UCSB 2009-2010 Catalog Course Search
Search by subject area and course number. Refer to this list of subject areas and their corresponding department.
Tip: A search for the subject area, for example, querying just "HIST" (without quotes), will return all courses of the queried subject area. Searching using subject area and number, such as "HIST 17" (without quotes), would return all courses in the series; in this example that would include HIST 17A, 17AH, 17B, etc.
| Search results: |
| PSTAT 5A - Statistics |
| (5) STAFF |
| high school algebra. |
| Random variables, sampling distribution, estimation hypothesis testing,
correlation and regression, other topics from statistics. Computing labs
required. |
| PSTAT 5A - Statistics |
| (5) STAFF |
| high school algebra. |
| Random variables, sampling distribution, estimation hypothesis testing,
correlation and regression, other topics from statistics. Computing labs
required. |
| PSTAT 5E - Statistics With Economics And Business Applications |
| (5) STAFF |
| high school algebra. |
| An introduction to statistical methods applied to the analysis of economic data.
Topics include basic probability, statistical inference and hypothesis testing, and
regression. Computing labs with Excel. |
| PSTAT 5E - Statistics With Economics And Business Applications |
| (5) STAFF |
| high school algebra. |
| An introduction to statistical methods applied to the analysis of economic data.
Topics include basic probability, statistical inference and hypothesis testing, and
regression. Computing labs with Excel. |
| PSTAT 5LS - Statistics |
| (5) STAFF |
| High school algebra. |
| An introduction to statistics for students interested in the quantitative analysis
of problems in the life sciences. The focus is on application; topics include
probability, correlation and regression, sampling distributions, confidence
intervals, and hypothesis testing.
|
| PSTAT 5LS - Statistics |
| (5) STAFF |
| High school algebra. |
| An introduction to statistics for students interested in the quantitative analysis
of problems in the life sciences. The focus is on application; topics include
probability, correlation and regression, sampling distributions, confidence
intervals, and hypothesis testing.
|
| PSTAT 6H - Introductory Statistics (Honors Section) |
| (1) STAFF |
| Prerequisites: Consent of instructor. |
| Should be enrolled in PSTAT 5AA-ZZ course with a minimum GPA of 3.5 on a minimum of 16 graded baccalaureate units. |
| Descriptive methods, histograms, measures of central tendency and spread, probability, random variables, mean and variance, binomial and normal distributions, estimating with random confidence, tests of significance, inference for means and proportions, regression and confidence tests of significance. |
| PSTAT 105 - Introduction to Nonparametric Methods |
| (4) STAFF |
| Prerequisites: PSTAT 120A and 120B or equivalent. |
| Statistical methods for model-free data analysis, including use of ranks in
comparing means and assessing correlation, computer-based permutation and
bootstrap calculations for significance tests and confidence intervals, estimation
of lifetime survival curves. Emphasis on scientific applications. |
| PSTAT 120A - Probability and Statistics |
| (4) STAFF |
| Prerequisites: Mathematics 3A-B-C. |
| Concepts of probability; random variables; combinatorial probability; discrete and continuous distributions; joint distributions, expected values; moment generating functions; law of large numbers and central limittheorems. |
| PSTAT 120B - Probability and Statistics |
| (4) STAFF |
| Prerequisites: PSTAT 120A with a grade of C or better. |
| Distribution of sample mean and sample variance; t, x2 and F distributions;summarizing data by statistics and graphs; estimation theory for single samples: sufficiency, efficiency, consistency, method of moments, maximum likelihood; hypothesis testing: likelihood ratio, goodness of fit tests; confidence intervals. |
| PSTAT 120C - Probability and Statistics |
| (4) STAFF |
| Prerequisites: PSTAT 120B with a grade C or better. |
| Hypothesis tests for means of independent samples and paired data; likelihood
ratio tests; nonparametric hypothesis tests: sign, rank, and Mann-Whitney tests;
chi-squared goodness-of-fit tests and contingency tables; Bayesian methods of
estimating parameters and credible intervals. |
| PSTAT 122 - Design and Analysis of Experiments |
| (4) STAFF |
| Prerequisites: PSTAT 120B. |
| Linear models; least squares theory; one way and two-way analysis of variance; multiple comparison procedures; fixed, random, and mixed effects models; basic designs including completely randomized design, randomized blocks design, imcomplete block designs, Latin squares, factorial and fractional factorial designs; analysis of covariance. |
| PSTAT 123 - Sampling Techniques |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| An elementary development of the statistical methods used to design and
analyze sample surveys. Basic ideas: estimates, bias, variance, sampling and
nonsampling errors; simple random sampling with and without replacement;
ratio and regression estimates; stratified sampling; systematic sampling;
cluster sampling; sampling with unequal probabilities, multistage sampling.
Examples from various fields will be discussed to illustrate the concepts
including sampling of biological populations, opinion polls, etc. |
| PSTAT 126 - Regression Analysis |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Linear and multiple regression, analysis of residuals, variable and model selection including stepwise regression, and analysis of covariance. Other topics may include logistic regression, probit analysis, nonlinear regression and nonparametric regression, and correlation methods. |
| PSTAT 130 - SAS Base Programming |
| (4) STAFF |
| Prerequisites: One upper division course in PSTAT, MATH, Computer Science or ECE. |
| Computer Science 10 or equivalent programming class. |
| In depth SAS programming course. Topics include importing/exporting raw data
files, manipulating/transforming data, combining SAS data sets, generating
reports, handling syntax and logic errors. Provides preparation for the SAS
Institute Certified Professional (Base Programming) Examination. |
| PSTAT 131 - Data Mining |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B, 126, 130. |
| Introduction to data mining techniques. Model assessment and performance
evaluation. Data preparation. Programming techniques for transforming raw
data into a form suitable for predictive modeling. Extracting data to a form
that predictive models can utilize. Incorporating non numeric data in
predictive models. Techniques for managing exceptional and extreme data.
Building predictive models using SAS Enterprise Miner 5 in SAS 9, including
decision trees and neural networks. |
| PSTAT 132C - Introduction to Operations Research |
| (4) STAFF |
| Prerequisites: Mathematics 132A-B; and PSTAT 120A or Economics 141A. |
| Review of probability; queuing theory; waiting models; birth and death processes; applications; inventive theory; Markov chains, applications, anddecision models; computer simulation; component and system reliability; decision analysis; list and area searching. |
| PSTAT 133A - Introduction to Statistical Methods |
| (4) STAFF |
| Basics of probability and statistics; minimum use of calculus; use of statistical packages. topics: probability, random variables, expectation, variance, binomial, normal, and other distributions. statistical inference,estimation and confidence intervals, testing, simple and multivariate regression, analysis of variance, non-parametric inference. |
| PSTAT 133B - Introduction to Statistical Methods |
| (4) STAFF |
| Prerequisites: Not open to mathematics and statistics majors. |
| Basics of probability and statistics; minimum use of calculus; use of statistical packages. Topics: probability, random variables, expectation, variance, binomial, normal, and other distributions. Statistical inference,estimation and confidence intervals, testing, simple and multivariate regression, analysis of variance, non-parametic inference. |
| PSTAT 140 - Statistical Process Control |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Topics include, statistical quality control charts for mean, standard deviation,
range, fraction defective, and number of defects; sampling by attributes and
variables; acceptance sampling, choice of acceptable quality level, average
outgoing quality limit and lot tolerance percent defective values. |
| PSTAT 160A - Applied Stochastic Processes |
| (4) STAFF |
| Prerequisites: Mathematics 5A and 8; PSTAT 120A with a minimum grade of C. |
| Random walks, Markov chains, Poisson processes, Markov processes; second order processes, Wiener process stochastic differential equations, optimal prediction, spectral distributions; queueing theory, simulation and applications to mathematical finance. |
| PSTAT 160B - Applied Stochastic Processes |
| (4) STAFF |
| Prerequisites: Mathematics 5A and 8; PSTAT 120A with a minimum grade of C. |
| Random walks, Markov chains, Poisson processes, Markov processes; second order processes, Wiener process stochastic differential equations, optimal prediction, spectral distributions; queueing theory, simulation and applications to mathematical finance. |
| PSTAT 170 - Introduction to Mathematical Finance |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B and 160A. |
| PSTAT 160B and 171. |
| Describes mathematical methods for estimating and evaluating asset pricing models, equilibrium and derivative pricing, options, bonds, and the term-structure of interest rates. Also introduces finance optimization models for risk management and financial engineering. |
| PSTAT 171 - Mathematics of Compound Interest |
| (4) Staff |
| Prerequisites: Mathematics 3A-B. |
| Introduction to compound interest. Topics include: measurement of interest,annuities certain, varying annuities, amortization schedules, sinking funds, bonds and related securities, depreciation. |
| PSTAT 171 - Mathematics of Fixed Income Markets |
| (4) Staff |
| Prerequisites: Mathematics 3A-B. |
| Introduction to fixed Income Markets. Topics include: measurement of interest, annuities certain, varying annuities, amortization schedules, sinking funds, bonds and related securities, depreciation. |
| PSTAT 171 - Mathematics of Compound Interest |
| (4) Staff |
| Prerequisites: Mathematics 3A-B. |
| Introduction to compound interest. Topics include: measurement of interest,annuities certain, varying annuities, amortization schedules, sinking funds, bonds and related securities, depreciation. |
| PSTAT 171 - Mathematics of Fixed Income Markets |
| (4) Staff |
| Prerequisites: Mathematics 3A-B. |
| Introduction to fixed Income Markets. Topics include: measurement of interest, annuities certain, varying annuities, amortization schedules, sinking funds, bonds and related securities, depreciation. |
| PSTAT 172A - Actuarial Statistics I |
| (4) STAFF |
| Prerequisites: PSTAT 120A and 171. |
| Probabilistic and deterministic contingency mathematics in life and health insurance, annuities, and pensions. Topics include: survival distributions and life tables, life insurance, life annuities, net premiums, net premium reserves. |
| PSTAT 172B - Actuarial Statistics II |
| (4) STAFF |
| Prerequisites: PSTAT 172A. |
| Net premium reserves, multiple life functions, multiple decrement models, valuation theory for pension plans, insurance models including expenses, nonforfeiture benefits and dividends. |
| PSTAT 173 - Risk Theory |
| (4) STAFF |
| Prerequisites: PSTAT 120A. |
| Utility theory and the economics of insurance, individual risk models for ashort term, collective risk models for a single period and for an extended period, applications. |
| PSTAT 174 - Time Series |
| (4) Staff |
| Prerequisites: PSTAT 120A-B. |
| Stationary and non-stationary models, seasonal time series, ARMA models:
calculation of ACF, PACF, mean and ACF estimation. Barlett's formula, model
estimation: Yule-Walker estimates, ML method. identification techniques,
diagnostic checking forecasting, spectral analysis, the periodogram. Current
software and applications. |
| PSTAT 175 - Survival Analysis |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Properties of survival models, including both parametric and tabular models; methods of estimating them from both complete and incomplete samples, including the actuarial, moment and maximum likelihood estimation techniques, and the estimation of life tables from general population data. |
| PSTAT 182T - Tutorial in Actuarial Statistics |
| (1) STAFF |
| Prerequisites: Upper-division standing and consent of instructor. |
| Problem solving sessions to prepare students for the first four actuarial examinations. Topics corresponding to these examinations (general mathematics, mathematical statistics, applied statistics and mathematics, and actuarial mathematics) will be offered in different quarters. |
| PSTAT 192 - Computer Laboratory |
| (1) STAFF |
| Prerequisites: Upper-division standing; consent of instructor. |
| Students become familiar with certain computer facilities and with some computing languages which are used to solve problems designed to enrich and supplement the material in the specified statistics course. |
| PSTAT 193 - Internship in Statistics |
| (1-4) STAFF |
| Prerequisites: Upper-division standing and consent of instructor. |
| Faculty sponsored academic internship in industrial or research firms. |
| PSTAT 194 - Group Studies for Advanced Students |
| (1-4) STAFF |
| Prerequisites: Upper-division standing; consent of instructor. |
| Lectures and discussions on special topics in probability and statistics. |
| PSTAT 195 - Special Topics in Statistics |
| (1-4) STAFF |
| Prerequisites: Upper-division standing in statistics and consent of instructor. |
| Special topics of current importance in statistical sciences. Course content will vary. |
| PSTAT 199 - Independent Studies in Statistics |
| (1-4) STAFF |
| Prerequisites: Upper-division standing; completion of 2 upper-division courses in PSTAT. |
| Independent studies in statistics. |
| PSTAT 199RA - Independent Research Assistance |
| (1-4) STAFF |
| Prerequisites: Upper-division standing; PSTAT 120A-B-C; an additional upper-division coursin PSTAT; consent of instuctor and department. |
| Coursework shall consist of faculty supervised research assistance. |
| PSTAT 207A - Statistical Theory |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B-C. |
| Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory: likelihood, sufficiency, efficiency, maximum likelihood; testing hypotheses: likelihood ratio and score tests, power; confidence and prediction intervals; bavesian estimation and hypothesis testing; basic decision theory, linear regression; analysis of variance. |
| PSTAT 207B - Statistical Theory |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B-C. |
| Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory: likelihood, sufficiency, efficiency, maximum likelihood; testing hypoth- eses: likelihood ratio and score tests, power; confidence and prediction intervals; bavesian estimation and hypothesis testing; basic decision theory, linear regression; analysis of variance. |
| PSTAT 207C - Statistical Theory |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B-C. |
| Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory; likelihood, sufficiency, efficiency, maximum likelihood; testing hypotheses: likelihood ratio and score tests, power; confidence and prediction intervals; bavesian estimation and hypothesis testing. |
| PSTAT 210 - Measure Theory for Probability |
| (4) STAFF |
| Prerequisites: PSTAT 120A. |
| Probability spaces: axioms, sigma-algebras, monotone class theorems, construction of probability measures on measurable spaces. Random variables. Expectations (integral Lebesque). Product spaces and Fubini theorem. L2 spaces of random variables. |
| PSTAT 213A - Introduction To Probability Theory And Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Students are advised to complete Mathematics 118A-B-C in preparation for this course. |
| Markov chains; random walks; branching processes; convergence concepts; laws of large numbers; characteristic functions; weak convergence; central limit theorems; conditional expectations; martingale sequences; introduction to large deviations; ergodic theory; continuous time; stochastic processes and Brownian motion. |
| PSTAT 213B - Introduction to Probability Theory and Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Mathematics 118A-B-C. |
| Markov chains; random walks; branching processes; convergence concepts; laws of large numbers; characteristic functions; weak convergence; central limit theorems; conditional expectations; matingale sequences; introduction to large deviations; ergodic theory; continuous time; stochastic processes and Brownian motion. |
| PSTAT 213C - Introduction To Probability Theory And Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B; PSTAT 210 or equivalent. |
| Students are advised to complete Mathematics 118A-B-C in preparation for this course. |
| Markov chains; random walks; branching processes; convergence concepts; laws of large numbers; characteristic functions; weak convergence; central limit theorems; conditional expectations; martingale sequences; introduction to large deviations; ergodic theory; continuous time; stochastic processes and Brownian motion. |
| PSTAT 215A - Bayesian Inference |
| (4) STAFF |
| Prerequisites: PSTAT 207A or PSTAT 220A (may be taken concurrently). |
| Fundamentals of the Bayesian inference, including the likelihood principle, the discrete version of Bayes theorem, prior and posterior distributions, Bayesian point and interval estimations, and predictions. Bayesian computational methods such as Laplacian approximations and
Markov Chain Monte Carlo (MCMC) simulation. |
| PSTAT 215B - Statistical Decision Theory |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B-C. |
| Statistical inference including estimation, testing and multiple decision rules in decision theoretic framework, relationship to game theory, admissibility, optimality including Bayes and minimax rules, empirical and hierarchical Bayes, invariant decisions. |
| PSTAT 215C - Statistical Decision Theory |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B-C. |
| Statistical inference including estimation, testing and multiple decision rules in decision theoretic framework, relationship to game theory, admissibility, optimality including Bayes and minimax rules, empirical and hierarchical Bayes, invariant decisions. |
| PSTAT 216 - Multivariate Analysis |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B-C or equivalent. |
| Statistical theory associated with the multivariate normal, wishart and related distributions, partial and multiple correlation, principal components. Hotelling's T2-statistic, multivariate linear models, classification and discriminant analysis. Other topics may include invariance, admissibility, minimax, james-stein estimates, multivariate probability inequalities, majorization, and Schur functions. |
| PSTAT 217 - Advanced Topics in Mathematical Statistics |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B-C. |
| May be repeated for credit provided topics are different. |
| Topics in mathematical statistics and decision theory including: asymptotics, nonparametric function estimation, design of experiments and linear models, sequential analysis, multiple testing problems, semiparametric inference, directional statistics. |
| PSTAT 220A - Advanced Statistical Methods |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B-C, 122, 126, and Mathematics 108A or equivalents. |
| General linear models; regression; analysis of variance of fixed, random, and mixed effects models; analysis of covariance; and experimental design. Discussion of each technique includes graphical methods; estimation and inference; diagnostics; and model selection. Emphasis on application rather than theory. R/SAS Computation. |
| PSTAT 220B - Advanced Statistical Methods |
| (4) STAFF |
| Prerequisites: PSTAT 120A or equivalent. |
| Generalized linear models; log-linear models with application to categorical data; and nonlinear regression models. Discussion of each technique includes graphical methods; estimation and inference; diagnostics; and model selection. Emphasis on application rather than theory. R/SAS computation. |
| PSTAT 220C - Advanced Statistical Methods |
| (4) STAFF |
| Prerequisites: PSTAT 220A and Mathematics 108B or equivalents. |
| Multivariate analysis. Topics selected from factor analysis; canonical correlation analysis; classification and discrimination; clustering; and data mining. Emphasis on application rather than theory. R/SAS computation. |
| PSTAT 221A - Advanced Probability Theory |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: large deviations; random walks; weak covergence in metric spaces; empirical processes; point processes; Gaussian processes; random fields; branching processes; inference for stochastic processes. Applications. |
| PSTAT 221B - Advanced Probability Theory |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: large deviations; random walks; weak convergence in metric spaces; empirical processes; point processes; Gaussian processes; random fields; branching processes; inference for stochastic processes. Applications. |
| PSTAT 221C - Advanced Probability Theory |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: large deviations; random walks; weak convergence in metric spaces; empirical processes; point processes; Gaussian processes; random fields; branching processes; inference for stochastic processes. Applications. |
| PSTAT 222A - Advanced Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: Markov processes; continuous time matringales; theory of Brownian motion and diffusion processes; Levy processes stochastic calculus; stochastic differential equations and numerical methods; stochastic control. Applications to engineering, finance, biology, etc. |
| PSTAT 222B - Advanced Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: Markov processes; coninuous time matringales; theory of Brownian motion and diffusion processes; Levy processes stochastic calculus; stochastic differential equations and numerical methods; stochastic control. Applications to engineering, finance, biology, etc. |
| PSTAT 222C - Advanced Stochastic Processes |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| Topics chosen from: Markov processes; continuous time matringales; theory of Brownian motion and diffusion processes; Levy processes stochastic calculus; stochastic differential equations and numerical methods; stochastic control. Applications to engineering, finance, biology, etc. |
| PSTAT 223A - Financial Modeling--An Engineering Approach |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| An introduction to stochastic models in finance. Stochastic models and applications to price determination for stocks, bonds, derivative securities, interest rate term structure. Portfolio issues, hedging, risk management and financial engineering. Numerical methods and computation. |
| PSTAT 223B - Financial Modeling--An Engineering Approach |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| An introduction to stochastic models in finance. Stochastic models and applications to price determination for stocks, bonds, derivative securities, interest rate term structure. Portfolio issues, hedging, risk management and financial engineering. Numerical methods and computation. |
| PSTAT 223C - Financial Modeling--An Engineering Approach |
| (4) STAFF |
| Prerequisites: PSTAT 213A-B-C. |
| An introduction to stochastic models in finance. Stochastic models and applications to price determination for stocks, bonds, derivative securities, interest rate term structure. Portfolio issues, hedging, risk management and financial engineering. Numerical methods and computation. |
| PSTAT 225 - Linear and Nonlinear Mixed Effects Models |
| (4) STAFF |
| Prerequisites: PSTAT 220A or equivalent. |
| Linear and nonlinear mixed effects models. Topics include fixed effects, random effects, several size experimental units, design structure, treatment structure, randomized block design, nested design, split plot design, repeated measures, growth curves, longitudinal and spatial data, BLUP, ML, and REML estimates. |
| PSTAT 226 - Nonparametric Regression and Classification Methods |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B and 220A or equivalents. |
| Introduction to some statistical regression and classification techniques including kernel smoothing, smoothing spline, local regression, generalized additive models, neural networks, wavelets, decision tree and nearest neighbor methods. |
| PSTAT 227 - Bootstrap and Resampling Methodology |
| (4) STAFF |
| Prerequisites: PSTAT 207A-B and PSTAT 220A or equivalents. |
| Resampling methods: bootstrap and subsampling. Topics: parametric and nonparametric bootstrap simulation; confidence limit methods; resample significance tests, including Monte Carlo and bootstrap; resampling for improved regression model selection and prediction; diagnostics for bootstrap validity. |
| PSTAT 228 - Spline Smoothing and Applications |
| (4) STAFF |
| Prerequisites: Statistics & Applied Probability 207A, B, C and 22A. |
| Model building, multivariate function estimation and supervised learning using reproducing kernel Hilbert space, regularization and splines. Smoothing splines for Gaussian and non-Gaussian data. Bayesian models and data-driven turning parameter selection. Emphasis on methodology, computation and application. |
| PSTAT 230 - Seminar and Projects in Statistical Consulting |
| (4) STAFF |
| Prerequisites: PSTAT 220A-B-C (may be taken concurrently) |
| Students participate in the discussions and consulting projects in the statistics laboratory. They are assigned project(s) to work on and write a report on statistical aspects of the project. |
| PSTAT 231 - Data Mining |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B, 130; and, PSTAT 120C or 126 (may be taken concurrently) |
| Introduction to data mining techniques. Model assessment and performance evaluation. Data preparation. Programming techniques for transforming raw data into a form suitable for predictive modeling. Extracting data to a form that predictive models can utilize. Incorporating non-numeric data in predictive models. Techniques for managing exceptional and extreme data. Building predictive models using SAS Enterprise Miner 5 in SAS 9, including Decision Trees, Neural Networks, and Bayesian Networks. |
| PSTAT 232 - Computational Techniques in Statistics |
| (4) Staff |
| Prerequisites: PSTAT 120A-B-C, 160A-B-C or equivalent. Knowledge of at least one programming language. |
| Explores computationally-intensive methods in statistics. Topics covered include combinatorial optimization, EM optimization, Monte Carlo simulation, Markov Chain Monte Carlo methods and bootstrapping. Lab work is carried out using R or SAS. |
| PSTAT 233A - Introduction To Statistical Methods. |
| (4) STAFF |
| Prerequisites: Not open to Mathematics and Statistics majors. |
| This course will consider basic ideas in probability and cover important topics in statistical methods with the minimum use of calculus; it will rely on the use of personal computers. Topics: probability, random variables and distributions, expectation and variance, binomial, normal, and other probability models. statistical tests, correlation, and regression. Elementary design of experiments, analysis of variance, sampling, and nonparametric statistics. |
| PSTAT 233B - Introduction To Statistical Methods. |
| (4) STAFF |
| This course will consider basic_ideas in probability and cover important topics in statistical methods withthe minimum use of calculus; it will rely on the use of personal computers.Topics: probability, random variabl- es and distributions, expectation and_variance, binomial, normal, and otherprobability models. Statistical_tests, correlation, and regression. Elementary design of experiments,_analysis of variance, sampling, and nonparametric statistics. |
| PSTAT 250 - Quantitative Methods in the Social Sciences Colloquium |
| (2) STAFF |
| Required colloquium course for students in the interdisciplinary Quantitative Methods in the Social Sciences emphasis. |
| PSTAT 262 - Seminars In Probability and Statistics |
| (1-6) STAFF |
| Prerequisites: PSTAT 120A-B-C; consent of instructor. |
| Topics of current research interest in probability and/or statistics, by means of lectures and informal conferences with members of staff. PSTAT 262FM is reserved for topics in financial mathematics and statistics. |
| PSTAT 262FM - Seminars In Probability And Statistics |
| (1-6) STAFF |
| Prerequisites: PSTAT 120A-B-C and consent of instructor. |
| Topics of current research interest in probability and/or statistics, by means of lectures and informal conferences with members of staff. PSTAT 262FM is reserved for topics in financial mathematics and statistics. |
| PSTAT 263 - Research Seminars in Probability and Statistics |
| (1) STAFF |
| Prerequisites: Graduate standing. |
| Research seminars presented by faculty, visiting scholars, and invited speakers on current research topics. |
| PSTAT 274 - Time Series |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B. |
| Stationary and non-stationary models, seasonal time series, ARMA models: calculation of ACF, PACF, mean and ACF estimation. Barlett's formula, model estimation: Yule-Walker estimates, ML method. Identification techniques, diagnostic checking, forecasting, spectral analysis, the periodogram. Current software and applications. |
| PSTAT 275 - Survival Analysis |
| (4) STAFF |
| Prerequisites: PSTAT 120A-B-C and PSTAT 220A. |
| Basic concepts: survival functions, hazard functions, cumulative hazard functions, and censoring types. Kaplan-Meier and Nelson-Fleming-Harrington estimates. Log-rank tests. Exponential and Weibull models. Cox proportional hazards and accelerated failure time regression models. Current software and applications. |
| PSTAT 500 - Teaching Assistant Practicum |
| (1-4) STAFF |
| Prerequisites: Appointment as teaching assistant. |
| Supervised teaching of undergraduate Probability and Statistics courses. |
| PSTAT 501 - Teaching Assistant Training |
| (1-2) STAFF |
| Prerequisites: Appointment as teaching assistant. |
| Consideration of ideas about the process of learning probability and statistics, and discussion of approaches to teaching. |
| PSTAT 502 - Teaching Associate Practicum |
| (1-5) STAFF |
| Prerequisites: Appointment as associate. |
| Supervised teaching of undergraduate courses. |
| PSTAT 510 - Readings for Area Examinations |
| (2-6) STAFF |
| Prerequisites: Enrollment in M.A. or Ph.D. program. |
| Readings for area examinations. |
| PSTAT 596 - Directed Reading and Research |
| (1-6) STAFF |
| Prerequisites: Graduate standing and consent of instructor. |
| Directed reading and research. |
| PSTAT 598 - Master's Thesis Research and Preparation |
| (1-6) STAFF |
| Prerequisites: Consent of instructor. |
| Only for research underlying the thesis, writing the thesis. Instructor should be the chair of the student's thesis committee. |
| PSTAT 599 - Ph.D Dissertation Preparation |
| (1-6) STAFF |
| Prerequisites: Graduate standing and consent of instructor. |
| Ph.D dissertation preparation. |