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STATS 206: Applied Multivariate Analysis
3.00 Credits
Stanford University
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre- or corequisite: 200. 3 units, Aut (Khalessi, S), Sum (Staff)
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STATS 208: Introduction to the Bootstrap
3.00 Credits
Stanford University
The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and confidence intervals; related resampling methods including the jackknife, cross-validation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability. 3 units, Spr (Holmes, S)
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STATS 209: Understanding Statistical Models and their Social Science Applications
3.00 Credits
Stanford University
(Same as EDUC 260X, HRP 239.) Statistical modeling in experimental and non-experimental settings, including misconceptions in social science applications such as causal models. Text is Statistical Models: Theory and Practice, by David Freedman. See http://www-stat.stanford.edu/~rag/stat209. Prerequisite: intermediate-level statistical methods including multiple regression, logistic regression, and log-linear models. 3 units, Win (Rogosa, D)
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STATS 209 - Understanding Statistical Models and their Social Science Applications
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STATS 211: Topics in Quantitative Methods:Meta-Analysis
3.00 Credits
Stanford University
Meta-analysis as a quantitative method for combining the results of independent studies enabling researchers to evaluate available evidence. Examples of meta-analysis in medicine, education, and social and behavioral sciences. Statistical methods include nonparametric methods, contingency tables, regression and analysis of variance, and Bayesian methods. Project involving an existing published meta-analysis. Prerequisite: basic sequence in statistics. 1-3 units, Win (Olkin, I)
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STATS 211 - Topics in Quantitative Methods:Meta-Analysis
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STATS 212: Applied Statistics with SAS
3.00 Credits
Stanford University
Data analysis and implementation of statistical tools in SAS. Topics: reading in and describing data, categorical data, dates and longitudinal data, correlation and regression, nonparametric comparisons, ANOVA, multiple regression, multivariate data analysis, using arrays and macros in SAS. Prerequisite: statistical techniques at the level of STATS 191 or 203; knowledge of SAS not required. 3 units, Sum (Staff)
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STATS 212 - Applied Statistics with SAS
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STATS 214: Randomness in the Physical World
3.00 Credits
Stanford University
(Same as APPPHYS 214.) Topics include: random numbers, and their generation and application; disordered systems, quenching, and annealing; percolation and fractal structures; universality, the renormalization group, and limit theorems; path integrals, partition functions, and Wiener measure; random matrices; and optical estimation. Prerequisite: introductory course in statistical mechanics or analysis. 3 units, Spr (Diaconis, P; Fisher, D; Holmes, S), alternate years, not given next year
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STATS 214 - Randomness in the Physical World
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STATS 215: Statistical Models in Biology
3.00 Credits
Stanford University
Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as in STATS 217, but emphasis is on examples drawn from applications in biology, especially genetics. Prerequisite: 116 or equivalent. 3 units, Win (Zhang, N)
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STATS 215 - Statistical Models in Biology
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STATS 217: Introduction to Stochastic Processes
3.00 Credits
Stanford University
Discrete and continuous time Markov chains, point processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Prerequisite: STATS 116 or consent of instructor. 3 units, Win (Rajaratnam, B), Sum (Staff)
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STATS 217 - Introduction to Stochastic Processes
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STATS 218: Introduction to Stochastic Processes
3.00 Credits
Stanford University
Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales. 3 units, Spr (Staff), Sum (Staff)
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STATS 218 - Introduction to Stochastic Processes
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STATS 219: Stochastic Processes
3.00 Credits
Stanford University
(Same as MATH 136.) Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite: STATS 116 or MATH 151 or equivalent. Recommended: MATH 115 or equivalent. 3 units, Aut (Ross, K)
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STATS 219 - Stochastic Processes
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