Undergraduate courses in Statistics

(F,Sem) Stanford Introductory Seminar. Preference to freshmen. Old and new reports of ESP experiments at Stanford and elsewhere. Principles of experimental design, randomization, experimental control and confounding, response modeling, and probabilistic calculation. Design and execution of student ESP experiments, literature reviews, probability calculations, critiques, and oral and written presentations.

3 units, Aut (Switzer, P)

STATS 47N. Breaking the Code?

(F,Sem) Stanford Introductory Seminar. Preference to freshmen. Cryptography and its counterpart, cryptanalysis or code breaking. How the earliest cryptanalysts used statistical tools to decrypt messages by uncovering recurring patterns. How such frequency-analysis tools have been used to analyze biblical texts to produce a Bible code, and to detect genes in the human genome. Overview of codes and ciphers. Statistical tools useful for code breaking. Students use simple computer programs to apply these tools to break codes and explore applications to various kinds of data. GER:DB-Math

3 units, Aut (Holmes, S)

STATS 50. Mathematics of Sports

(Same as MCS 100.) The use of mathematics, statistics, and probability in the analysis of sports performance, sports records, and strategy. Topics include mathematical analysis of the physics of sports and the determinations of optimal strategies. New diagnostic statistics and strategies for each sport. Corequisite: STATS 116. GER:DB-Math

3 units, not given this year

STATS 60. Introduction to Statistical Methods: Precalculus

(Same as PSYCH 10, STATS 160.) 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. GER:DB-Math

5 units, Aut (Thomas, E), Win (Walther, G), Spr (Boik, J), Sum (Staff)

STATS 110. Statistical Methods in Engineering and the Physical Sciences

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)

STATS 116. Theory of Probability

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)

STATS 141. Biostatistics

(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)

STATS 166. Computational Biology

(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)

STATS 191. Introduction to Applied Statistics

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)

STATS 199. Independent Study

For undergraduates.

1-15 units, Aut (Staff), Win (Staff), Spr (Staff), Sum (Staff)

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Stanford Bulletin, Archive 2008-09

Stanford Bulletin, Archive 2008-09

Alphabetical Index

Bulletin Archive

Undergraduate courses in Statistics