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STATS 110: Statistical Methods in Engineering and the Physical Sciences
3.00 Credits
Stanford University
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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STATS 110 - Statistical Methods in Engineering and the Physical Sciences
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STATS 116: Theory of Probability
3.00 Credits
Stanford University
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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STATS 116 - Theory of Probability
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STATS 141: Biostatistics
4.00 - 5.00 Credits
Stanford University
(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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STATS 141 - Biostatistics
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STATS 160: Introduction to Statistical Methods:Precalculus
5.00 Credits
Stanford University
(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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STATS 160 - Introduction to Statistical Methods:Precalculus
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STATS 166: Computational Biology
2.00 - 3.00 Credits
Stanford University
(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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STATS 166 - Computational Biology
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STATS 191: Introduction to Applied Statistics
3.00 - 4.00 Credits
Stanford University
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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STATS 191 - Introduction to Applied Statistics
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STATS 199: Independent Study
1.00 - 15.00 Credits
Stanford University
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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STATS 199 - Independent Study
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STATS 200: Introduction to Statistical Inference
3.00 Credits
Stanford University
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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STATS 200 - Introduction to Statistical Inference
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STATS 202: Data Mining and Analysis
3.00 Credits
Stanford University
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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STATS 202 - Data Mining and Analysis
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STATS 203: Introduction to Regression Models and Analysis of Variance
3.00 Credits
Stanford University
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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STATS 203 - Introduction to Regression Models and Analysis of Variance
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