Teaching

  • Graphical, computational, interactive, and simulation capabilities of computers can be successfully employed in the teaching of elementary probability, either as classroom demonstrations or as exploratory exercises in a computer laboratory. In this first paper of a contemplated series, two programs for EGA-equipped IBM-PC compatible machines are included with indications of their pedagogical uses. Concepts illustrated include the law of the large numbers, the frequentist definition of a probability, the Poisson distribution and process, and intuitive approaches to independence and randomness. (Commands for rough equivalents to the programs using Minitab are shown in the Appendix.)

  • Students of all ages seem fascinated by the lottery, making it a ready tool for illustrating basic probabilistic concepts. The author has developed a program called "Lotto Luck" for IBM PC compatibles which has been used on over 100 classrooms from grades 6 through 12 and with dozens of college classes and civic groups to demonstrate what happens to the "earnings" of the frequent lottery player over a period of time. We discuss how to use the program and provide information for obtaining the complied code by ftp.

  • We intended this Handbook to help instructors who teach statistics and research methods, either as separate courses or in others, such as introductory psychology and advanced content courses. We organized the 90 articles into two main sections, Statistics and Research Methods, and each major section contains subgroups of papers on common themes. Collectively, the articles include a stunning amount of information. Among other topics, articles in the first section cover (a) how to reduce students' anxiety about statistics, (b) general and specific strategies for teaching statistics, (c) how to illustrate some statistical concepts and techniques, and (d) several ways to generate data sets for student use. Among other topics, articles in the second section cover (a) ethical issues, (b) proposals for designing and conducting a research methods course, (c) techniques for enlivening and improving students' literature reviews, (d) general and specific strategies for teaching a variety of methodological concepts and procedures, (e) use of computers, (f) suggestions for successfully involving students in substantive research and encouraging formal presentations of their results, and (g) recommendations for making theses and dissertations more productive and pleasant. Equally important, many of the articles in both sections are rich sources of ideas for further research.

  • This booklet is part of the Curriculum and Evaluation for School Mathematics Addenda Series, Grades K-6. This series was designed to illustrate the standards and to help you translate them into classroom practice. In Making Sense of Data you will find that familiar activities have been redesigned and infused with an investigative flavor. You will discover new ideas that can be easily incorporated into your mathematics program. You will also encounter a variety of problems and questions to explore with your class. Margin notes give you an additional information on the activities and on such topics as student self-confidence, evaluation, and grouping. Connections to science, language arts, social studies, and other areas in the curriculum are made throughout. Supporting statements from the Curriculum and Evaluation Standards appear as margin notes.

  • Are SAT scores useful predictors of success in college? I led a group of mathematics majors at Oberlin College in an exploration of this question as the core of a one-credit course, entitled Data Analysis, last year. This course, MATH 337, is an adjunct to the standard, junior-level, two semester sequence in probability and mathematical statistics that we offer each year at Oberlin. Unlike most statistics courses for mathematics majors, the Data Analysis course allows - indeed, it forces - students to "get their hands dirty" exploring real data and trying to answer real questions. Each year I select a set of data, such as the SAT data, to serve as the central focus of the course. I believe that it is imperative that student learn something of how statistical theory is applied in practice and I try to show this side of statistics in the courses I teach at all levels. However, it is particularly difficult to cover much material on applied statistics while at the same time covering the many important topics in the mathematical statistics course - probability, random variables, functions of random variables, expectation, the central limit theorem, estimators, confidence intervals, hypothesis testing, and others - that are fundamental to the discipline and are an essential foundation for advanced (graduate) training in statistics. The Data Analysis course provides a workable solution to this problem.

  • A course in time series analysis offers a number of unique opportunities for introducing mathematically oriented students to the applications of statistics. Characteristics of such a course and its suitability as a "first course" in statistics are discussed.

  • Brief descriptions of several model courses which have been developed by participants in a series of Statistics in the Liberal Arts Workshops (SLAW).

  • I have selected the stated problem which may be found in Takacs (1960,p.21) with a brief reference to statistical mechanics, for several reasons: its source is an important test used by many engineering students; the problem lends itself well to illustrate the ideas on imagination presented in this paper and, with imagination unfettered, it can serve to generate quite exciting purely mathematical questions.

  • In South Australia mathematics students typically have had considerable exposure to calculus but little to probability on entering university. The amount of probability in school syllabuses has decreased, and our first year university students often associate the subject with fatuous but intricate examples. Some undertake a first year statistics subject, but many will come to a first course in applied probability/stochastic processes in second year applied mathematics without that background. They are ill at lease with any second year applied mathematics courses not based on calculus (forgetting the effort with which the comfort with calculus was won!) and will often demand evidence of meaningful applicability at an early stage. This creates some challenge and necessitates an examination of one's teaching philosophy.

  • This paper discusses the modeling process.

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