Literature Index

Displaying 2491 - 2500 of 3326
  • Author(s):
    Jim Albert
    Year:
    2007
    Abstract:
    An introductory statistics course is described that is entirely taught from a baseball perspective. This class has been taught as a special section of the basic introductory course offered at Bowling Green State University . Topics in data analysis are communicated using current and historical baseball datasets. Probability is introduced by describing and playing tabletop baseball games. Inference is taught by distinguishing between a player's "ability" and his "performance", and then describing how one can learn about a player's ability based on his season performance. Baseball issues such as the proper interpretation of situational and "streaky" data are used to illustrate statistical inference.
  • Author(s):
    MacGillivray, H. L.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    This paper is about teaching large service courses in introductory statistics, with particular reference to engineering students and science students not majoring in mathematics.
  • Author(s):
    Holmes, P.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Over many years I have been attempting to improve statistical literacy in the population by changing the school curriculum. All such attempts have to be put in the general context of teaching, learning and assessing the subject. Ideally these should complement and reinforce each other. In practice they often conflict - in particular assessment can distort the learning process. In this talk I consider the nature of these conflicts and how they might be overcome in practice, giving examples from a lifetime's experience.
  • Author(s):
    Parker, J., & Widmer, C. C.
    Year:
    1992
    Abstract:
    Presents a series of four steps used in data analysis processes that help students investigate and interpret real world situations. Gives activities that employ computer software to create representative graphs of the data in the analysis process. (MDH)
  • Author(s):
    Megan M. Marron & Abdus S. Wahed
    Year:
    2016
    Abstract:
    Missing data mechanisms, methods of handling missing data, and the potential impact of missing data on study results are usually not taught until graduate school. However, the appropriate handling of missing data is fundamental to biomedical research and should be introduced earlier on in a student's education. The Summer Institute for Training in Biostatistics (SIBS) provides practical experience to motivate trainees to pursue graduate training and biomedical research. Since 2010, SIBS Pittsburgh has demonstrated the feasibility of introducing missing data concepts to trainees in a small-group project-based setting that involves both simulation and data analysis. After learning about missing data mechanisms and statistical techniques, trainees apply what they have learned to a NIDDK/NIH-funded Hepatitis C treatment study, to examine how various hypothesized missing data patterns can affect results. A simulation is also used to examine the bias and precision of these methods under each missing data pattern. Our experience shows that under such project-based training, advanced topics, such as missing data, can be presented to trainees with limited statistical preparation, and ultimately, can further their statistical literacy and reasoning.
  • Author(s):
    Lindgren, G., & Zetterqvist, L.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    The Mathematical statistics division at Lund University teaches 8 core and 13 elective statistics courses within 14 different engineering programmes leading to Master of Science in Engineering. This paper uses cases to analyse the combination of ingredients that seems to make the difference between success and otherwise in design of curriculum for engineering programmes. The key aspects underpinning the efforts seem to be collaborative curriculum development, and a joint view from both engineering and statistics of the role of mathematics and statistics in technology and engineering. This respect and high regard for mathematical statistics from the engineering side, often arising from research or other collaboration, changes them from clients to partners. This paper is an attempt to systematize what we see as the important success factors in an engineering statistics education.
  • Author(s):
    Anderson, M. J.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Multivariate data in ecological applications most often occur in the form of counts of species abundances in assemblages, where each species is a variable. These data do not generally conform to traditional statistical assumptions, and so special approaches and methods are needed in this context. Statisticians need to be informed about these special problems with ecological data. In addition, the rationale for complex experimental designs that is a trademark of most ecological studies needs to be well understood by applied statisticians in this area. On the other hand, successful approaches for teaching ecologists about the use of multivariate statistics include sticking to the conceptual, rather than the mathematical. I provide here an overview of the methods that have helped teaching across these two disciplines, including a general approach for the use of novel non-parametric methods in the analysis of ecological community data.
  • Author(s):
    Blejec, A.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    Statistical methods are widely used in everyday life and research. Users are very different in their abilities and skills. Given the diversity of users every statistical educator must find a way to teach statistics and to decide what is important and understandable for a specific group of users. We shall try to present some views of the teaching of statistics.
  • Author(s):
    Yanagawa, T.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., & Constable, G. M.
    Year:
    1983
    Abstract:
    The influence of Japanese statisticians on the teaching of statistics has been insignificant; we still have no research and teaching institute such as a Department of Statistics in Japanese Universities. The following are examples of our efforts and indications of the improvement in the environment and the quality of statistical teaching in Japan.
  • Author(s):
    Odhiambo, J. W.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In Kenya today statistics is taught at various levels in the education system to various degrees of coverage and sophistication. At secondary school level there are rudimentary elements of descriptive statistics and probability. In teachers colleges elements of descriptive statistics and statistical inference are taught to those taking mathematics as a major teaching subject in secondary school. In the universities, statistics is taught principally in departments of mathematics in science faculties; but statistics is also taught to students of commerce, economics sociology, education, engineering, agriculture and computer science. This paper reviews the curriculum in statistics, the teaching approaches, availability of qualified teaching staff, availability of teaching and learning resources and performance of students. Emphasis is on teaching of statistics at university level.

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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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