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Course Criteria
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3.00 Credits
No course description available.
Prerequisite:
Separate File
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4.00 Credits
Concepts of probability and statistics. Conditional probability, independence, random variables, distribution functions. Descriptive statistics, transformations, sampling errors, confidence intervals, least squares and maximum likelihood. Exploratory data analysis and interactive computing. Students may receive credit for only one of 390, STAT/ECON 481, and ECON 580. Offered: jointly with MATH 390; AWSpS.
Prerequisite:
either MATH 126 or MATH 136
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4.00 Credits
Meila Fundamentals of probability and statistics from the perspective of the computer scientist. Random variables, distributions and densities, conditional probability, independence. Maximum likelihood, density estimation, Markov chains, classification. Applications in computer science.
Prerequisite:
2.5 in MATH 126; 2.5 in MATH 308; either CSE 326, CSE 373, CSE 417, or CSE 421.
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3.00 Credits
Sample spaces; basic axioms of probability; combinatorial probability; conditional probability and independence; binomial, Poisson and normal distributions, central limit theorem.
Prerequisite:
either 2. 0 in MATH 126, or 2.0 in MATH 136; recommended: MATH 324 or MATH 327. Offered: jointly with MATH 394; AWS.
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3.00 Credits
Random variables; expectation and variance; laws of large numbers; normal approximation and other limit theorems; multidimensional distributions and transformations.
Prerequisite:
2.0 in STAT/MATH 394. Offered: jointly with MATH 395; WSpS.
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3.00 Credits
Characteristic functions and generating functions; recurrent events and renewal theory; random walk.Offered: jointly with MATH 396; Sp.
Prerequisite:
either 2 0 in MATH 395 or 2.0 in STAT 395.
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2.00 Credits
Techniques of effective writing and oral presentations in the mathematical sciences. Offered: jointly with AMATH 400/ MATH 400.
Prerequisite:
at least 15 credits in MATH, STAT, AMATH, or CSE at the 300 or 400 level, including MATH 307 or AMATH 351 and MATH 308 or AMATH 352
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4.00 Credits
Introduction to computerintensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases. Credit allowed for 403 or 503 but not both. Offered: Sp.
Prerequisite:
either STAT 220, STAT 301, STAT/ ECON 311, STAT 341, STAT 361, STAT/MATH 390, or STAT/ECON 481
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4.00 Credits
Computer-aided data analyses using comparisons between batches, analysis of variance and regression. Evaluation of assumptions, data transformation, reliability of statistical measures (jackknife, bootstrap). Fisher-Gosset controversy. Offered: A.
Prerequisite:
either STAT 342, STAT/MATH 390, or STAT/ECON 481; recommended: MATH 308
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4.00 Credits
Regression analysis. Problems in interpreting regression coefficients. Estimation, including two-stage least squares. Guided regression: building linear models, selecting carriers. Regression residuals. Analysis of variance. Nonparametric regression. Factorial designs, response surface methods. Offered: W.
Prerequisite:
either STAT 342, STAT/MATH 390, STAT 421, or STAT/ECON 481; recommended: MATH 308
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