Prerequisite: STAT 381. (Undergraduates register in STAT 410; graduates enroll in STAT 510.)

Simple linear regression: estimation and inference, prediction, analysis of residuals, detection of outliers, use of transformations. Multiple linear regression: influence diagnostics, multi-collinearity, selection of variables, simultaneous estimation and inference, validation techniques. Statistical software for data analysis used.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 480 or MATH 590.

Prerequisite: STAT 381 or consent of instructor.

Properties of a random sample, convergence in probability, law of large numbers, sampling from the normal distribution, the central limit theorem, principles of data reduction, likelihood principle, point estimation, Bayesian estimation, methods of evaluating estimators, hypothesis testing, decision theory, confidence intervals.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 580.

Prerequisite: STAT 381 or consent of instructor.

Design of experiments to permit efficient analysis of sources of variation with application to quality assurance. Factorial and fractional factorial designs; block designs; confounding. Fixed and random effect models. Effects of departure from assumptions; transformations. Response surface techniques. Taguchi methods.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 581.

Prerequisite: STAT 381 or consent of instructor.

Introduction to methods of statistical quality control. Includes control charts, acceptance sampling, process capability analysis, and aspects of experimental design.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 584.

Prerequisite: STAT 381 or consent of instructor.

Theory and practice of sampling from finite populations. Simple random sampling, stratified random sampling, systematic sampling, cluster sampling, properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 583.

Prerequisites: STAT 381 and STAT 410/510 or consent of instructor.

The methodology of statistical consulting: guidelines for client-consultant communications, presentations to clients, basics of writing final technical reports, thorough case studies involving advanced statistical analysis, invited client presentations, real-life projects, group discussions, written and oral statistical reports by students.

Letter grade only (A-F). (Lecture 3 hrs.)

Prerequisite: STAT 381; Prerequisite/Corequisite STAT 410. (Undergraduates register in STAT 450; graduates enroll in STAT 550.)

Discriminate analysis, principal components, factor analysis, cluster analysis, logistic regression, canonical correlation, multidimensional scaling, and some nonlinear techniques. Statistical software used.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 483 or MATH 593.

Prerequisite: STAT 410 or STAT 510, or consent of instructor.

Alternatives to normal-theory statistical methods, analysis of categorical and ordinal data, methods based on ranks, measures of association, goodness of fit tests, order statistics.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 585.

Prerequisite: STAT 381 or consent of instructor.

Simulation modeling techniques; generation of discrete and continuous random numbers from given distributions; Monte Carlo methods; discrete event simulations, statistical analysis of simulated data; variance reduction; statistical validation; introduction to simulation languages; industry applications. Statistical packages used.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 587 or MATH 487.

Prerequisite: STAT 381 or consent of instructor.

Random number generation, sampling and subsampling, exploratory data analysis, Markov chain Monte Carlo methods, density estimation and EM algorithm. Topics of current interest.

Letter grade only (A-F). (Lecture 3 hrs.)

Prerequisite: STAT 410, or STAT 510, or consent of instructor.

Basics of data mining algorithms with emphasis on industrial applications. Prediction and classification techniques such as decision trees, neural networks, Multivariate Adaptive Regression Splines, and other methods. Several software packages utilized.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 586.

Prerequisite: STAT 410/510 or consent of instructor.

Genetic algorithms, fuzzy logic, discrete choice analysis, online analytical processing, structured query language, statistical database management, and text and web mining. Topics of current interest.

Letter grade only (A-F). (Lecture 3 hrs).

Prerequisite: STAT 381 or consent of instructor.

Includes moving averages, smoothing, Box-Jenkins (ARIMA) models, testing for nonstationarity, model fitting and checking, prediction and model selection, seasonal adjustment, ARCH, GARCH, cointegration, state-space models. Statistical packages used throughout the course.

Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 582.

Prerequisite: STAT 381 or consent of instructor.

Lifetime distributions, hazard and survival functions, censoring and truncation, Kaplan Meier and Nelson-Aelen estimators, Cox proportional hazard models, m-sample tests, goodness-of-fit tests, Bayesian survival analysis, analysis of multivariate survival data, exploring longitudinal data designs and models, clinical trials.

Letter grade only (A-F). (Lecture 3 hrs.)

Prerequisite: Consent of instructor. (Undergraduates register in STAT 495; graduates enroll in STAT 595.)

Topics of current interest from statistics literature.

Letter grade only (A-F). Course may be repeated to a maximum of 6 units with different topics. (Lecture 3 hrs)

Prerequisite: Consent of instructor.

Presentation and discussion of advanced work in applied statistics.

May be repeated to a maximum of six units. Letter grade only (A-F).

Prerequisite: Consent of instructor.

Research on a specific area in applied statistics. Topic for study to be approved and directed by a statistics faculty member.

Credit/No Credit only.

Prerequisite: Advancement to candidacy.

Formal report of research or project in mathematics.

Letter grade only (A-F). May be repeated to a maximum of 6 units.

- Bachelor of Science in Mathematics
- Option in Applied Mathematics
- Option in Statistics
- Option in Mathematics Education – Single Subject Preliminary Credential Mathematics (code 165)
- Honors in Mathematics
- Minor in Mathematics
- Minor in Applied Mathematics
- Minor in Statistics

- How to Apply
- Master of Science in Mathematics
- Option in Applied Mathematics
- Option in Mathematics Education for Secondary School Teachers
- Master of Science in Applied Statistics