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Course Criteria
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3.00 Credits
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. GER:DB-Math 4-5 units, Aut (Staff), Sum (Staff)
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3.00 Credits
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent. GER:DB-Math 3-5 units, Aut (Ross, K), Spr (Staff), Sum (Staff)
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4.00 - 5.00 Credits
(Same as BIO 141.) Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R. See http://www-stat.stanford.edu/~rag/ stat141/. GER:DB-Math 4-5 units, Aut (Boik, J; Rogosa, D)
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5.00 Credits
(Same as PSYCH 10, STATS 60.) Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages. 5 units, Aut (Thomas, E), Win (Walther, G), Spr (Boik, J), Sum (Staff)
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2.00 - 3.00 Credits
(Same as BIOMEDIN 366, STATS 366.) Methods to understand sequence alignments and phylogenetic trees built from molecular data, and general genetic data. Phylogenetic trees, median networks, microarray analysis, Bayesian statistics. Binary labeled trees as combinatorial objects, graphs, and networks. Distances between trees. Multivariate methods (PCA, CA, multidimensional scaling). Combining data, nonparametric inference. Algorithms used: branch and bound, dynamic programming, Markov chain approach to combinatorial optimization (simulated annealing, Markov chain Monte Carlo, approximate counting, exact tests). Software such as Matlab, Phylip, Seq-gen, Arlequin, Puzzle, Splitstree, XGobi. 2-3 units, Spr (Wong, W)
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3.00 - 4.00 Credits
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141. GER:DB-Math 3-4 units, Win (Taylor, J)
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1.00 - 15.00 Credits
For undergraduates. 1-15 units, Aut (Staff), Win (Staff), Spr (Staff), Sum (Staff) Primarily for graduate students; undergraduates may enroll with consent of instructor.
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3.00 Credits
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116. 3 units, Win (Romano, J), Sum (Staff)
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3.00 Credits
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, neural networks, association rules, clustering, case based methods, and data visualization. 3 units, Aut (Walther, G)
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3.00 Credits
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200. 3 units, Win (Zhang, N)
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