STATS 204: Sampling
How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estimators, two stage sampling.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Owen, A. (PI)
STATS 205: Introduction to Nonparametric Statistics
Nonparametric analogs of the one and twosample
ttests and analysis of variance; the sign test, median test, Wilcoxon's tests, and the KruskalWallis and Friedman tests, tests of independence. Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Seiler, C. (PI)
STATS 206: Applied Multivariate Analysis
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computerintensive 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.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Johnstone, I. (PI)
;
Michael, H. (TA)
STATS 207: Introduction to Time Series Analysis
Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and statespace models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
STATS 208: Introduction to the Bootstrap
The bootstrap is a computerbased 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, crossvalidation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 209: Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239)
Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes framework, encouragement designs, multilevel models, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, path analysis and graphical models, group comparisons with longitudinal data. See
http://rogosateaching.com/stat209/. Prerequisite: intermediatelevel statistical methods.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Rogosa, D. (PI)
STATS 211: Metaresearch: Appraising Research Findings, Bias, and Metaanalysis (CHPR 206, HRP 206, MED 206)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Metaanalysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a metaresearch project or reworking and evaluation of an existing published metaanalysis. Prerequisite: knowledge of basic statistics.
Terms: Win

Units: 3

Grading: Medical Satisfactory/No Credit
Instructors:
Ioannidis, J. (PI)
STATS 212: Applied Statistics with SAS
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.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 213: Introduction to Graphical Models (STATS 313)
Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites:
STATS 217,
STATS 200 (preferably
STATS 300A),
MATH 104 or equivalent class in linear algebra.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 215: Statistical Models in Biology
Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the WrightFisher 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.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Siegmund, D. (PI)
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