Literature Index

Displaying 581 - 590 of 3326

  • Classroom and platform performance

    Author(s):
    Mosteller, F.
    Year:
    1980
    Abstract:
    According to the author, there are five main components of teaching a statistics class. First, the importance of the subject matter must be demonstrated and conveyed to students by illustrating its application to the real world. It is suggested that teachers start the lecture with a few remarks about some real world problem that uses the specific method to be taught in the day's lecture. Second, a demonstration must be worked out prior to class presentation for a higher rate of success. Teachers should get students involved by having them gather and analyze their own data in class. Third, teachers should provide students with concrete real life problems that use the technique to be taught in the lesson. The author suggests that applications or activities are drawn from the students' own interests. To an extent, components 1-3 are similar to "examples training" and components 4 & 5 are similar to "formal training" (see Fong, Krantz, Nisbett; 1986). The fourth and fifth components refer to the actual lesson or lecture beginning with instruction of statistical and probability principles and ending with presentation of proofs or plausibility arguments.

  • Classroom and Worksheet Activities across the Curriculum.

    Author(s):
    Connor, D. & Holmes, P.
    Editors:
    Goodall, G.
    Year:
    2002
    Abstract:
    This article shows how teachers can create useful classroom activities to underpin data-handling methods for pupils aged 7-19. We use the data base of responses from the UK CensusAtSchool project that are available for pupils and teachers.

  • CLASSROOM NOTES An Upper Bound on the Value of the Standard Deviation.

    Author(s):
    Croucher, J. S.
    Editors:
    Goodall, G.
    Year:
    2004
    Abstract:
    This article derives a simple upper bound for the sample standard deviation that could be useful in guarding against gross errors of calculation.

  • CLASSROOM NOTES Sensitivity of the ?<sup>2</sup> Goodness-of-Fit Test to the Choice of Classes.

    Author(s):
    Mooney, J. & Jolliffe, I.
    Editors:
    Goodall, G.
    Year:
    2004
    Abstract:
    The ?<sup>2</sup> goodness-of-fit test is often one of the first tests of hypotheses encountered by students. When some of the expected frequencies are small, classes need to be combined. A real-life example is given that illustrates a surprising sensitivity of the results of the test to the way in which such combinations are chosen.

  • Classroom practicals using the binomial distribution

    Author(s):
    Rouncefield, M.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    In this paper, practical experiments are used not only to illustrate the binomial distribution, but to make it relevant and important to the pupils. The aim is to take statistics and probability out of the textbook and into the pupils' direct experience.

  • Classroom research: Assessment of student understanding of sampling distributions of means and the Central Limit Theorem in post-calculus probability and statistics classes.

    Author(s):
    Lunsford, M. L., Rowell, G. H., & Goodson-Espy, T.
    Editors:
    Stephenson, W. R.
    Year:
    2006
    Abstract:
    We applied a classroom research model to investigate student understanding of sampling distributions of sample means and the Central Limit Theorem in post-calculus introductory probability and statistics courses. Using a quantitative assessment tool developed by previous researchers and a qualitative assessment tool developed by the authors, we embarked on data exploration of our students' responses on these assessments. We observed various trends regarding their understanding of the concepts including results that were consistent with research completed previously (by other authors) for algebra-based introductory level statistics students. We also used the information obtained from our data exploration and our experiences in the classroom to examine and conjecture about possible reasons for our results.
    Location:
    http://www.amstat.org/publications/jse/v14n3/lunsford.html

  • Cleaning Dirty Data in Excel.

    Author(s):
    Hung, N. & Tyrell, S.
    Editors:
    Goodall, G.
    Year:
    2002
    Abstract:
    This article gives a flavour of the CHIME materials on data handling in Microsoft Excel. It demonstrates the facilities for data validation within Excel.

  • Clinical approach to training statistics teachers

    Author(s):
    Rustagi, J.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., & Constable, G. M.
    Year:
    1983
    Abstract:
    One of the important issues not adequately addressed is the training of teachers of statistics for schools and colleges. In this paper, we propose to provide a framework to enrich the training of statistics teachers at the college level.

  • Co-constructing Statistical Knowledge

    Author(s):
    Carvalho, C. & Cesar, M.
    Editors:
    Starkings, S.
    Year:
    2000
    Abstract:
    In the literature means, modes and medians are referred as measures of central tendency and they are important concepts in data handling and analysis. Some authors (Batanero et al., 1994; Carvalho, 1996, 1998; Cudmore, 1996; Hawkins, Jolliffe and Glickman, 1991) also stress that students have difficulty with these basic concepts and to some of them these concepts can be reduced to a computation formula. The main goal of this study was to analyse peer interactions in order to understand their role in pupils' performances when they were solving statistical tasks. A deep analysis of their discourse makes clear the way they construct an intersubjectivity (Wertsch, 1991) that facilitates the choice of the solving strategies and helps pupils to undertake their mistakes.
    Location:
    see Papers on Statistical Education from ICME-9 item #2834

  • Cognition as Intuitive Statistics

    Author(s):
    Gigerenzer, G., & Murray, D. J.
    Year:
    1987
    Abstract:
    The present book focuses on the intersection between two recent lines of thought. Both have been called "revolutions." The term "cognitive revolution" signifies the transition from understanding humans in terms of overt behavior to understanding them in terms of mental structures and processes. The term "probabilistic revolution" describes the transition from a deterministic understanding of science, in which uncertainty and variability were not permitted, to an understanding of science where probabilistic ideas became indispensable in theorizing. From the intersection of the inference revolution and the rising cognitive revolution a new understanding of the mind emerged: The mind as an "intuitive statistician." It is this second revolution on which the book focuses: It treats the new view of cognitive processes as statistical inference and hypotheses testing. But claim that the success of the second revolution relies heavily on that of the first and that the new methods of inferences have been transplanted to serve as explanations for how many cognitive processes work., and this has brought to cognitive psychology both a unifying perspective and, also certain blind spots inherent in these institutionalized statistical tools.

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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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