Literature Index

Displaying 751 - 760 of 3326
  • Author(s):
    Thomas, C. S.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This study investigated whether elementary education majors in the teacher education program at Montana State University (MSU) acquire and retain knowledge of statistical data analysis concepts and skills consistent with expectations specified in the NCTM "Principles and Standards for School Mathematics" (2000). The following statistical topics were covered: Finding, describing and interpreting mean, median and mode; interpreting the spread of a set of data; understanding the meaning of the shape and features of a graph; comparing centers, spreads, and graphical representations of related data sets; and using scatter plots and lines of best fit.
  • Author(s):
    Habibullah, S. N.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Ever since 1985, the Department of Statistics at Kinnaird College for Women, Lahore, Pakistan has been engaged in a large number of projects and programmes aimed at the improvement of statistical education in the country. These include (a) statistical competitions for students, (b) statistical exhibitions, and (c) workshops for lecturers and assistant professors working in various intermediate and degree colleges of Pakistan (Habibullah, 1992, 1994, 1995, 2001). The year 1997 witnessed the very first efforts of the Kinnaird College Statistics Department to expand the statistical competition to the worldwide community of students/young adults (Habibullah, 1999). The competition entitled "Data Analysis Talent Award (DATA)" invites students to indulge in projects involving collection and analysis of real-life data, and to write comprehensive reports regarding their projects.
  • Author(s):
    Witmer, J.
    Year:
    1991
    Abstract:
    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.
  • Author(s):
    Green, D. R.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    This paper offers an overview of research on teaching and learning statistics, what research is needed and for what purpose. The author suggests that future research must concentrate on establishing the best ways to teach statistical concepts so that meaningful long-term learning takes place.
  • Author(s):
    Zawojewski, J. S., & Shaughnessy, J. M.
    Editors:
    Silver, E. A., Kenney, P. A.
    Year:
    1999
    Abstract:
    This chapter reports on student performance with data and chance by examining individual 1996 NAEP items or clusters of related items and where available samples of student responses to constructed-response questions. The focus of this chapter is on four categories that are often interrelated: central tendency, reasoning with data, graphical data displays, and probability and chance. In addition to reporting and interpreting performance based on NAEP results, for some short and extended constructed-response items we also examined a set of sample student responses. This sample was not a representative sample of all responses; rather it was a convenience sample of non-blank responses. Some of the items described in this chapter were extended constructed-response tasks, a type of NAEP item that is discussed extensively in chapter 11 by Silver, Alacaci, and Stylianou.
  • Author(s):
    Behrens, J. T., Smith, M. L.
    Editors:
    Jonassen, D. H.
    Year:
    1996
    Abstract:
    Whereas data analysis was once considered synonymous with statistics, a broader view is emerging in educational psychology. In their presient work, Tukey and Wilk (1966/1986) articulated such a broad view, one we adopt for this chapter: "[T]he science and art of data analysis concerns the process of learning from ...records of experience" (p. 554). Although Tukey and Wilk wrote about their own quantitative analysis, the definition seems equally appropriate for qualitative work. Good data analysis, regardless of the approach, is a mixture of science and art. Data analysis employs creativity in search of meaning, intelligibility, and pattern while rooted in systematic methods that emphasize open-mindedness and public scrutiny. Regardless of the theoretical emphasis, data analysis seeks revelation --the unveiling of the world around us.
  • Author(s):
    Grant, C. M.
    Year:
    1998
    Abstract:
    School are flocking to software publishers to equip their newly acquied compuers with the latest in software. Electronic graphing tools are an important component of any computer tool kit. Graphers..Data Explorere...Math Lab Toolkit...Green Globs and Graphing Equations...Symbols and Graphs...First Workshop...The Graph Club.. The 199701998 SUMBURST educational software catalogue alone lists these 7 electronic graphing tools. In many schools today, it is not unusual to see students projects with computer-generated graphs lining school hallways. Parents are delighted that their children are sorking with data and using computers to peoduce neat , professional -looking products that incorporate graphs along with the textk, graphics, and tables they have come to look for.<br><br>Yet the fact that students are graphing with computers doesn't, in and of itself, mean that they are developing rich understandings of data and of the subtleties of data representation. Rather, too often they use the tool to produce "quick but meaningless graphs" without having a real grasp of the nature of the data with which they are working (Ainley and Pratt, 1995, p. 438). Graphs, generated by hand or electronically, must not be relegated to the passive role of presentation tools. Rather, they need to become central components of a wider analytical activity, used to interpret the data, identify trends and make predictions (Parker, 1992; Aily and Pratt, 1995).
    Location:
  • Author(s):
    Lucy, D.
    Editors:
    Rossman, A., &amp; Chance, B.
    Year:
    2006
    Abstract:
    The widespread international adoption of DNA technology in forensic science over the last twenty years or so has resulted in some standardised methods of data collection and data interpretation. The impetus generated by the systematic approach characteristic of forensic DNA has carried into other fields of forensic science, typically resulting in forensic scientists wondering whether the same approaches can be applied to their own specialisms. Workers in areas of forensic interest such as ballistics and trace evidence have for some time collected in a systematic manner data connected with those fields. However there are many more areas of forensic science which require large bodies of systematically collected data. Some of these areas are so rarely used in forensic science that the required data is not available, and for a few areas of evidence it is infeasible, if not impossible to collect suitable data.
  • Author(s):
    Witmer, J. A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Real data and statistical techniques can motivate many traditional mathematical topics at the secondary level. Collectively, the important statistical ideas and ways of reasoning can be developed in the context of studying mathematics. The study of formulas, linearity, centers, inequalities, matrices, and logarithms can be embedded in data and statistics and used to lay the foundation for the mathematics and to demonstrate the relevance of statistics to the world. Data Driven Mathematics provides teachers and students with application based activities that makes this happen and that can be used in conjunction with a standard mathematics course or to design a data based statistics course.
  • Author(s):
    Tammy A. Grace &amp; Shlomo S. Sawilowsky
    Year:
    2009
    Abstract:
    The proper analysis of data is predicated on the existence of a data set containing valid responses. There are many sound techniques that should be employed to minimize data errors, and to cleanse data sets. The purpose of this article is to provide instructors and their students with an overview of the mechanics of data capture; the metadata framework; outlier detection and treatment; and contemporary solutions for missing data.

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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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