This paper presents a three level scheme for categorizing the use of computers/microcomputers in the teaching of statistics.
This paper presents a three level scheme for categorizing the use of computers/microcomputers in the teaching of statistics.
This article relates some of our experiences at Dalhousie University in the use of projects for elementary statistics courses and argues for both their feasibility and worth.
I feel it is essential for a high school graduate to be able to understand and interpret basic ideas of statistics.
Statistics has never been one of the major strands in the mathematics curriculum in the United Stated. Recently, however, educators have recognized the need for a society that is quantitatively literate; almost every national report or set of recommendations contains a call for statistics in the curriculum.
This report discusses various techniques to teaching statistics: Writing, Concepts, Essay Exams, Precis and Note cards.
I teach a standard, junior-level, two-semester sequence in probability and mathematical statistics, MATH 335-336, at Oberlin College. In this sequence students learn the mathematical theory that underlies statistical practice as we cover the random variables, functions of random variables, expectation, the central limit theorem, estimators, confidence intervals, hypothesis testing, and regression, among others. Most of the students who take the sequence have no previous experience with statistical applications or with data. Unfortunately, in MATH 335-336 students see little of the applied side of the discipline - there only so much that we can do in two semesters! Although they learn about sampling distributions and large-sample properties of estimators, they learn little about the concerns practicing statisticians have about how samples are actually drawn: experimental design, randomization, bias, etc. I address this problem by offering an additional, one-credit, course - MATH 337 - DATA ANALYSIS - as an adjunct to MATH 336.
Computers have been used in the introductory statistics course at Brigham Young University for several years. The number and type of computers, as well as the manner in which they have been used, has evolved considerably during this time. This paper attempts to explain some of the lessons from this experience and some of our current views on the subject.
In this article I will describe: - how my minute paper questions have evolved, - how my minute paper process has evolved, and - things I have learned from and about minute papers.
The subject here is resampling as a substitute and complement to conventional methods, the method of first choice in handling actual everyday problems, and not an improvement in the standard pedagogy.
Together with Julian Simon in the same class, I used Against All Odds in teaching introductory business statistics at the University of Maryland's College of Business and Management. A description of how we used it is preceded by a discussion of the role of probability and inference in a statistics course, and the use of resampling simulation in teaching statistics.