Literature Index

Displaying 961 - 970 of 3326
  • Author(s):
    Caroline Brophy and Lukas Hahn
    Year:
    2013
    Abstract:
    In this paper, we describe an in-class experiment that is easy to implement with large groups of students. The experiment takes approximately 15-20 minutes to run and involves each student completing one of four types of Sudoku puzzles and recording the time it takes to completion. The resulting data set can be used as a teaching tool at an introductory level right through to an advanced level of statistics. Basic methods for describing and displaying data as well as the intricacies that arise with real data may be discussed in an introductory course. The range of more sophisticated analyses that can be taught with the data set include chi-squared tests for independence, ANOVA, t- and F-tests, logistic regression and survival analysis. We describe and provide the tools to implement the experiment and illustrate several potential teaching topics using a collected data set.
  • Author(s):
    Groth, R. E.
    Editors:
    Burrill, G. F.
    Year:
    2006
    Abstract:
    This article will address some issues involved in shifting away from teaching statistics as a collection of techniques and tools toward what can be called a more authentic approach that involves genuine problem solving and reasoning with data.
  • Author(s):
    Konold, C., Coulter, B., & Feldman, A.
    Year:
    2000
    Abstract:
    In the third part of a series, advice for teachers on moving students engaged in Internet science projects beyond collecting and uploading data to analyzing data from many sites is presented. The advice deals with what is involved in a data-centered investigation and with leading such investigations by using reliable data, beginning with familiar contexts, using data with salient trends, and working with representations that students understand.
  • Author(s):
    Lesser, L. M
    Year:
    2006
    Abstract:
    An overview of how to motivate and bring intuition to concepts that are initially nonintuitive or even counterintuitive to students. Examples are provided that use a variety of means, including using multiple representations, intuitive analogies, and using (and resolving) counterintuitive examples. A thorough bibliography of additional resources and references is included.
  • Author(s):
    Susan A. Peters
    Year:
    2010
    Abstract:
    Statistics uses scientific tools but also requires the art of flexible and creative reasoning.
  • Author(s):
    Lawrence M. Lesser and Matthew S. Winsor
    Year:
    2009
    Abstract:
    Despite the rapidly growing population of English language learners in U.S. colleges and schools, very little research has focused on understanding the challenges of English language learners specifically in statistics education. At a university near the United States-México border, the authors conducted an exploratory qualitative case study of issues of language in learning statistics for pre-service teachers whose first (and stronger) language is Spanish. The two strongest findings that emerged from cross-case analysis of the interviews were the importance of the role of context (the setting in which information is communicated) and the confusion among registers (subsets of language). This paper overviews and synthesizes relevant literature and offers resources and recommendations for teaching and future research.
  • Author(s):
    Robert M. Pruzek and James E. Helmreich
    Year:
    2009
    Abstract:
    A standard topic in many Introductory Statistics courses is the analysis of dependent samples. A simple graphical approach that is particularly relevant to dependent sample comparisons is presented, illustrated and discussed in the context of analyzing five real data sets. Each data set to be presented has been published in a textbook, usually introductory. Illustrations show that comprehensive graphical analyses often yield more nuanced, and sometimes quite different interpretations of data than are derived from standard numerical summaries. Indeed, several of our findings would not readily have been revealed without the aid of graphic or visual assessment. Several arguments made by John Tukey about data analysis are seen to have special force and relevance.
  • Author(s):
    Canada, D.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    While recent and ongoing research has begun to reveal ways that precollege students think about variation, little research has been done with the preservice teachers who will eventually serve such students. Specifically, more research is needed to understand what are the conceptions of variation held by elementary preservice teachers (EPSTs), and also how to shape the university courses where those preservice teachers learn. This paper, sharing an excerpt from an exploratory study aimed at EPSTs, describes changes in class responses to a probability task where variation is a key component. Overall, going from before to after a series of instructional interventions, responses reflected a more appropriate sensitivity to the presence of variation.
  • Author(s):
    Wallman, K. K.
    Year:
    1993
    Abstract:
    Statistical questions suffuse the favric of our society at almost all folds. When Bill Krusak and I offered that ovservation in an Amstat News artical just over a decade ago, the universe of our immediate concern was the federal statistical system--a universe that to some may have seemed rather parochial. Our principal intent in sharing out views with the members of ASA was to underscore the pervasiveness of statistics produced by the federal govenment in out professional and personal lives. The urgency in our voices stemmed from what we perceived to be "penny-wise but poiund0foolish decision" that would undermine the quality of data for research, program planning, allocation of resources, and policy evaluation--by academics, buiness leaders, government officials, and citizens--for years to come (Druskal and Wallman 1982).<br>It is not my mission tonight to revisit either historic or recent tragedies and triumphs of the federal statisital system. Many have written and spoken on these matters; several of my predecessors have discussed these issues, and how the ASA might respond to them, intheir presidential addresses to out membershp. I will, hoever, use the milieu of federal statistic as the opening scene for elaborating my hope that by enhancing statistical literacy we may succeed in enriching our society. My aims for the remarks I will share with you this evening are three:<br>--to underscore the importance of strengthening understanding of statistics and statistical thinking among all sectors of our population;<br>--to highlight some avenue we can pursue to enhance our citizens' statistical literacy; and<br>--to suggest some ways that individual statisticians and the American Statistical Association can enrich our society.
  • Author(s):
    Grabowski, B. L., &amp; Harkness, W. L.
    Year:
    1996
    Abstract:
    This article reports on the results of two studies that investigated the effectiveness of different uses of expert systems in large introductory statistics classes. Three groups of students were compared -- those who used an expert system created by the instructor of the course, those who created their own expert system, and those who did not use any at all. The first experiment showed non-significant, but interesting, trends that were explored in the second experiment. In the second experiment, significant differences emerged as the semester evolved in favor of those who used the expert system, regardless of whether or not the students created it themselves. These differences disappeared on the final exam, when technological problems added to the end-of-the-semester tension. These findings support the notion that the use of expert systems in the classroom can have an important impact on the level and amount of learning that occurs. This article describes these two studies in detail and draws some implications for teaching.

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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

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