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Bedwell, Michael – Teaching Statistics: An International Journal for Teachers, 2009
This article argues that the Normal distribution is often not a good model for anthropomorphic data.
Descriptors: Models, Statistical Distributions, Item Response Theory, Statistics
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du Feu, Chris – Mathematics in School, 1996
Responds to an earlier article by the same title in Mathematics in School; v25 n2. Asks the question, Which diagram communicates the nature of the distribution most effectively? Uses debate about the number of angels that can balance on a pin head to illustrate various statistical diagrams and explains problems inherent in each. Concludes with…
Descriptors: Diagrams, High Schools, Mathematics Education, Models
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Stewart, John A. – Information Processing and Management, 1994
Illustrates that the Poisson-lognormal model provides good fits to a diverse set of distributions commonly studied in bibliometrics and scientometrics. Topics discussed include applications to the empirical data sets related to the laws of Lotka, Bradford, and Zipf; causal processes that could generate lognormal distributions; and implications for…
Descriptors: Bibliometrics, Mathematical Formulas, Models, Research Needs
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Burrell, Quentin L.; Fenton, Michael R. – Journal of the American Society for Information Science, 1994
Proposes and explains a modification of the mixed Poisson model for library circulations which takes into account the periods when a book is out on loan and therefore unavailable for borrowing. Highlights include frequency of circulation distributions; negative binomial distribution; and examples of the model at two universities. (Contains 34…
Descriptors: Academic Libraries, Higher Education, Library Circulation, Library Statistics
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Losee, Robert – Journal of the American Society for Information Science, 1987
Presents a coordination level matching algorithm to be used in document retrieval systems that incorporate relevance feedback strategies. It is argued that this algorithm may eliminate the need for the frequent reevaluation of documents that is currently found in such systems, and conditions under which reranking is unnecessary are given.…
Descriptors: Algorithms, Estimation (Mathematics), Evaluation Criteria, Feedback
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Losee, Robert M. – Journal of the American Society for Information Science, 1988
Describes probabilistic document retrieval systems as a sequential learning process, in which the system learns the parameters of probability distributions describing the frequencies of feature occurrences in relevant and nonrelevant documents. Several techniques for estimating the parameters of distributions are described and the results of tests…
Descriptors: Estimation (Mathematics), Feedback, Information Retrieval, Man Machine Systems