STATISTICS

Subject abbreviation: STAT

Robert J. Beaver, Ph.D., Chair  

Department Office, 2626 Statistics-Computer Building

Professors:

Barry C. Arnold, Ph.D.

Robert J. Beaver, Ph.D.

Subir Ghosh, Ph.D.

D. V. Gokhale, Ph.D.

Keh-Shin Lii, Ph.D.

S. James Press, Ph.D.

David J. Strauss, Ph.D.

Associate Professor:

Christopher A. Robertson, Ph.D.

**

MAJOR  

The Department of Statistics is concerned with teaching, research, and statistical consulting. The courses offered present a comprehensive spectrum of statistical and probability theory, in so far as such theory is necessary for the understanding and analysis of observational data. The applications of the theory delineated in the courses may be made in any field of experience. Laboratory classes in which examples related to the student's actual field of interest are worked out, play an essential part. The department offers both B.A. and B.S. degrees in Statistics as well as a B.S. in Statistics with options in Statistical Computing and Quantitative Management; the M.S. degree in Statistics; and the Ph.D. degree in Applied Statistics.

The courses STAT 002, STAT 040, STAT 048, STAT 100A-STAT 100B, STAT 105, STAT 120A-STAT 120B, STAT 121, STAT 130, STAT 140, STAT 146, and STAT 155 are intended for students of other departments who wish a knowledge of statistical techniques. Some of them may be taken as electives by statistics majors. The objective of these courses is to acquaint the student with the elements of statistics with only the necessary amount of mathematical training.

STAT 004, STAT 147, STAT 150, STAT 157 are computer oriented courses intended for students who would like to learn about computer programming in the most important languages and who would like to learn about statistical computing.

In addition to teaching, the Department of Statistics is responsible to the Dean of the College of Natural and Agricultural Sciences and director of the Agricultural Experiment Station for collaboration with research workers in the biological and agricultural fields. A consultative service in the design, analysis, and interpretation of experimental data relating to the agricultural sciences is provided.

COMPUTING AND CONSULTING

The Department of Statistics has a strong applied orientation which involves the use of computing and the solving of real world statistical problems that arise in many disciplines. The Department has multiple person computer laboratories that include Pentium-class machines (running Windows 95 and Windows NT), a UNIX-based laboratory that includes five Sun Microsystems Ultra 10 computers, a Sun Microsystems SPARCstation 5 computer, three Sun Microsystems diskless workstations, all of which are networked with direct access to the internet. In addition, these computers provide the students, faculty, and staff access to the campus DEC-alpha computer network. The CRAY T90 Supercomputer at SDSC Center is also available to graduate students and faculty.

CAREER OPPORTUNITIES

The Department of Statistics prepares students for careers in business, government, and industry as well as for research and teaching. There is substantial demand in both the private and public sectors of our economy for those with strong training in statistics. People with bachelor's and master's degrees in statistics will typically find employment with the research departments of banks, financial and insurance institutions (actuarial activities); aerospace, electronics and other engineering organizations; pharmaceutical companies; urban planning units; marketing companies; and government agencies responsible for establishing and compiling standards for public health, safety, and quality of life. People with either the M.S. or Ph.D. in statistics may find employment in teaching as well as consulting.

DEGREE REQUIREMENTS  

UNIVERSITY REQUIREMENTS

General University requirements are Universitywide requirements which all undergraduates must satisfy. See the Undergraduate Studies section for a complete listing.

COLLEGE REQUIREMENTS

Students must fulfill all breadth requirements of the College of Natural and Agricultural Sciences. See Degree Requirements under College of Natural and Agricultural Sciences in the Undergraduate Studies section of this catalog.

Some of the following requirements for the major may also fulfill some of the College's breadth requirements. Consult with a department advisor for course planning.

MAJOR REQUIREMENTS

The Department offers both a B.A. and a B.S. degree in Statistics as well as a B.S. in Statistics with options in Statistical Computing and Quantitative Management.

The major requirements for both the Bachelor of Arts and the Bachelor of Science degrees in Statistics are as follows:

For the B.A. degree:

1. Core requirements (20 units)

• a) MATH 009A-MATH 009B-MATH 009C, MATH 010A
• b) Four (4) additional units in Mathematics

2. Upper-division requirements

• a) Thirty-six (36) units of upper-division course work
  • (1) STAT 147, STAT 150, STAT 155, STAT 170A-STAT 170B
  • (2) Sixteen (16) units chosen from STAT 127, STAT 130, STAT 140, STAT 146, STAT 157, STAT 160A-STAT 160B-STAT 160C, STAT 171

Note: An introductory Statistics class such as STAT 040, STAT 048, or STAT 100A is strongly recommended.

For the B.S. degree:

1. Core requirements are the same as for the B.A. degree

• a) MATH 009A-MATH 009B-MATH 009C, MATH 010A
• b) Four (4) additional units in Mathematics

2. Upper-division requirements (52 units)

• a) Thirty-six (36) units of upper-division course work
  • (1) STAT 147, STAT 150, STAT 155, STAT 170A-STAT 170B
  • (2) Sixteen (16) units chosen from STAT 127, STAT 130, STAT 140, STAT 146, STAT 157, STAT 160A-STAT 160B-STAT 160C, STAT 171

• b) Sixteen (16) units of additional course work chosen, with the approval of the major advisor, from Statistics courses numbered 121 and higher or from related fields.

Note: An introductory Statistics class such as STAT 040, STAT 048, or STAT 100A is strongly recommended.

For the B.S. degree with Options in Statistical Computing

The requirements for this option are in addition to the requirements for the B.S. in Statistics.

1. Lower-division requirements (8 units)

• a) CS 010, CS 012

2. Upper-division requirements (24 units)

• a) STAT 157
• b) MATH 131
• c) Sixteen (16) units of course work selected from
  • (1) CS 140A-CS 140B, CS 177
  • (2) MATH 112, MATH 120
  • (3) STAT 198-I

• d) MATH 135A-MATH 135B recommended

For the B.S. degree with Options in Quantitative Management

The requirements for this option are in addition to the requirements for the B.S. in Statistics.

1. Lower-division requirements (20 units)

• a) ECON 003
• b) CS 008
• c) BSAD 010, BSAD 020A, BSAD 020B

2. Upper-division requirements (16 units)

• a) MATH 131
• b) Three courses from one area
  • (1) Marketing--BSAD 110, BSAD 113, BSAD 117
  • (2) Finance--BSAD 134, BSAD 135A-135B, BSAD 136, BSAD 138
  • (3) Accounting--BSAD 163, BSAD 165A-165B, BSAD 168
  • (4) Management Information Systems--BSAD 170, BSAD 171, BSAD 172, BSAD 173

MINOR

The minor in Applied Statistics is designed to give students in either the social sciences or the physical sciences a cohesive set of statistics courses to deal with the data analytic aspects of their disciplines and to understand the statistical summaries that are encountered in everyday activities.

The requirements for the minor consist of at least 24 and not more than 28 upper-division units in Statistics to include the following:

1. STAT 100A-100B or STAT 120A-STAT 120B

2. Eight (8) units from STAT 127, STAT 130, STAT 140, STAT 146

3. Four (4) units from STAT 147, STAT 157

4. Four (4) additional units from (2) or (3) above

No more than 8 of the 24 units may be in courses required in the student's major.

No more than 4 units may be in courses numbered 190 through 199.

See Minors under the College of Natural and Agricultural Sciences in the Undergraduate Studies section of this catalog for additional information on minors.

GRADUATE PROGRAMS  

The department offers an M.S. degree in Statistics and a Ph.D. degree in Applied Statistics.

Domestic applicants for admission to graduate programs must supply GRE verbal and quantitative test scores before they can be admitted.

MASTER'S PROGRAM

Students entering the program must either have completed a Bachelor's degree in Statistics (or the equivalent), or take STAT 160A-STAT 160B-STAT 160C, STAT 161 and STAT 170A-STAT 170B, STAT 171, covering basic areas of probability and statistics. These courses would not be counted as credit towards the Master's degree. Students must also meet the other requirements for admission as specified by the Graduate Division. The program is Plan II (as described in the Graduate Studies section of this catalog); that is, it is taken by comprehensive examination and not by thesis. This is consistent with admission requirements of the Applied Statistics Ph.D. program. No foreign language is required.

It is expected that graduate students in Statistics take (or have already taken) appropriate courses in Mathematics to give them the proper background for graduate work in Statistics. Important areas include Calculus (at least MATH 009A-MATH 009B-MATH 009C and MATH 010A) and Linear Algebra (at least MATH 131). Students are strongly encouraged to take at least one of the following: MATH 120 (Optimization), MATH 125A-MATH 125B (Introduction to Combinatorics), MATH 135A-MATH 135B (Numerical Analysis), MATH 151A-MATH 151B-MATH 151C (Advanced Calculus), MATH 165A-MATH 165B (Complex Variables), and MATH 209A-MATH 209B-MATH 209C (Real Analysis). The specific courses selected naturally depend on the research area selected by the student.

The program shall consist of a minimum of 36 approved units. These must include STAT 281, STAT 293A-STAT 293B-STAT 293C, and 1 unit of STAT 288. In addition, at least 20 units must be from STAT 200A-STAT 200B, STAT 203A, STAT 203B, STAT 205, STAT 207A-STAT 207B, STAT 210A-STAT 210B-STAT 210C, STAT 215, STAT 216A-STAT 216B, STAT 220A-STAT 220B, STAT 230, STAT 240. Knowledge of at least one computer language and the use of statistical computer packages is required, and students lacking this background should take STAT 150 and STAT 157. Early in the program the student shall submit a program proposal, which will require the approval of the M.S. advisor. The advisor will also supervise the student's progress and course of study.

After completion of the required courses, the student will take a written comprehensive examination. This will generally be offered twice annually, in the fall and spring quarters. It is expected that some students would proceed from the M.S. degree to the Ph.D. program in Applied Statistics. Admission to the Ph.D. program normally requires preparation equivalent to the M.S. degree.

APPLIED STATISTICS DOCTORAL PROGRAM

The program for a Ph.D. in Applied Statistics emphasizes both the theory of statistics and its application to special fields of interest. In addition to courses in statistics, a student would take courses in a substantive field from which a thesis problem requiring a statistical approach should arise. The substantive field may be chosen from areas such as biology, economics, political science, psychology or administration. Specialties might include, for example, population genetics, biological control, hydrology, epidemiology, geology, discrimination in learning, or scales and measurements.

Students entering the Ph.D. program in Applied Statistics usually will have completed a Master's Degree in Statistics, Computer Science, Mathematics, or some other quantitatively based discipline. In some instance, students with M.S. degrees in other fields will be admitted to the program, but in such cases, remedial course work in Statistics, Computer Science, or Mathematics will probably be required. Students also have to meet the general requirements listed in the Graduate Studies section of this catalog.

Courses to be taken will be in statistics and the substantive field appropriate to the student's interest. Students without the courses prescribed by the M.S. in statistics or their equivalent will be required to take them as soon as possible. Students in the Ph.D. program in Applied Statistics are required to complete course work in statistics greater in depth than that required for the M.S. Knowledge of at least one computer language and the use of statistical computer packages is required, and students lacking this background should take STAT 150 and STAT 157. They are required to select 4 or more additional quarter courses in Statistics at the 200 level, not to be graded "Satisfactory/No Credit." These additional courses should be selected in consultation with the graduate advisor and/or the student's major professor in order to strengthen a student's background in statistics and to prepare the student for thesis work and a career in research and teaching. To be approved, a program must include STAT 210A-STAT 210B-STAT 210C and three of the following five courses: STAT 200A, STAT 203A, STAT 215, STAT 216A, STAT 220A. In preparing for the written qualifying examinations, a student is permitted to register for up to 6 units of STAT 291 (Individual Studies in Coordinated Areas) only during quarters that the student actually participates in qualifying examinations. In addition, students in the Ph.D. program are required to complete a minimum of 12 units (or equivalent) in a substantive field with a minimum grade point average of 3.00. The requirement may be waived if the student already has the background in the substantive area. No foreign language is required for the Ph.D. in Applied Statistics.

Before advancement to candidacy, students must demonstrate proficiency on a qualifying examination which is normally taken after two years of course work and seminars. The dissertation will be pertinent to a problem area specified by the candidate's substantive field and will be submitted in accordance with the requirements of the Graduate Division, Riverside. Teaching practice will also be required. All students in the program will, for at least 3 quarters, assist with laboratory (practice) sections of undergraduate Statistics courses or individual tutorial (consultative) work with undergraduate students.

The normative time to the Ph.D. degree is 15 quarters.

LOWER-DIVISION COURSES  

STAT 002.
Elements of Statistics and Programming. (4)

Lecture, three hours; laboratory, three hours. Prerequisite(s): none. Types of data and aggregates. Representation of data, measurements and their errors. Measures of location and dispersion. Populations and samples. Elementary descriptive statistics; their use and how to program for them using Fortran. The idea of probability as a basis for statistics.

STAT 004.
Interactive Programming in BASIC. (4)

Lecture, three hours; laboratory, three hours. Prerequisite(s): MATH 005 or equivalent. Introduction to principles of computing; use of remote terminals. Introduction to the language BASIC as an interactive programming language for main frame and personal computers. Students will write programs to be entered and debugged at remote terminals and on personal computers, by using structured style.

STAT 020.
Statistics for the Life Sciences. (2)

Lecture, two hours. Prerequisite(s): MATH 005. Descriptive statistics, samples and populations, random sampling. Probability, independence. Binomial distribution, normal distributions, sampling distributions. Confidence intervals for means. Hypothesis testing for population means, p-values. Categorical data, chi-square goodness-of-fit tests, contingency tables, independence.

STAT 040.
Elements of Statistics. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): none. An introduction to statistics. Adopts the modern Bayesian approach that advocates that estimates, hypothesis tests, and decisions be made from information developed from a formal combination of current and earlier data. Topics include summarizing and displaying data, designing experiments, probability, Bayes' rule, inferences from proportions and normal populations, sampling, and regression analysis. MINITAB is used. Credit is awarded for only one of STAT 040, STAT 048, STAT 100A, or ECON 101/STAT 101.

STAT 048.
Statistics for Business. (5)

Lecture, three hours; discussion, one hour; laboratory, three hours. Prerequisite(s): MATH 005 and CS 008 or equivalent. Descriptive statistics, probability, discrete and continuous distributions, Bayes' theorem, random variables, estimation and confidence intervals, hypothesis of testing, analysis of variance, and simple linear regression. Credit is awarded for only one of STAT 040, STAT 048, STAT 100A, or ECON 101/STAT 101.

UPPER-DIVISION COURSES

STAT 100A-STAT 100B.
Introduction to Statistics. (5-5)

Lecture, three hours; discussion, one hour; laboratory, three hours. Prerequisite(s): MATH 005 or equivalent. (A) Histograms. Descriptive statistics. Probability. Normal, binomial and Poisson distributions. Sampling distributions. Hypothesis testing. Confidence intervals. (B) Linear regression; correlation. Analysis of variance. Nonparametric methods. Simple experimental designs. Discussion and laboratory sections: (1) Administration/Economics; (2) Social Sciences/Humanities; (3) Life Sciences; (4) Physical Sciences. Credit is awarded for only one of STAT 040, STAT 048, STAT 100A, or ECON 101/STAT 101 and only one of ECON 107 or STAT 100B.

STAT 101.
Statistics for Economics. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 005. An introduction to the basic statistical methods for economics, including economic data analysis, index numbers, univariate and bivariate probability distributions, correlation and regression, sampling distributions, properties of estimators, and hypothesis testing. Only one of STAT 040, STAT 048, STAT 100A, or ECON 101/STAT 101 may be taken for credit.

STAT 105.
Statistics for Biomedical Sciences. (2)

Lecture, two hours. Prerequisite(s): MATH 009A-MATH 009B and either upper-division standing in Biomedical Sciences or consent of instructor. Descriptive statistics; probability and distributions; statistical inference, including estimation and testing of hypotheses; nonparametric methods; analysis of categorical data; regression; and correlation.

STAT 120A-STAT 120B.
Experimental Techniques for Biologists. (4-4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 005 and upper-division standing. (A) Descriptive statistics. One and two sample tests. One-way analysis of variance. Multiple comparisons. Simple linear regression and correlation. (B) Multi-way analysis of variance. Factorial experiments. Transformations. Multiple linear and polynomial regression. Analysis of covariance.

STAT 121.
Introduction to Management Science. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 023, CS 008 or their equivalent; upper-division standing. Survey of deterministic and probabilistic models for decision making: linear programming and extensions, networks, dynamic programming, decision trees, queuing models, and simulation. Uses of these models in decision making are discussed. Use of the computer is emphasized. Cross-listed with BSAD 121.

STAT 127.
Introduction to Quality Improvements. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 048 or consent of instructor. Deming's 14 points for management, graphical methods, fishbone diagram, Pareto analysis, control charts for attributes and variables, cusum and moving average charts, process-capability, economic design, acceptance sampling, Taguchi method, parameter design, tolerance design, reliability, hazard rate, censoring, accelerated life testing. Cross-listed with BSAD 127.

STAT 130.
Sampling Surveys. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 100A-STAT 100B, or equivalent. Simple random sampling. Stratified sampling. Cluster sampling. Ratio and regression estimates. Random response, capture-recapture and jack-knife techniques.

STAT 140.
Nonparametric Techniques. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 100A or equivalent. Randomization tests. Rank tests. Methods of association. Distribution free tests.

STAT 146.
Statistical Forecasting Techniques. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 100A or consent of instructor. Exponential smoothing. Regression analysis (simple and multiple). Time series. Trend analysis, seasonal analysis.

STAT 147.
Introduction to Statistical Computing. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 040 or equivalent. Introduction to computer-assisted data analysis and statistical inference using both the MINITAB and SAS packages. Topics include input, output, and editing of data; graphical procedures; descriptive statistics; cross-tabulation; inferential statistical techniques including estimation and testing; regression; and analysis of variance.

STAT 150.
Basic Computer Methodology: FORTRAN Programming. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 005 or equivalent. Students write FORTRAN programs (using the FORTRAN 90 compiler) to implement data manipulation, statistical computations, printing of tables and reports, statistical simulation, bootstrapping, and other computationally intensive statistics and procedures. Programming topics include implementation of algorithms; file transfer and manipulation; programming structures and substructures; input/output and arithmetical and logical operations; subroutines; one-dimensional and two-dimensional arrays; matrix manipulations and algebraic structures. Credit is awarded for only one of CS 008 or STAT 150.

STAT 155.
Probability and Statistics for Science and Engineering. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 009C. Sample spaces and probability. Random variables and probability distributions. Selected topics in multivariate distributions. Introduction to stochastic processes. Elements of statistical inference; testing and estimation.

STAT 157.
Statistical Computer Packages. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 100A-STAT 100B or equivalents, STAT 147; or consent of instructor. A study of major statistical packages, including SAS and BMPD with the emphasis on advanced SAS programming. Topics include advanced graphical procedures, linear models (regression and analysis of variance), multivariate techniques, and SAS macros.

STAT 160A-STAT 160B-STAT 160C.
Elements of Probability and Statistical Theory. (4-4-4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MATH 009A, MATH 009B, MATH 009C; may be taken concurrently. Statistical regularity, probability spaces. Fundamental theorems in discrete probability. Bayes' theorem. Random variables; densities and distribution functions. Continuous distributions. Transformations of random variables. Central limit theorem. Distributions of sample statistics. Statistical inference; estimation, hypothesis testing. Chi-square tests. Sequential inference. Decision theory. Nonparametric methods. Correlation. Only one of STAT 160A-STAT 160B-STAT 160C or MATH 149A-MATH 149B-MATH 149C may be taken for credit.

STAT 161.
Introduction to Probability Models. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A and STAT 160B, or equivalent. Compound distributions. Branching processes. Random walk. Continuous time models; poisson process, queuing models. The Markov property. Introduction to Markov chains. Simple time series models.

STAT 170A.
Regression Analysis. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C or equivalent. Simple and multiple linear regression: scatter-plots, point and interval estimation, prediction, testing, and calibration. Interpretation and practical applications of multiple regression. Simple, partial, and multiple correlation. Causality. Variable selection methods. Diagnostic procedures. Regression for longitudinal data.

STAT 170B.
Design of Experiments. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 170A. Principles of design and experiments. Completely randomized designs and analysis of variance. Complete block designs and two-way analysis of variance. Multiple comparisons. Complete factorial experiments. Fixed, random, and mixed models. Split-plot designs. Confounding and fractional factorial experiments.

STAT 171.
General Statistical Models. (4)

Lecture, three hours, discussion, one hour. Prerequisite(s): STAT 170B. Generalized linear models and least squares. Analysis of covariance, nonlinear regression, nonlinear least squares. Regression methods for discrete data: loglinear models, logistic regression, discriminant analysis. Regression methods for life data. Cox survival model, Kaplan-Meier estimation, Mantel-Haenszel test.

STAT 190.
Special Studies. (1-5)

Hours to be arranged. To be taken with the consent of the chair of the department as a means of meeting special curricular problems. Course is repeatable to a maximum of 10 units.

STAT 198-I.
Internship in Statistics. (1-12)

Prerequisite(s): STAT 100A-STAT 100B (or STAT 120A-STAT 120B), STAT 150, consent of instructor, upper-division standing. An internship to provide the student with statistical field experience in governmental, industrial, or research units. Each individual project must be approved by the Statistics Department and the head of the unit in which the internship is to be carried out. A written report is required. May be repeated for a total of 16 units but not more than 12 can count toward graduation. Graded Satisfactory (S) or No Credit (NC).

STAT 199H.
Senior Honors Research. (1-5)

Prerequisite(s): senior standing with major concentration is statistics and with consent of instructor. Course is repeatable to a maximum of 10 units.

GRADUATE COURSES  

STAT 200A-STAT 200B.
Advanced Design and Analysis of Experiments. (4-4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 170A-STAT 170B-STAT 170C, or equivalent. Random and mixed models. Nested classifications. Factorial experiments. Split plot and block designs. Confounding and fractional replication in 2n and 3n series. Response surfaces; method of steepest ascent. Second order designs; orthogonal/rotatable/central composite designs/canonical forms. Balanced incomplete block designs. Intra- and inter-block analyses. Covariance.

STAT 203A.
Bayesian Statistics I. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160C or equivalent. Subjective probability, Renyi axiom system, Savage axioms, coherence, Bayes theorem, credibility intervals, Lindley paradox, empirical Bayes estimation, natural conjugate priors, de Finetti's theorem, approximation methods. Bayesian bootstrap, Bayesian computer programs.

STAT 203B.
Bayesian Statistics II. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 203A. Assessing priors, nonparametric density estimation for expert group judgements, Bayesian regression, Bayesian analysis of variance, Bayesian regression with correlated disturbances and heteroskedasticity, Bayesian inference in time series models, Bayesian classification, Bayesian inference in spatial statistics, Bayesian factor analysis, disputed authorship.

STAT 205.
Discrete Data Analysis. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C or equivalent, or consent of instructor. Contingency tables, log-linear models, information theory models, maximum likelihood estimation, goodness of fit, measures of association, computational procedures.

STAT 207A-STAT 207B.
Statistical Computing. (4-4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C, STAT 170A-STAT 170B-STAT 170C. Computational aspects of least squares inlinear statistical models, optimization in nonlinear statistical models, consistency, eigenvector-eigenvalue computations in multivariate statistical analysis, error analysis, simulations and Monte Carlo methods for problems in statistical inference, pseudorandom numbers, variance reduction, fast Fourier transform in time series analysis, and numerical approximations.

STAT 210A-STAT 210B-STAT 210C.
Theoretical Statistics and Probability. (4-4-4)

Lecture, three hours. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C, MATH 010B or equivalent. Conditional probability, independence. Distribution functions. Characteristic functions. Convergence concepts. Limit theorems. Estimation and hypothesis testing. Order statistics. Decision theory. Bayes and empirical Bayes rules. Efficiency. Sequential inference. Distribution free and robust techniques.

STAT 215.
Stochastic Processes. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C; STAT 161. The Markov property; Markov chains; Markov processes and Poisson processes. Birth and death models. Queues. Random walks. Renewal processes. Wiener processes and diffusion.

STAT 216A-STAT 216B.
Time Series Analysis. (4-4)

Lecture, three hours; discussion, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C, STAT 161. Stationary processes, Autoregressive--Moving Average (ARIM) processes, trend, seasonality, model building, estimation and forecasting, spectral analysis and estimation, Kalman filtering and prediction, higher-order spectral analysis, nonlinear and non-Gaussian time series.

STAT 220A-STAT 220B.
Multivariate Analysis. (4-4)

Lecture, three hours. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C or equivalent plus familiarity with matrix algebra. Matrix algebra, multivariate distributions (normal, Wishart, Hotelling's T2, Dirichlet, etc.). Simultaneous equation systems, multivariate and categorical regression, principal components, classification, multi dimensional scaling. Both theoretical foundations and models and applications will be discussed.

STAT 230.
Sampling Theory. (4)

Lecture, three hours. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C. Basic theory of stratified, ratio and regression methods of estimation; cluster and double sampling. Concept of sufficiency and its applications from finite populations. Nonsampling errors. Estimation of response bias and of optimum number of interviewers. Sampling inspection.

STAT 232.
Statistical Methods for Management. (4)

Lecture, three hours; discussion, one hour. Prerequisite(s): MGT 403A or equivalent; familiarity with Microsoft's Excel spreadsheet software. Teaches how to generate decision-making information from data and solve management problems using common computer tools. Covers problem identification and formulation, model selection and use, and interpretation of the results of statistical analysis. hypothesis testing, analysis of variance, simple and multiple regression, time series and forecasting. May not be taken for degree credit by students in Statistics undergraduate or graduate programs. Cross-listed with MGT 201.

STAT 240.
Nonparametric Methods. (4)

Lecture, three hours; consultation, one hour. Prerequisite(s): STAT 160A-STAT 160B-STAT 160C. Theory of distribution-free statistics, ranking statistics, rank correlation, U-statistics. Nonparametric point and interval estimation. Empirical distribution function methods. Combinatorial problems; runs, matching, occupancy; limiting distributions.

STAT 251.
Statistics Colloquium. (1)

Seminar, one and one-half hours. Presentation of current research in statistics by faculty, advanced graduate students and guest lecturers. Grades will be Satisfactory (S) or No Credit (NC).

STAT 252.
Spatial Statistics. (3)

Seminar, three hours. Prerequisite(s): graduate standing. "Classical" analysis on point patterns, quadrat and lattice data; simple models for nonrandomness. Interactive (Markov) systems; Markov Random Fields, Gibbs Ensembles, Hammersley-Clifford theorem. Metropolis method. Data analysis techniques. Smoothing and interpolation. Spatial auto-regression. Graded Satisfactory (S) or No Credit (NC).

STAT 255 (E-Z).
Seminar on Topics in Advanced Statistics. (3-4)

Seminar, three hours; discussion, one hour. Prerequisite(s): graduate standing. Additional prerequisites are required for some segments of this course; see Department. Discussions and lectures by graduate students and faculty on topics related to student and faculty research. In some courses students will receive letter grades only. In others students may receive either a letter grade or Satisfactory (S) or No Credit (NC) grade; no petition is required, but students must see instructor for grading basis. The department will maintain a listing of all 255 segments and their unit value and grading basis.

STAT 281.
Practical Problems in Statistics. (2)

Seminar, two hours. Prerequisite(s): consent of instructor. A variety of practical statistical problems will be presented and discussed. To be graded Satisfactory (S) or No Credit (NC).

STAT 288.
Literature Seminar. (1)

Seminar, one hour. Students will make oral presentations summarizing important research papers in the statistics literature. All graduate students are encouraged to participate. Topics may vary each term. To be graded Satisfactory (S) or No Credit (NC).

STAT 290.
Directed Studies. (1-6)

Prerequisite(s): graduate standing and consent of instructor. Individual studies on specially selected topics in statistical applications. To be graded Satisfactory (S) or No Credit (NC). Course is repeatable.

STAT 291.
Individual Studies in Coordinated Areas. (1-6)

Consultation, one to six hours. Prerequisite(s): graduate standing. A program of studies designed to assist candidates who are preparing for examinations. Open to M.S. and Ph.D. students; does not count toward the unit requirement for the M.S. degree. To be graded Satisfactory (S) or No Credit (NC). May be repeated for credit.

STAT 292.
Concurrent Analytical Studies. (1-4)

Research, 3 to 12 hours. Prerequisite(s): consent of instructor and concurrent enrollment in 100-series course. To be taken on an individual basis. Student will complete a graduate paper related to the 100-series course. To be graded Satisfactory (S) or No Credit (NC). May be repeated for credit.

STAT 293A-STAT 293B-STAT 293C.
Statistical Consulting and Data Analysis. (2-4)

Consultation, two to four hours. Prerequisite(s): STAT 160C, STAT 171 and consent of instructor. Students will consult with clients from a variety of disciplines, analyze real data, and write reports. Other areas important to the applied statistician will be covered, including how to write a technical report and how to prepare and present a technical talk. Graded Satisfactory (S) or No Credit (NC).

STAT 297.
Directed Research. (1-6)

Prerequisite(s): graduate standing and consent of instructor. Directed research in applications of statistics in biological studies, including computer simulation. To be graded Satisfactory (S) or No Credit (NC).

STAT 299.
Research for Thesis or Dissertation. (1-12)

Prerequisite(s): graduate standing and consent of instructor. To be graded Satisfactory (S) or No Credit (NC). Course is repeatable.

PROFESSIONAL COURSES

STAT 302.
College Teaching Practicum. (1-4)

Practicum, three to twelve hours. Prerequisite(s): graduate standing and consent of instructor. Required of all teaching assistants in the department. Credit not applicable to graduate unit requirements. Supervised teaching in college level classes under the supervision of the course instructor. Course will be graded Satisfactory (S) or No Credit (NC). Course is repeatable.