Allan J. Rossman

Workshop Statistics: Discovery with Data.

Exploring Data.- Exploring Data: Comparisons and Relationships.- Collecting Data.- Randomness in Data.- Inference from Data: Principles.- Inference for Comparisons and Relationships.

Conditional Probability and Education Reform: Are They Compatible?

We demonstrate that one can teach conditional probability in a manner consistent with many features of the statistics education reform movement. Presenting a variety of applications of conditional probability to realistic problems, we propose that interactive activities and the use of technology make conditional probability understandable, interactive, and interesting for students at a wide range of levels of mathematical ability. Along with specific examples, we provide guidelines for implementation of the activities in the classroom and instructional cues for promoting curiosity and discussion among students.

Teaching the Reasoning of Statistical Inference: A "Top Ten" List

Allan Rossman (rossman@dickinson.edu) has taught at Dickinson College since receiving his Ph.D. in statistics from Carnegie Mellon in 1989. He has developed interactive curricular materials through which students explore principles of introductory statistics. He directs the MAAsSTATS (Statistical Thinking with Active Teaching Strategies) project, which conducted workshops for mathematicians who teach statistics. With mixed success he applies his statistical training to managing his fantasy baseball team, the Domestic Shorthairs.

Comparing Two Means

You have been studying the application of inference techniques to various situations involving genuine data. In the previous two topics you have investigated problems which call for inferences about a population mean. With this topic you will examine the case of comparing two sample means where the samples have been collected independently (as opposed to the paired comparisons design that you studied in the last topic). The inference procedures will again be based on the t-distribution; the reasoning behind and interpretation of the procedures remain the same as always. Also as always, you will see the importance of visual and numerical examinations of the data prior to applying formal inference procedures.

Students and Teachers’ Knowledge of Sampling and Inference

Ideas of statistical inference are being increasingly included at various levels of complexity in the high school curriculum in many countries and are typically taught by mathematics teachers. Most of these teachers have not received a specific preparation in statistics and therefore, could share some of the common reasoning biases and misconceptions about statistical inference that are widespread among both students and researchers. In this chapter, the basic components of statistical inference, appropriate to school level, are analysed, and research related to these concepts is summarised. Finally, recommendations are made for teaching and research in this area.

Televisions, Physicians, and Life Expectancy

This dataset contains information on life expectancies in various countries of the world and the densities of people per television set and of people per physician in those countries. The example h...

Quantitative evaluation of software process improvement

Abstract This article describes statistical analysis techniques and results used to quantitatively evaluate software process improvement. The analysis techniques include linear regression, rank correlation, and χ2 tests that have been successfully used to quantitatively assess the software process of a large military subcontractor. A logical extension of this work is to examine the results of these statistical techniques after process improvement. We perform these investigations by altering original data to reflect varying types and degrees of process improvements and then repeating the statistical analyses. We find that different types of process improvement generate very different statistical results. The techniques and results presented here can be used to evaluate the effectiveness of process improvements and determine where continued process improvement is needed.

Interview with David Moore

David Moore is Professor Emeritus of Statistics at Purdue University. He served as the first President of the International Association for Statistical Education (IASE) from 1993-1995 and as President of the American Statistical Association (ASA) in 1998. He is a Fellow of the ASA and of the IMS and was awarded the ASA’s Founders Award in 2001. He has written several influential, widely used textbooks for introductory statistics.

Bayes Estimators for the Continuous Uniform Distribution

Classical estimators for the parameter of a uniform distribution on the interval (0, θ) are often discussed in mathematical statistics courses, but students are frequently left wondering how to dis...

Advanced Placement Statistics: Expanding the Scope of Statistics Education

A list of consequential developments in the field of statistics for the past quarter-century must include the creation and implementation of the Advanced Placement (AP) program in Statistics. This program has introduced millions of high school students to our discipline over the past 18 years, contributing to the large increase in the number of undergraduate students pursuing statistics as their major in college. ASA members and leaders have played a substantial role in shaping this program and furthering its success.