Literature Index

Displaying 3091 - 3100 of 3326
  • Author(s):
    McConnell, J. W.
    Editors:
    Shulte, A. P., & Smart, J. R.
    Year:
    1981
    Abstract:
    The author discusses the potential of the magazine "Consumer Reports" as a source of data for Shopping Statistics. This magazine provides alot of data on prices and various aspects of product quality for many different makes and brands for all sorts of products. Such a data source can allow students to learn about statistics using information found in every day life. Real world data can serve to contextualize student learning.
  • Author(s):
    Lesh, R., Amit, M., & Schorr, R. Y.
    Editors:
    Gal, I., & Garfield, J. B.
    Year:
    1997
    Abstract:
    The purpose of this chapter is to examine a "model-eliciting activity", based on a "real-life" problem situation, in which students were provided with an opportunity to construct powerful ideas relating to data analysis and statistics, without explicitly being taught. Student results of this activity will be examined that reveal the somewhat surprising fact that children, even those who traditionally do not perform well in mathematics, can invent more powerful ideas relating to trends, averages, and graphical representations of data than their teacers ever anticipated. The student results shared in this chapter are not unique. In classrooms where we have piloted and refined problems (including the ones presented), one common observation is that many of the children who emerge as "most productive" are often those whose mathematical abilities had not been recognized or rewarded by their teachers in the past.
  • Author(s):
    Witmer, J. A.
    Year:
    1991
    Abstract:
    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.
  • Author(s):
    Albert, J.
    Year:
    2000
    Abstract:
    This article describes the evaluation of the teaching of statistical inference in a first statistics class. A sample survey project is described as a means of assessing the effectiveness of a Bayesian approach in communicating the basis tenets of inference. There are several advantages of the Bayes viewpoint in performing this survey project, including the explicit modeling of one's prior opinion by means of a probability distribution and the relative ease in reporting statistical conclusions. Some evidence is presented to show that students with sufficient knowledge can accurately specify probability distributions. The success of the survey project is evaluated, and changes to the structure of the project are described that facilitate the interaction of the instructor with the students.
  • Author(s):
    Pace, L. A., & Barchard, K. A.
    Abstract:
    This paper repsents an innovative method using spreadsheets to reduce anxiety and build understanding in introductory statistics courses. Empirical data show that instruction using spreadsheets is currently concentrated in business statistics classes. The authors advocate the use of spreadsheets in statistics education in other fields and present supporting cases.
  • Author(s):
    Sharleen Forbes
    Year:
    2014
    Abstract:
    Many adults who need an understanding of statistical concepts have limited mathematical skills. They need a teaching approach that includes as little mathematical context as possible. Iterative participatory qualitative research (action research) was used to develop a statistical literacy course for adult learners informed by teaching in traditional first year university courses, workplace based training, teacher workshops and Masters of Public Policy courses. The latter learners in particular regularly come across confidence intervals and statistical significance in their everyday reading. The goal is to give them a conceptual rather than theoretical understanding of inferential concepts by developing inferential statistics logic through the introduction of exact probabilities in simple non-parametric tests (two-tailed coin tossing) and then contingency tables and parametric situations. The final course developed for the New Zealand Certificate of Official Statistics uses “hands-on” examples to reinforce concepts before proceeding to computer simulations. It emphasizes evaluation of the strength of statistical significance and its relationship to the possible cost of making an incorrect decision. Case studies that have influenced government policy reinforce inferential concepts and demonstrate the importance of statistics in complex real problems.
  • Author(s):
    Wasik, J. L.
    Year:
    1992
    Abstract:
    This paper will describe applications of reforms proposed for college mathematics for teaching introductory statistics to prospective high school mathematics teachers.
    Location:
  • Author(s):
    Sarah Bansilal
    Year:
    2014
    Abstract:
    This study is an exploration of teachers’ engagement with concepts embedded in the normal distribution. The participants were a group of 290 in-service teachers enrolled in a teacher development program. The research instrument was an assessment task that can be described as an “unknown percentage” problem, which required the application of properties of the standard normal distribution curve. Responses to the task were analyzed using the Action, Process, Object, Schema (APOS) framework that specified a standardization and a probability layer of understanding. The success rates were 27% and 14% in the two questions, with most teachers experiencing problems in the probability layer because of a failure to link the probability values with the area covered by the curve.
  • Author(s):
    Wolfe, C. R.
    Year:
    1992
    Abstract:
    Describes the use of a microcomputer-based authoring system to facilitate student-centered discovery oriented learning in an undergraduate social science course. Use of the authoring system to implement student-generated experiments, to become familiar with statistics and experimental methods, and to promote empirical thinking is discussed. (14 references) (LRW)
  • Author(s):
    Caffarella, E. P.
    Year:
    1981
    Abstract:
    This paper discusses the use of an interactive computer system as a major component of instruction for the graduate level introductory educational statistics course at the University of Maine at Orono. Four major computer topics are covered in the statistics course: (1) terminal and computer operation, (2) Montana State University Interactive Statistical Analysis Program (MSUSTAT), (3) the CMS Editor, and (4) the Statistical Package for the Social Sciences (SPSS). These topics are introduced sequentially during the first six weeks of the semester. The major objective is for the students to be able to use SPSS; the other three topics provide the prerequisite skills. Four references are listed and the appendices include a course syllabus for the summer, 1981; instructions on how to use the Interactive Statistics Program; instructions for using the terminal; three study guides; and instructions for card order and deck setup for generating and processing SPSS files. (CHC)

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education