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11 - 20 of 108 results for: STATS

STATS 195: Introduction to R (CME 195)

This short course runs for the first four weeks of the quarter and is offered in the fall. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut | Units: 1

STATS 198: Practical Training

For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable 2 times (up to 6 units total)

STATS 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

STATS 200: Introduction to Statistical Inference

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.
Terms: Win, Sum | Units: 3

STATS 201: Design and Analysis of Experiments

Theory and applications. Factors that affect response. Optimum levels of parameters. How to balance theory and practical design techniques. Prerequisites: basic statistics and probability theory.
Terms: Aut, Sum | Units: 3-5

STATS 202: Data Mining and Analysis

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, association rules, clustering, case based methods, and data visualization.
Terms: Aut, Sum | Units: 3

STATS 203: Introduction to Regression Models and Analysis of Variance

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.
Terms: Spr | Units: 3

STATS 206: Applied Multivariate Analysis

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.
Terms: Aut | Units: 3

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 state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Spr | Units: 3
Instructors: Donoho, D. (PI)

STATS 208: Introduction to the Bootstrap

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.
Terms: Spr | Units: 3
Instructors: Donoho, D. (PI)
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