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Course Criteria
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4.00 Credits
An introduction to time series models and associated methods of data analysis and inference. Auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral analysis, and transfer function models.
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4.00 Credits
An introduction to major statistics packages used in academics and industry (SAS and R). Will discuss data entry and manipulation, implementing standard analyses and graphics, exploratory data analysis, simulation-based methods, and new programming methods.
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4.00 Credits
A serious introduction to statistical inference where linear models and related methods are used. Topics include the pros and cons of t-tools and their alternatives, multiple-group comparisons, linear regressions, model checking and refinement. Emphasis on statistical thinking and tools for real-life problems, application to current events whenever relevant.
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4.00 Credits
Statistical designs for efficient experimentation in physical, chemical, biological, social and management sciences and in engineering. A systematic approach to explore input-output relationships by deliberately manipulating input variables. Topics include analysis of variance, completely randomized and randomized block designs, Latin square designs, balanced incomplete block designs, factorial designs, confounding in blocks, fractional replications, orthogonal arrays, and response surface designs. Each topic is motivated by a real-life example.
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4.00 Credits
A sequel to Statistics 139, emphasizing common methods for analyzing categorical data. Topics include mixed effects model, contingency tables, log-linear models, logistic, Probit and Poisson regression, model selection, and model checking. Examples will be drawn from several fields, particularly from biology and social sciences.
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4.00 Credits
An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.
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4.00 Credits
Supervised reading and research in an area of statistics agreed upon by the student and a faculty adviser.
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4.00 Credits
The systematic application of statistical ideas to a problem area.
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4.00 Credits
Course description unavailable
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8.00 Credits
Introduction to the Sumerian language with emphasis on grammatical structure.
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