756
Statistics (STAT)
STAT 5053 Time Series Analysis Prerequisites: STAT 4043.
STAT 5093 Statistical Computing Prerequisites: STAT 5123 or STAT 4203, STAT 5013 or equivalent, CS 1113 or equivalent. Description: Random variable generation; numerical calculations of maximum likelihood estimators, quasi-likelihood estimators, probabilities, and quantiles; computer intensive exact tests and distributions; randomized tests; bootstrap and jack knife methods, Monte Carlo simulations Markov Chain Monte Carlo methods for Bayesian estimation. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture Department/School: Statistics STAT 5123 Probability Theory Prerequisites: MATH 2163 and one other course in MATH that has either MATH 2144 or MATH 2153 as a prerequisite. Description: Basic probability theory, random events, dependence and independence, random variables, moments, distributions of functions of random variables, weak laws of large numbers, central limit theorems. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture
Description: An applied approach to the analysis of time series in the time domain. Trends, autocorrelation, random walk, seasonality, stationarity, autoregressive integrated moving average (ARIMA) processes, Box- Jenkins method, forecasting. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture
Department/School: Statistics STAT 5063 Multivariate Methods Prerequisites: STAT 4043 and (STAT 4023 or STAT 5023).
Description: Use of Hotelling's T-squared statistic, multivariate analysis of variance, canonical correlation, principal components, factor analysis and linear discriminate functions. No degree credit for students with credit in STAT 4463. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture Department/School: Statistics STAT 5073 Categorical Data Analysis Prerequisites: STAT 5223, STAT 5023 or equivalent or concurrent enrollment. Description: Analysis of data involving variables of a categorical nature. Contingency tables, exact tests, binary response models, loglinear models, analyses involving ordinal variables, multinomial response models. Computer usage for analysis is discussed. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture Department/School: Statistics STAT 5083 Statistics for Biomedical Researchers Prerequisites: STAT 5013. Description: Analysis of variance, experimental designs pertaining to medical research, regression and data modeling, categorical techniques and the evaluation of diagnostic tests. No credit for students with credit
Department/School: Statistics STAT 5133 Stochastic Processes Prerequisites: STAT 5123 and MATH 2233, MATH 3013.
Description: Definition of a stochastic process, probability structure, mean and covariance function, the set of sample functions, stationary processes and their spectral analyses, renewal processes, counting processes, discrete and continuous Markov chains, birth and death processes, exponential model, queuing theory. Same course as IEM 5133
& MATH 5133. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture Department/School: Statistics STAT 5191 R Programming Prerequisites: STAT 4013 or equivalent.
Description: R dataset construction, elementary statistical analysis, and use of statistics and graphics with R. May not be used for degree credit with STAT 4191, STAT 4193, STAT 5193. Credit hours: 1 Contact hours: Lecture: 1 Levels: Graduate Schedule types: Lecture Department/School: Statistics
in STAT 5023. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture
Department/School: Statistics STAT 5091 Sas Programming Prerequisites: STAT 5013 or equivalent.
Description: SAS dataset construction, elementary statistical analysis, and use of statistics and graphics procedures available in SAS. No credit for students with credit in STAT 4091. Credit hours: 1 Contact hours: Lecture: 1 Levels: Graduate Schedule types: Lecture Department/School: Statistics
STAT 5193 SAS and R Programming Prerequisites: STAT 5013 or equivalent.
Description: SAS and R dataset construction, elementary statistical analysis, and use of statistics and graphics with SAS and R. Students are required to complete the SAS Certified Base Programmer exam. Exam content, fees, and discount information is available at https:// www.sas.com/en_us/certification.html#. May not be used for degree credit with STAT 4091, STAT 4191, STAT 4193, STAT 5191, STAT 5091. Credit hours: 3 Contact hours: Lecture: 3 Levels: Graduate Schedule types: Lecture Department/School: Statistics
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