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Math 231 Probability and Statistics [3] Instructor: B. Eisenberg
Current Course Catalog Description
Probability and distribution of random variables; populations and random sampling; chi-square and distributions, estimation and tests of hypothesis; correlation and regression theory of two variables.
Textbook:
Walpole, Myers, Myers and Ye, Probability and Statistics for Engineers and Scientists, 7th ed.,
References
Course Goals
Students should master the following concepts and techniques, with the ultimate goal of their application in statistical inference: basic notions of probability; random variables and their distributions/densities, linear combinations; intensive work with important distributions (e.g. binomial, multinomial, hypergeometric, negative binomial, Poisson, normal, exponential and t distributions; random sampling and sampling distributions; point and interval estimation, hypothesis tests on the mean; see also Annotated Syllabus Outline, appended. Prerequisites by Topic
Mastery of the concepts and techniques of multivariable calculus, as developed in Mathematics 23 (see Course Description for Mathematics 23 and its Annotated Syllabus Outline, appended). Major Topics Covered in the Course
See Course Goals, above; also see Annotated Syllabus Outline, appended.
Laboratory projects (specify number of weeks on each)
Estimate CSAB Category Content
Data Structures
Every student is required to submit at least _____ written reports (not including exams, tests, quizzes, or commented programs) of typically _____ pages and to make _____ oral presentations of typically _____ minutes duration. Include only material that is graded for grammar, spelling, style, and so forth, as well as for technical content, completeness, and accuracy.
Social and Ethical Issues
Theoretical Content
Sample spaces, events and their probabilities, counting techniques, additive rules, conditional probability, independence, multiplicative rules; random variables and their distributions/densities; joint distributions/densities; mean, variance and covariance; Chebyshev’s theorem; Law of Large Numbers, Central Limit Theorem; random sampling and sampling distributions.
Problem Analysis
Identification of problem types and matching of techniques mastered to problem types. Developing an overview of the range of probability models and an understanding of which ones apply in which contexts, and why.
Solution Design
Devising and carrying out solution strategies to problems from a wide range of applications areas. |
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