Course Requirements

Computer Usage

The department places strong emphasis on computation and is well supported with equipment and software necessary for research. The computer software packages used in most courses are likely to play an important role in the solution of the problems students will encounter in their places of employment.

Comprehensive Exam Requirement

Candidates for the Master's of Science in Applied Statistics degree must pass two sets of comprehensive examinations. Part I covers the material in MAT 451-MAT 452-MAT 453, and Part II covers material in MAT 456, MAT 526 and MAT 528.

Comprehensive examinations are offered twice a year, in the autumn and spring quarters during last two weekends in October and April.
At the beginning of the quarter when students plan to take the comprehensive examinations, they should register with the program director.

MAT 441

APPLIED STATISTICS I

Parametric and non-parametric statistical inferential methods for the univariate and bivariate situations using SAS. Specific topics include classical and exploratory graphical & numerical methods of data descriptions; inference about means, medians, and associations including analysis of variance and linear regression. Data analytic projects are an integral part of the course.

MAT 442

APPLIED STATISTICS II

A continuation of MAT 441. Repeated measures design, association, analysis of covariance, and multivariate relationships. Diagnostics and model building. Methods of categorical data analysis. Logistical regression and log-linear models. Data analytical projects using SAS are an integral part of the course.
Prerequisites:
MAT 441 is a prerequisite for this class.

MAT 443

APPLIED STATISTICS III

A continuation of MAT 442. The course material generalizes univariate methods of inference to multivariate situations using SAS. Specific topics include canonical correlation, discriminate analysis, principal component analysis, factor analysis, and multivariate analysis of variance. Emphasis in the curse is on data analytic projects.
Prerequisites:
MAT 442 is a prerequisite for this class.

MAT 451

PROBABILITY AND STATISTICS I

The course covers elements of probability theory; distributions of random variables and linear functions of random variables; moment generating functions; and discrete and continuous probability models. COREQUISITE(S): MAT 260.
Prerequisites:
MAT 260 is a corequisite for this class.

MAT 452

PROBABILITY AND STATISTICS II

A continuation of MAT 451. More continuous probability model. Laws of large numbers and the central limit theorem. Sampling distributions of certain statistics. An introduction to the theory of estimation and principals of hypothesis testing. COREQUISITE: MAT 261.
Prerequisites:
MAT 451 is a prerequisite for this class and MAT 261 is a corequisite for this class.

MAT 453

PROBABILITY AND STATISTICS III

A continuation of MAT 452. More on hypothesis testing, most powerful, uniformly most powerful, and likelihood ratio tests. Introduction to the analysis of variance; linear regression; categorical data analysis, and nonparametric methods of inference.
Prerequisites:
MAT 452 is a prerequisite for this class.

MAT 456

APPLIED REGRESSION ANALYSIS

Simple linear, multiple, polynomial and general linear regression models. Model diagnostics; Model selection and Validation. Cross-listed with MAT 356.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 512

APPLIED TIME SERIES AND FORECASTING

Development of the Box-Jenkins methodology for the identification, estimation and fitting of ARIMA, and transfer-function stochastic models for the purpose of analyzing and forecasting stationary, non-stationary, and seasonal time series data. The course emphasizes practical time series data analysis, using computer packages and includes applications to economic, business and industrial forecasting.
Prerequisites:
MAT 341 or MAT 348 or MAT 453 is a prerequisite for this class.

MAT 528

DESIGN AND ANALYSIS OF EXPERIMENTS

Single-factor fixed, random and mixed designs with and without restrictions on randomizations, including randomized block designs, Latin & Graeco-Latin squares. Factorial and fractional factorial experiments. Nested and split-plot designs. Confounding and response surface methodology.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 526

SAMPLING THEORY AND METHODS

Simple random, stratified, systematic and cluster sampling. Multistage and area sampling. Random-response and capture-release models. Cross-listed as MAT 326.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 421

BASIC BIOSTATISTICS

This course includes both data analysis and experimental design, up to and including survival analysis such as used in the analysis of clinical trials. The course will be supplemented by standard topics with application areas relevant to drug development, including pharmacokinetics, clinical trials, bioequivalence, and pharmacoepidemiology.
Prerequisites:
MAT 453 or instructor consent is a prerequisite for this class.

MAT 422

GENE EXPRESSION ANALYSIS

In this course, students will build on the principles of MAT 421 by considering experimental design and data analysis issues pertaining to gene expression and genome-wide association studies. Introduction to gene expression studies, multiple comparisons problem in microarray studies, introduction to genome-wide association studies and experimental design for GWAS - one and two-stage approaches will also be addressed.
Prerequisites:
MAT 421 and MAT 453 are a prerequisite for this class.

MAT 423

GENOME SEQUENCING

The course provides a basic understanding of sequencer-based genetic analyses starting with the basics: what a genome sequencer is, how genome sequences are assembled, and the statistics involved in designing sequencer experiments. The course will also consider typical models of sequence evolution leading into coverage of approaches to sequence alignment and statistical phylogeny along with issues related to biomedical applications, detecting sequence variants, transcriptome and metagenome sequencing, and ChIP-Seq. RNA and protein folding will also be included.
Prerequisites:
MAT 453 or instructor consent is a prerequisite for this class.

MAT 454

MULTIVARIATE STATISTICS

The multivariate normal distribution. The general linear model. Multivariate regression and analysis of variance; discriminant analysis; principal component and factor analysis; applications and use of statistical software. Cross-listed with MAT 354.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 455

STOCHASTIC PROCESSES

Discrete Markov chains and random walks, birth and death processes, Poisson process, queuing systems, and renewal processes.Cross-listed as MAT 355.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 457

NONPARAMETRIC STATISTICS

Inference concerning location and scale parameters, goodness of fit tests, association analysis and tests of randomness using distribution free procedures. Bootstrap techniques. Smoothing methodologies. Cross-listed with MAT 357.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 458

STATISTICAL QUALITY CONTROL

History; Deming guide to quality; graphical techniques of process control; Schewhart's control charts for means, ranges, standard deviations, individual measurements, and attributes; process capabilities and statistical tolerance; cumulative-sum charts. product liability; acceptance sampling; product and process design; applications and case studies.

MAT 459

SIMULATION MODELS AND MONTE CARLO METHOD

Techniques of computer simulation of the classical univariate and multivariate probability models, and such random processes as random walks, Markov chains, and queues. Cross-listed with MAT 359.
Prerequisites:
MAT 453 is a prerequisite for this class.

MAT 460

TOPICS IN STATISTICS

One of the following topics: Clinical trials; Reliability and life testing; Categorical data analysis; Bootstrapping; Data Mining; Response Surface Methodology; Meta analysis; Survival Models.
Prerequisites:
MAT 453 or instructor consent is a prerequisite for this class.

MAT 489

QUEUING THEORY WITH APPLICATIONS

Discrete and continuous-time Markov chain models, Queuing systems, and topics from renewal and reliability theory.
Prerequisites:
MAT 453 is a prerequisite for this class.