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Albert, Jim; Hu, Jingchen – Journal of Statistics Education, 2020
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an…
Descriptors: Bayesian Statistics, Computation, Statistics Education, Undergraduate Students
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Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
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Azevedo, Ana, Ed.; Azevedo, José, Ed. – IGI Global, 2019
E-assessments of students profoundly influence their motivation and play a key role in the educational process. Adapting assessment techniques to current technological advancements allows for effective pedagogical practices, learning processes, and student engagement. The "Handbook of Research on E-Assessment in Higher Education"…
Descriptors: Higher Education, Computer Assisted Testing, Multiple Choice Tests, Guides
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Trafimow, David – Teaching Statistics: An International Journal for Teachers, 2011
Students often have difficulty understanding algebraic proofs of statistics theorems. However, it sometimes is possible to prove statistical theorems with pictures in which case students can gain understanding more easily. I provide examples for two versions of Bayes' theorem.
Descriptors: Visual Aids, Bayesian Statistics, Mathematical Logic, Validity
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CadwalladerOlsker, Todd D. – Mathematics Teacher, 2011
Bayes's theorem is notorious for being a difficult topic to learn and to teach. Problems involving Bayes's theorem (either implicitly or explicitly) generally involve calculations based on two or more given probabilities and their complements. Further, a correct solution depends on students' ability to interpret the problem correctly. Most people…
Descriptors: Critical Thinking, Probability, Mathematical Logic, Mathematics Skills
Schmalz, Steve W.; Cartledge, Carolyn M. – 1982
During the last decade the use of Bayesian statistical method has become quite prevalent in the educational community. Yet, like most statistical techniques, little has been written concerning the application of these methods to the classroom setting. The purpose of this paper is to help correct such a deficiency in the literature by developing a…
Descriptors: Bayesian Statistics, Classroom Techniques, Mastery Tests, Mathematical Models
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Khuri, Andre – International Journal of Mathematical Education in Science and Technology, 2004
The Dirac delta function has been used successfully in mathematical physics for many years. The purpose of this article is to bring attention to several useful applications of this function in mathematical statistics. Some of these applications include a unified representation of the distribution of a function (or functions) of one or several…
Descriptors: Maximum Likelihood Statistics, Bayesian Statistics, Statistics, College Mathematics
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Jarrell, Stephen – Mathematics and Computer Education, 1990
Explains a new way of viewing Bayes' formula. Discusses the revision factor and its interpretation. (YP)
Descriptors: Bayesian Statistics, College Mathematics, Computation, Decimal Fractions
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Sahai, Hardeo; Reesal, Michael R. – School Science and Mathematics, 1992
Illustrates some applications of elementary probability and statistics to epidemiology, the branch of medical science that attempts to discover associations between events, patterns, and the cause of disease in human populations. Uses real-life examples involving cancer's link to smoking and the AIDS virus. (MDH)
Descriptors: Bayesian Statistics, Epidemiology, Integrated Activities, Mathematical Applications