Graduate Courses
20142015 Course Resources
Graduate Courses
 200AB  Advanced Design and Analysis of Experiments
 201ABC  Theory of Probability and Statistics
 203AB  Bayesian Statistics I & II
 205  Discrete Data Analysis
 207  Statistical Computing
 209AB  Statistical Data Mining
 210AB  Advanced Theory of Probability and Statistics
 215  Stochastic Processes
 216AB  Time Series Analysis
 220AB  Multivariate Analysis
 230  Sampling Theory
 231AB  Statistics for Biological Sciences
 240  Nonparametric Methods
 251  Statistics Colloquium
 255 (EZ)  Seminar on Topics in Advanced Statistics
 288  Literature Seminar
 290  Directed Studies
 291  Individual Studies in Coordinated Areas
 292  Concurrent Analytical Studies
 293  Methods in Applied Statistics
 297  Directed Research
 299  Research for Thesis or Dissertation
 302  College Teaching Practicum

Statistics 200AB  Advanced Design and Analysis of Experiments
4 Units
(A) Topics include fixed, mixed, and random effects models; complete and incomplete block designs; rowcolumn designs; nested designs; split plot designs; crossover designs, analysis of covariance; repeated measure designs; and optimality of designs.
Lecture, three hours; discussion, one hour
Prerequisite(s): Statistics 170A170B, 171 or equivalent
(B) Topics include factorial experiments confounding and fractional factorial experiments for symmetrical and asymetrical factorial experiments; orthogonal and balanced arrays; optimal fractional factorial designs; first and second order response surface designs; rotatibility; and blocking of response surface designs; method of steepest ascent; canonical representation; and minimum bias, variance, and mean square error designs. 
Statistics 201ABC  Theory of Probability and Statistics
4 Units
Lecture, three hours; discussion one hour
Prerequisite(s): Math 010B; Statistics 160C or equivalent; graduate standing; or consent of instructor.(A) Topics include probability and conditional probability; random variables: univariate and multivariate; distributions; independence; moments; generating functions; transformations, and standard distributions. Also addresses multivariate normal distribution; order statistics; inequalities; convergence concepts; law of large numbers, and the central limit theorem. Credit is not awarded for STAT201A, if it has already been awarded to STAT210A if STAT210A was taken prior to Fall 2013.
(B) Topics include exponential families; delta method; inference concepts; sufficiency; point estimation; unbiasedness; completeness; and consistency. Also explores relative efficiency; maximum likelihood; method of moments; interval estimation; pivotals; and approximate intervals and regions. Credit is not awarded for STAT201B, if it has already been awarded to STAT210B if STAT210B was taken prior to Fall 2013.
(C) Topics include Bayesian estimation; prior selection; loss functions;
admissibility; hypothesis testing; NeymanPearson lemma; size; power; UMP tests; likelihood ratio tests; sequential tests; nonparametric tests; and bootstrap. Credit is not awarded for STAT201C, if it has already been awarded to STAT210C if STAT210C was taken prior to Fall 2013.

Statistics 203AB Bayesian Statistics I and II
4 Units
(A) 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.
Lecture, three hours; discussion one hour
Prerequisite(s): Statistics 160C or equivalent
(B) Assessing priors, nonparametric density estimation for expert group judgements, Bayesian regression, Bayesian analysis of variance, Bayesian regression with correlated disturbances and heteroscedasticity, Bayesian inference in time series models, Bayesian classification, Bayesian inference in spatial statistics, Bayesian factor analysis, disputed authorship. 
Statistics 205  Discrete Data Analysis
4 Units
Contingency tables, loglinear models, information theory models, maximum likelihood estimation, goodness of fit, measures of association, computational procedures.
Lecture, three hour; discussion, one hour
Prerequisite(s): Statistics 160ABC or equivalent, or consent of instructor 
Statistics 207  Statistical Computing
4 Units
Topics include computational aspects of optimization; numerical integration; Monte Carlo methods; resampling methods; expectation maximization (EM) algorithm; Markov chain and Monte Carlo methods; nonparametric estimation; and other current computational methods.
Lecture, three hours; discussion, one hour.
Prerequisite(s): Statistics 201A, or consent of instructor. 
Statistics 209AB  Statistical Data Mining
4 Units
Introduces principal datamining methodologies, major software tools, and typical applications for structuring, understanding, and using large datasets effectively and efficiently.
Lecture, three hours; discussion, one hour.
Prerequisite(s): STAT 160A, STAT 160B, STAT170; or consent of the instructor. 
Statistics 210AB  Advanced Theory of Probability and Statistics
4 Units
(A) Topics include measure spaces; measure integration; convergence
Lecture, three hours; discussion, one hour.
Prerequisite(s):graduate standing; STAT 201C or equivalent; or consent of instructor.
theorems; absolute continuity; product spaces; Tonellifubini theorems; convolutions and transforms; probability spaces; and existence and extension theorems. Also covers inequalities; independence; conditional probability and expectation; convergence concepts; laws of large numbers; weak convergence; and central limit theorem. Credit is not awarded for STAT 201A if it has already been awarded to STAT 210A if STAT 210A was taken prior to Fall 2013.
(B) Topics include estimation, decision theory, Bayes and empirical Bayes rules, and efficiency. Prerequisite: STAT210A. Credit is not awarded for STAT 201B if it has already been awarded to STAT 210B if STAT 210B was taken prior to Fall 2013. 
Statistics 215  Stochastic Processes
4 Units
The Markov property; Markov chains; Markov processes and Poisson processes. Birth and death models. Queues. Random walks. Renewal processes. Wiener processes and diffusion.
Lecture, three hours; discussion, one hour.
Prerequisite(s): Statistics 160ABC; Statistics 161. 
Statistics 216AB  Time Series Analysis
4 Units
(A) Topics include stationary processes, autoregressivemoving average (ARIMA) processes, trend, seasonality, model building, estimation and forecasting, and spectral analysis and estimation.
Lecture, three hours; discussion, one hour.
Prerequisite(s): Statistics 160A160B160C, 161
(B) Topics include spectral analysis and estimation, higherorder spectral analysis, Kalman filtering and prediction, and nonlinear, nonstationary,
and nonGaussian time series. Prerequisite: STAT216A or consent of instructor. 
Statistics 220AB Multivariate Analysis
4 Units
(A) Topics include algebra and calculus of vectors and matrices, special multivariate distributions (Normal, Wishart, Hotelling's Tsquared, multivariate T, multivariate lognormal, etc).
Lecture, three hours.
Prerequisite(s): Statistics 160A160B160C or equivalent plus familiarity with matrix algebra.
(B) Topics include categorical dependent variable regression, loglinear models, inference in the multivariate normal distribution, multivariate multiple regression, hypothesis testing, likelihood ratio tests, multivariate analysis of variance and covariance, principal components analysis, factor analysis, and classification and discrimination models. Prerequisite: STAT 220A or consent of instructor. 
Statistics 230  Sampling Theory
4 Units
Covers the theory of stratified, ratio, and regression methods of estimation and cluster and double sampling. Includes the concept of sufficiency and its applications from finite populations, nonsampling errors, estimation of response bias and of optimum number of interviewers, and sampling inspection.
Lecture, three hours.
Prerequisite(s): Statistics 160C. 
Statistics 231AB  Statistics for Biological Sciences
4 Units
(A) Covers one and twosample tests, one and twoway analysis of variance, multiple comparisons, simple and multiple linear regression, nonparametric statistics, and categorical data.
Lecture, three hours; discussion, one hour.
Prerequisite(s): Math 023, 100B or equivalent, or consent of the instructor.
(B) Covers logistic regression, analysis of covariance, advanced experimental designs, randomization, bootstrapping, jackknifing, and other procedures. Prerequisites: MATH 031, or equivalent; STAT 231A or consent of instructor; graduate standing in Biochemistry and Molecular Biology; Biomedical Sciences; Botany; Cell, Molecular, and Developmental Biology; Entomology; Environmental Toxicology; Genetics, Genomics, & Bioinfomatics; Evolution, Ecology, and Organismal Biology; Microbiology; Nematology; Neuroscience; Plant Biology; Plant Genetics; Plant Pathology; Plant Science. 
Statistics 240  Nonparametric Methods
4 Units
Theory of distributionfree statistics, ranking statistics, rank correlation, Ustatistics. Nonparametric point and interval estimation. Empirical distribution function methods. Combinatorial problems; runs, matching, occupancy; limiting distributions.
Lecture, three hours; consultation, one hour.
Prerequisite(s): Statistics 160ABC. 
Statistics 251  Statistics Colloquium
1 Unit
Presentation of current research in statistics by faculty, advanced graduate students and guest lecturers. Grades will be Satisfactory (S) or No Credit (NC).
Seminar, one and onehalf hours. 
Statistics 255 (EZ)  Seminar on Topics in Advanced Statistics
34 Units
Discussions and lecturers 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.
Seminar, three hours; discussion, one hour.
Prerequisite(s): graduate standing. 
Statistics 288  Literature Seminar
1 Unit
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).
Seminar, one hour. 
Statistics 290  Directed Studies
16 Units
Individual studies on specially selected topics in statistical applications. To be graded Satisfactory (S) or No Credit (NC). Course is repeatable.
Hours to be arranged
Prerequisite(s): graduate standing and consent of instructor. 
Statistics 291  Individual Studies in Coordinated Areas
16 Units
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.
Consultation, one to six hours.
Prerequisite(s): graduate standing. 
Statistics 292  Concurrent Analytical Studies
14 Units
To be taken on an individual basis. Student will complete a graduate paper related to the 100series course. To be graded Satisfactory (S) or No Credit (NC). May be repeated for credit.
Research, 312 hours.
Prerequisite(s): consent of instructor and concurrent enrollment in 100series course. 
Statistics 293  Methods in Applied Statistics
4 Units
Covers statistical consulting and analysis of client data, the clientconsultant meeting, negotiations, communications, interactions, and skills that facilitate the process of selflearning. Involves client visitations and field trips. Students present written and oral reports and technical talks. Statistics graduate students receive a letter grade; other student receive a letter grade or S/NC. Course is repeatable to a maximum of 12 units.
Lecture, 3 hours; discussion, 1 hour.
Prerequisite(s): Statistics 160C, 170B, 171 or consent of instructor. 
Statistics 297  Directed Research
16 Units
Directed research in applications of statistics in biological studies, including computer simulation. To be graded Satisfactory (S) or No Credit (NC).
Prerequisite(s): graduate standing and consent of instructor. 
Statistics 299  Research for Thesis or Dissertation
112 Units
To be graded Satisfactory (S) or No Credit (NC). Course is repeatable.
Prerequisite(s): graduate standing and consent of instructor. 
Statistics 302  College Teaching Practicum
14 Units
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).
Practicum, three to twelve hours
Prerequisite(s): graduate standing and consent of instructor.