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Prerequisites: Three years of high school mathematics including algebra, geometry, and intermediate algebra (or MATH 010), or the equivalent.
Exploratory data analysis, methods of visualizing data, descriptive
statistics, misuse and manipulation of data in statistical analysis,
probability, binomial and normal distributions, hypothesis testing,
correlation and regression, contingency tables. (Lecture 3 hrs.) (CAN
STAT 2)
UPPER DIVISION STAT COURSES
STAT 380. Probability and Statistics (3)
Prerequisite: MATH 222 or 224.
Frequency interpretation of probability. Axioms of probability
theory. Discrete probability and combinatorics. Random variables.
Distribution and density functions. Moment generating functions and
moments. Sampling theory and limit theorems. (Lecture 3 hrs.)
STAT *381. Mathematical Statistics (3)
Prerequisites: MATH 247 and 380.
Estimation and hypothesis testing. Maximum likelihood and method of
moments estimation. Efficiency, unbiasedness, and asymptotic
distribution of estimators. Neyman-Pearson Lemma. Goodness-of-fit
tests. Correlation and regression. Experimental design and analysis of
variance. Nonparametric methods. (Lecture 3 hrs.)
STAT 410./510. Regression Analysis (3)
Prerequisites: MATH 247, 380; prerequisite STAT 381. (Undergraduates enroll in STAT 410; graduates enroll in
STAT 510.)
Simple linear regression: estimation and inference, prediction,
analysis of residuals, detection of outlier, use of transformations.
Multiple linear regression: influence diagnostics, mult-colinearity,
selection of variables, simultaneous estimation and inference,
validation techniques. Statistical software for data analysis used.
Letter grade only (A-F). (Lecture 3 hrs.)
STAT 450./550. Multivariate Statistical Analysis (3)
Prerequisites: STAT 381; prerequisite/corequisite
STAT 410. (Undergraduates enroll 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.)
STAT *475. Data Analysis With SAS (3)
Prerequisite: STAT 381 or consent of instructor.
Topics include: Statistical analysis including extraction,
presentation of data in graphical form, creation, modification of
datasets, interpretation of output, writing of reports. Provides SAS
programming techniques for aforementioned topics as well as prepare for
SAS base certification. (Lecture 3 hrs.)
STAT *482. Random Processes (3)
Prerequisites: MATH 247 and
380.
Further topics in probability. Markov processes. Renewal theory. Random
walks. Queueing theory. Poisson processes. Brownian motion. (Lecture 3 hrs.)
STAT *484. Actuarial Science: Models(3)
Prerequisites: STAT 381 or consent of instructor.
Statistical techniques applied to risk management. Expected utility
theory, individual risk models, compound Poisson distributions and
processes, ruin probability and first surplus, stop-loss and
proportional reinsurance, statistical survival distributions and life
tables, life annuity, actuarial present values, and premiums
determination.
Letter grade only (A-F). (Lecture 3 hrs.)
STAT *485. Actuarial Science: Financial Mathematics(3)
Prerequisites: STAT 381 or consent of instructor.
Letter grade only (A-F). (Lecture 3 hrs.)
STAT *495. Topics in Modern Statistics (3)
Prerequisite: Consent of instructor.
Topics of current interest from statistics literature.
STAT 497. Directed Study (3)
Prerequisite: Consent of instructor.
Graduate STAT Courses
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STAT 510./410. Regression Analysis (3)
Prerequisites: MATH 247, 380, prerequisite STAT 381.
(Undergraduates enroll in MATH 480; graduates enroll in MATH 590.)
Simple linear regression: estimation and inference,
prediction, analysis of residuals, detection of outliers, use of
transformations. Multiple linear regression: influence
diagnostics, multicollinearity, selection of variables, simultaneous
estimation and inference, validation techniques. Use of
statistical software for data analysis. Letter grade only
(A-F). (Lecture 3 hrs.)
STAT 520. Statistical Inference (3)
Prerequisites: STAT 381 or consent of instructor.
Properties of a random sample, convergence in probability, the law of
large numbers, sampling from the normal distribution, the central limit
theorem, principles of data reduction, the likelihood principle, point
estimation, Bayesian estimation, methods of evaluating estimators,
hypothesis testing, decision theory, confidence intervals. Traditional
grading only. (Lecture 3 hrs.)
STAT 530. Experimental Design and Analysis (3)
Prerequisites: STAT 381 or consent of instructor.
The 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. (Lecture
3 hrs.)
STAT 532. Statistical Quality Control (3)
Prerequisites: STAT 381 or consent of instructor.
An introduction to the methods of statistical quality control. Topics
covered include control charts, acceptance sampling, process capability
analysis, and some aspects of experimental design. (Lecture 3 hrs.)
STAT 540. Survey Sampling (3)
Prerequisites: 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).
STAT 550./450. Multivariate Statistical Analysis (3)
Prerequisites: STAT 381, prerequisite or corequisite
STAT 410
(Undergraduates enroll in STAT 450; graduates enroll in STAT 550.) Potential topics include: discriminate analysis, principal
components, factor analysis, cluster analysis, logistic regression,
canonical correlation, multidimensional scaling, and some
nonlinear techniques. Statistical software will be used.
Letter grade only (A-F). (Lecture 3 hrs.)
STAT 560. Nonparametric Statistics (3)
Prerequisites: STAT 410 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).
STAT 570. Statistical Simulation (3)
Prerequisites: 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 analysis of simulated
data; variance reduction; statistical validation; introduction to
simulation languages; industry applications. Statistical packages
such as SAS or MATLAB will be used throughout the course. Letter grade
only (A-F). (Lecture 3 hrs).
STAT 572. Computational Statistics (3)
Prerequisites: 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.)
STAT 574. Data Mining (3)
Prerequisites: STAT 410 or
510 or consent of instructor.
Basics of data mining algorithms with an emphasis on applications to
industry. Prediction and classification techniques such as Multivariate
Adaptive Regression Splines (MARS), Classification and Regression Trees
(CART), neural networks, and other methods. Several software packages
will be utilized. Traditional grading only. (Lecture 3 hrs.)
STAT 576. Data Informatics (3)
Prerequisites: STAT 410 or 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.)
STAT 580. Time Series (3)
Prerequisites: STAT 381.
Potential topics include: moving averages, smoothing, Box-Jenkins
(ARIMA) models, tests for nonstationarity, model fitting and checking,
prediction and model selection, seasonal adjustment, ARCH, GARCH,
cointegration, state-space models. Computer analysis using statistical
packages such as SPSS and SAS will be used extensively throughout the
course. (Lecture 3 hrs.) Letter grade only (A-F).
STAT 590. Statistical Analysis of Medical Data (3)
Prerequisites: 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. (Lecture 3 hrs.) Letter grade only (A-F).
STAT 695. Seminar in Applied Statistics (3)
Prerequisites: Consent of instructor
Presentation and discussion of advanced work, including original
research by faculty and students. Topics to be announced in the
Schedule of Classes. May be repeated to a total of six units.
STAT 697. Directed Studies (1-3)
Prerequisites: Consent of instructor
Research
on a specific area in applied statistics. Topic for study to be approved and
directed by advisor in the mathematics department.
STAT 698. Thesis or Project (2-4)
Prerequisites: Completion of at least one 500 and/or 600 level statistics course.
Formal report of research or project in statistics.
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