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Course Criteria
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3.00 Credits
Staff. mw 2.30-3.45 QR (0) Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used.
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3.00 Credits
Staff. mwf 2.30-3.20 QR (37) Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data. After math 118a or b or 120a or b. Some acquaintance with matrix algebra and computing assumed.
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3.00 Credits
Staff mw1-2.15 QR (0) Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications to image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution. After stat 241a or equivalent.
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3.00 Credits
Staff. tth9-10.15 QR (22) The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to s-plus); alternatives to least squares. Generalized linear models. Linear algebra and some acquaintance with statistics assumed. After stat 242b and math 222a or b or 225a or b.
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3.00 Credits
Timothy Gregoire, Jonathan Reuning-Scherer. m 2.30-4.30 QR (0) Principles of design for planned experiments, coupled with methods of analysis of experimental data. Strengths and weaknesses of block, split-plot, and completely randomized designs; extensive analysis of data that these designs produce. Some attention to questions of sample size estimation. Prerequisite: an introductory course in statistics.
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3.00 Credits
Staff. tth 2.30-3.45 QR (0) Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis assumed.
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3.00 Credits
Staff. mw 2.30-3.45 QR (0) Through analysis of data sets using the R statistical computing language, study of a selection of statistical topics such as linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. Weekly sessions in the Statistical Computing laboratory. After stat 242b and math 222a orb or 225a or b, or equivalents.
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3.00 Credits
Jonathan Reuning-Scherer. tth1-2.15 QR (0) Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either sas or spss programming software. Prerequisites: knowledge of basic inferential procedures and experience with linear models.
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