algorithm must also process the data very rapidly. Thisarticle presents a new statistical prediction methodology that preserves "mass balance"across resolutions and computes spatial predictions and prediction (co)variances extremely...

The American Statistical Association began a national Statistics Poster Competition in the United States in 1990. The competition provides an ideal way to introduce students to the world of statistics. Statistical posters require that students select and define a topic of interest, design a corresponding study, collect data, appropriately present the data, and effectively communicate their findings to a non-statistical audience via graphical and, perhaps, inferential methods. The national competition spawned numerous regional and state competitions throughout the U.S. We provide a brief history of the national statistics poster competition. We describe the experiences of a group of statisticians at Grand Valley State University who began a statewide statistics poster competition in 2000 in the state of Michigan. We provide lessons learned for those hoping to start a competition in their own regions. A BRIEF HISTORY OF STATISTICS POSTER COMPETITIONS IN THE UNITED STATES The national statistics poster competition in the United States dates back to the late 1980s when Lorraine Denby, then American Statistical Association (ASA) officer of the Graphics Section, learned about a national statistics poster competition held in Japan. Lorraine suggested that ASA sponsor such a national contest in the United States. At the Joint Statistical Meetings (JSM) in 1989 Jerry Moreno of John Carroll University in Cleveland, Ohio was asked to organize a national competition. With the help of Kathryn Rowe, then Director of the ASA Center for Statistics Education, Jerry formed a committee of five teachers and statisticians from across the country who organized the first national poster competition held in spring 1990. The competition was a joint effort between the ASA Center for Statistics Education (CSE) and the ASA Section on Statistical Graphics. In the mid-1990s the ASA and the National Council of Teachers of Mathematics (NCTM) assumed responsibility for the poster competition through the ASA/NCTM Joint Committee (background from Young, 1998, and Rogness et al., 2003). The national competition spawned numerous regional and statewide competitions through word-of-mouth advertising. Statisticians at Grand Valley State University (GVSU) have organized a competition open to all kindergarten through grade 12 students in the state of Michigan since 2000. In this paper we describe the steps taken to launch the competition, advertise its existence, and successfully build upon a network of participating teachers. Before we go into the details of the Michigan model, we describe the purpose and educational benefits of getting students involved in a statistics poster competition. EDUCATIONAL GOALS OF A STATISTICS POSTER COMPETITION