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Course Criteria
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4.00 Credits
This course is an introduction to the basic concepts of linear algebra, with an emphasis on matrix manipulation. Topics include Gaussian elimination, matrix arithmetic, determinants, Cramer's rule, vector spaces, linear independence, basis, nullspace, row and column spaces of a matrix, eigenvalues and eigenvectors. Various applications are studied throughout the course. (1016-305 or 1016-366) Class 4, Credit 4 (F, W, S)
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4.00 Credits
This course covers descriptive statistics; sample spaces and events; axioms of probability; counting techniques; conditional probability and independence; distributions of discrete and continuous random variables; joint distributions; and central limit theorem. (1016-273 or 1016-283) Class 4, Credit 4 (F, W, S)
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4.00 Credits
This course covers basic statistical concepts, sampling theory, hypothesis testing, confi dence intervals, point estimation and simple linear regression. A statistical software package is used for data analysis and statistical applications. (1016-351) Class 4, Credit 4 (F, W, S)
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4.00 Credits
This course is an introduction to simple linear regression, analysis of variance, and the use of the statistical software package SAS. (1016-314 or 1016-352) Class 4, Credit 4 (S)
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4.00 Credits
This course is a study of regression techniques with applications to the type of problems encountered in real-world situations. It includes use of statistical software. Topics include review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, various other models as time permits. (1016-353 and 331 or equivalent) Class 4, Credit 4 (W)
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4.00 Credits
This course is a study of the design and analysis of experiments and includes extensive use of statistical software. Topics include single factor analysis of variance; multiple comparisons and model validation; multifactor factorial designs; fi xed, random and mixed models; expected mean square calculations; confounding; randomized block designs; other designs and topics as time permits. (1016-314 or 1016-352) Class 4, Credit 4 (F)
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4.00 Credits
This course is a review of probability models associated with control charts; control charts for continuous and discrete data; interpretation of control charts; and some standard sampling plans. A statistical software package is used for data analysis. (1016-314 or 1016-352) Class 4, Credit 4 (S)
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4.00 Credits
This is an introduction to the mathematical theory of combination, arrangement, and enumeration of discrete structures. Topics include enumeration, recursion, inclusion-exclusion, block design, generating functions. (1016-265 or permission of instructor) Class 4, Credit 4 (W)
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4.00 Credits
This course is a continuation of 1016-265 Discrete Mathematics I with applications in computer science. Topics include relations, their closures, equivalence relations, partial orderings, recursively defi ned sets, countable and uncountable sets, and an introduction to graph theory. (1016-265) Class 4, Credit 4 (F, W, S)
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4.00 Credits
This is an introduction to the skills necessary for independent research on a mathematical or statistical problem with a focus on a specifi c research problem or problems. Literature search techniques, writing, and presentations are included in the course. The students work on a research topic. (1016-331 or permission of instructor) Class 4, Credit 4 (S)
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