| source Berkeley (X) |
level |
department Statistics (X) |
Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.
Score: 8.875565 Details | Listing | Web page
For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.
Score: 8.875565 Details | Listing | Web page
Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
Score: 8.875565 Details | Listing | Web page
Emphasis on concepts and applications. Conditional probability. Independence. Expectation. Standard discrete and continuous distributions. Regression and correlation. Point and interval estimation. Illustrations from engineering.
Score: 8.875565 Details | Listing | Web page
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.
Score: 8.875565 Details | Listing | Web page
Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.
Score: 8.875565 Details | Listing | Web page
Ideas for estimation and hypothesis testing basic to applications. Linear estimation and normal regression theory.
Score: 8.875565 Details | Listing | Web page
An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.
Score: 8.875565 Details | Listing | Web page
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
Score: 8.875565 Details | Listing | Web page
A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, goodness-of-fit tests, analysis of variance, and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.
Score: 8.875565 Details | Listing | Web page
Study of bioinformatics problems such as DNA pattern finding, gene expression data analysis, molecular evolution models, and biomolecular sequence database searching. Introduction of the necessary probability and statistics: events, (conditional) probability, random variables, estimation, testing, and linear regression. Also listed as Bioengineering C141.
Score: 8.875565 Details | Listing | Web page
This course provides an introduction to statistical and computational methods for the analysis of biomedical and genomic data. Statistical topics, introduced in a biological context, include numerical and graphical summaries of data; basic notions in probability; loss-based estimation (e.g., least-squares regression, maximum likelihood estimation); model selection; multiple hypothesis testing; Markov chains; hidden Markov models, resampling; simulation studies. Biological questions considered include, but are not limited to, modeling meiosis; genetic mapping; nucleotide and protein-sequence analysis; molecular evolution; computational gene finding; and DNA microarray experiments. The course also introduces statistical computing resources for the analysis of biological data, with emphasis on the R language and environment (www.r-project.org) and bioconductor packages (www.bioconductor.org). In addition, the course introduces basic notions in genetics and molecular biology and involves the critical reading of articles related to statistical analyses in the biological and medical sciences. Also listed as Public Health C143.
Score: 8.875565 Details | Listing | Web page
Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.
Score: 8.875565 Details | Listing | Web page
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.
Score: 8.875565 Details | Listing | Web page
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.
Score: 8.875565 Details | Listing | Web page
Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.
Score: 8.875565 Details | Listing | Web page
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
Score: 8.875565 Details | Listing | Web page
General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.
Score: 8.875565 Details | Listing | Web page
Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester, see departmental bulletins.
Score: 8.875565 Details | Listing | Web page
Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Score: 8.875565 Details | Listing | Web page
Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Score: 8.875565 Details | Listing | Web page
A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.
Score: 8.875565 Details | Listing | Web page
Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes. Also listed as Mathematics C218A.
Score: 8.875565 Details | Listing | Web page
Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes.
Score: 8.875565 Details | Listing | Web page
Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Measuretheory concepts needed for probability. Expectation, distributions. Laws of lar ge numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes. Also listed as Mathematics C218B.
Score: 8.875565 Details | Listing | Web page