Graduate Degree Courses of Statistics at Virginia Polytechnic Institute and State University
Statistics Courses For Graduate at Virginia Polytechnic Institute And State University
STAT 4004 - Methods of Statistical Computing
Computationally intensive computer methods used in statistical analyses. Statistical univariate and multivariate graphics; resampling methods including bootstrap estimation and hypothesis testing and simulations; classification and regression trees; scatterplot smoothing and splines.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4105 - Theoretical Statistics
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4106 - Theoretical Statistics
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4204 - Experimental Designs
Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and latin square designs, analysis of covariance, split–plot designs, factorial and fractional designs, incomplete block designs. Project. Knowledge of WIN/MAC required. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4214 - Methods of Regression Analysis
Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS. Project. Knowledge of WIN/MAC required. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4504 - Applied Multivariate Analysis
Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Use of SAS. Project. Knowledge of WIN/MAC required, even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4514 - Contingency Table Analysis
Statistical techniques for frequency data. Goodness-of-fit. Tests and measures of association for two-way tables. Log-linear models for multidimensional tables. Parameter estimation, model selection, incomplete tables, ordinal categories, logistic regression. Use of SAS and SPSSx. Project. Knowledge of WIN/MAC required, even years. II.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4524 - Sample Survey Methods
Statistical methods for the design and analysis of survey sampling. Fundamental survey designs. Methods of randomization specific to various survey designs. Estimation of population means, proportions, totals, variances, and mean squared errors. Design of questionnaires and organization of a survey. Project. Odd years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4584 (MATH 4584) - Advanced Calculus for Statistics
Introduction to those topics in advanced calculus and linear algebra needed by statistics majors. Infinite sequences and series. Orthogonal matrices, projections, quadratic forms. Extrema of functions of several variables. Multiple integrals, including convolution and nonlinear coordinate changes.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4604 - Statistical Methods for Engineers
Introduction to statistical methodology with emphasis on engineering applications: probability distributions, estimation, hypothesis testing, regression, analysis of variance, quality control. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. Knowledge of WIN required. I, II.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4705 - Probability and Statistics for Engineers
Basic concepts of probability and statistics with emphasis on engineering applications. 4705: Probability, random variables, sampling distributions, estimation, hypothesis testing, simple linear regression correlation, one-way analysis of variance. 4706: Multiple regression, analysis of variance, factorial and fractional experiments. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. Knowledge of WIN/MAC required. I, II, III.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4706 - Probability and Statistics for Engineers
Basic concepts of probability and statistics with emphasis on engineering applications. 4705: Probability, random variables, sampling distributions, estimation, hypothesis testing, simple linear regression correlation, one-way analysis of variance. 4706: Multiple regression, analysis of variance, factorial and fractional experiments. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. WIN/MAC. I, II.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4714 - Probability and Statistics for Electrical Engineers
Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application in electrical engineering. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. I, II, III.
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4724 - Statistical Theory for Economists
Probability, random variables, marginal and conditional distributions, mathematical expectations, sampling distributions, properties of estimators, maximum likelihood and least squares estimation, confidence intervals, hypothesis tests, linear regression. Emphasis on preparation for graduate study in econometrics. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 4804 (AAEC 4804) - Elementary Econometrics
Economic applications of mathematical and statistical techniques: regression, estimators, hypothesis testing, lagged variables, discrete variables, violations of assumptions, simultaneous equations. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Undergraduate and Graduate
Instruction Type: Lecture
STAT 5014 - Introduction to Statistical Program Packages
Introduction to computing facilities (mainframe and microcomputers), conversational monitoring system (CMS), and statistical program computer packages. Restricted to Statistics majors. I
Credit Hours: 1
Lecture Hours: 1
Level(s): Graduate
Instruction Type: Lecture
STAT 5024 - Effective Communication in Statistical Consulting
Application of statistical design, analysis and computing methods to current interdisciplinary statistical consulting projects. Skills important to effective client-statistician interactions, including interview, report-writing and oral presentation skills, will be developed.
Credit Hours: 1
Lecture Hours: 1
Level(s): Graduate
Instruction Type: Lecture
STAT 5034 - Inference Fundamentals with Applications to Categorical Data
Fundamental ideas of statistical estimation and testing; principles and methods for standard one-sample settings; applications to categorical data problems. Topics include probability distributions, means, variances, and independence; point and interval estimation, including small and large sample procedures; hypothesis testing including exact and large-sample tests; goodness-of-fit; categorical data analysis; log-linear models; simple logistic regression.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5044 - Regression and Analysis of Variance
Principles and methods of data analysis employing linear models for continuous response variables. Topics include both classical descriptive measures and modern computer-based techniques for data visualization; simple, multiple and weighted regression; analysis of variance for one-way and higher-way classifications; fixed, mixed, and random effects models; analysis of covariance; detection and correction of modeling flaws; statistical power.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5104 - Probability and Distribution Theory
Fundamental concepts of probability, random variables and their distributions, functions of random variables, mathematical expectations, and stochastic convergence. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5114 - Statistical Inference
Decision theoretic formulation of statistical inference, concept and methods of point and confidence set estimation, notion and theory of hypothesis testing, relation between confidence set estimation and hypothesis testing. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5124 - Linear Models Theory
A study of the theory underlying the general linear model and general linear hypothesis. Applications in linear regression (full rank) and analysis of variance. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5204 - Experimental Design and Analysis I
Principles and concepts of experimental design; systematic overview and discussion of basic designs from the point of view of blocking, error reduction, and treatment structure; and development of analysis based on linear models. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5304 - Statistical Computing
Computational methods for statistical computing problems. Orthogonal transformations, sweep operators, and other numerical methods applied to general optimization techniques for maximum likelihood and least squares estimation problems. Knowledge of SAS programming language required. Even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5334 - Exploratory and Robust Data Analysis
Analysis of data by graphical and numerical techniques, statistical analysis of non-Gaussian data, topics in robust estimation for location, regression and correlation models, and the jackknife and bootstrap techniques. CMS. Even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5354 - Structured Process Improvement
An introduction to the selection, management, leadership and execution of structured process improvement projects. Topics include effective roadmaps for process improvement, team facilitation and leadership, project selection and management, sampling, process capability analysis, data transformation, variance component analysis, response surface methodology (including full and fractional factorial designs, Plackett-Burman designs, central composite designs, Box-Behnken designs, analysis of variance, regression, and multi-response optimization), and statistical process control.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5404 - Nonparametric Statistics
Introduction to theory and methods of nonparametric statistical inference. General linear rank statistics, tests and estimation of location, dispersion, regression, and association. Selected topics. Odd years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5414 - Time Series Analysis I
Analysis of data when observations are not mutually independent, stationary and nonstationary time series, linear filtering, trend elimination, prediction, and applications in economics and engineering. Even years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5444 - Bayesian Statistics
Introductory course of Bayesian statistics on basic concepts of probability, Bayesian inference of Normal, Binomial, Poisson, Uniform and other common distributions, selections of prior information, Bayesian decision theory, Bayesian analysis of regression and analysis of variance and Bayesian foundation. Even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5454 - Reliability Theory
Basic concepts of lifetime distributions, types of censoring, inference procedures for exponential, Weibull and extreme value distributions, nonparametric estimation of survival function, kernel density estimation, accelerated life testing, and goodness of fit tests. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5474 (ISE 5474) - Statistical Theory of Quality Control
Development of statistical concepts and theory underlying procedures used in quality control applications. Sampling inspection procedures, the sequential probability ratio test, continuous sampling procedures, process control procedures, and experimental design. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5504 - Multivariate Statistical Methods
Methods of inference for multivariate distributions. Multivariate distributions, location and dispersion problems for one and two samples, multivariate analysis of variance, linear models, repeated measurements, inference for dispersion and association parameters, principal components, discriminant and cluster analysis, and simultaneous inference. Emphasis on robustness. Use of SAS. Knowledge of CMS required. Even years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5514 - Regression Analysis
Classical and modern techniques in regression analysis. Use of modern regression techniques to diagnose collinearity, leverage, and outliers. Model discrimination using cross validation techniques. The study of transformations, biased estimation, and nonlinear regression. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5524 - Sample Survey Theory
Theory of sample surveys including major sampling designs, sample size determination, estimation and interval estimation, and questionnaire design. Even years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5534 - Analysis of Multivariate Categorical Data
Log-linear models for unconstrained and ordinal multidimensional contingency tables; testing and estimation; random and structural zeros; model building; logit models and logistic regression; and use of major statistical packages. Knowledge of CMS required. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5544 - Spatial Statistics
Spatial data structures: geostatistical data, lattices and point patterns. Stationary and isotropic random fields. Autocorrelated data structures. Semivariogram estimation and spatial prediction for geostatistical data. Mapped and sampled point patterns. Regular, completely random and clustered point processes. Spatial regression and neighborhood analyses for data on lattices.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5554 - Variance Components
Theoretical treatment of the general problem of estimating and testing hypotheses about variance components within the framework of random effects and mixed linear models; derivation of different estimation procedures and their statistical properties; and discussion of balanced and unbalanced data and of designs for estimating variance components. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5564 - Statistical Genetics
Statistical methods for linkage mapping of quantitative trait genes in populations ranging from inbred lines to complex pedigrees. Statistical methods to fine-map genes using linkage disequilibrium. Population genetic analyses of DNA sequence data. Statistical methods include multiple regression, (co)variance components estimation, Maximum Likelihood and Bayesian analyses. Algorithms to implement these methods include Expectation-Maximization, Markov chain Monte Carlo, neural networks, and genetic algorithms. Data are discrete or continuous. Odd years.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5574 - Response Surface Design and Analysis I
Use of response surface analysis to design and analyze industrial experiments. First and second order models. First and second order experimental designs. Use of model diagnostics for finding optimum operating conditions. Even years. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5594 - Topics in Biostatistics
Course with variable content; specialized application of statistical theory and methodology to biological and medical sciences; topics include bioassay, epidemiology, survival analysis, and statistical ecology. May be repeated for credit with different topics. Odd years. III
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5605 - Biometry
5605: The normal distribution, estimation, hypothesis testing, simple linear regression, and one-way analysis of variance with applications to the biological sciences. 5606: Experimental design, nested and factorial analysis of variance, linear regression and correlation, and the use of SAS, with applications to the biological sciences. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5606 - Biometry
5605: The normal distribution, estimation, hypothesis testing, simple linear regression, and one-way analysis of variance with applications to the biological sciences. 5606: Experimental design, nested and factorial analysis of variance, linear regression and correlation, and the use of SAS, with applications to the biological sciences. Knowledge of CMS required. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5615 - Statistics in Research
5615: Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of convariance. One year of Calculus. CMS. I,III
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5616 - Statistics in Research
5615: Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of convariance. One year of Calculus and knowledge of CMS required. II,IV
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5624 - Managerial Statistics
Introduction to statistical concepts and methods: descriptive statistics, probability, and distributions; inferences for means, variances, and proportions; and regression and correlation. I,II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5634 - Statistics for Behavioral Sciences
Basic statistical methods used in research in the behavioral sciences with emphasis on the applications of these methods. Descriptive statistics, correlational analyses, and intermediate inferential methods will be discussed. I,II,III
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5644 - Nonparametric Statistical Methods
Applications of rank-order statistics, sign statistics, the empirical distribution function, and runs to commonly occurring data structures. Emphasis on concepts, assumptions, comparisons to normal theory methods, and hands-on data analysis with the computer. Knowledge of CMS required. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5654 - Measures of Association
Statistical methods of measuring association between nominal, ordinal, or interval level variables. Tests for significance of association. Use of two-group tests and analysis of variance to explore mixed mode relationships, such as betwen nominal and interval level variables. Prediction models for interval level measurements. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5665 - Statistics for Social Science Research
5665: Basic concepts of statistical inference, including probability, point and interval estimation, hypothesis testing logic; one-sample and two-sample tests on means, variances and proportions; one-way and two-way analysis of variance; multiple comparison procedures. 5666: Multi-way analysis of variance; nested designs; repeated measures experiments; correlation for discrete and continuous variables; simple and multiple linear regression. College Algebra required. CMS. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5666 - Statistics for Social Science Research
5665: Basic concepts of statistical inference, including probability, point and interval estimation, hypothesis testing logic; one-sample and two-sample tests on means, variances and proportions; one-way and two-way analysis of variance; multiple comparison procedures. 5666: Multi-way analysis of variance; nested designs; repeated measures experiments; correlation for discrete and continuous variables; simple and multiple linear regression. College algebra and knowledge of CMS required. I,II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5674 - Methods in Biostatistics
Statistical principles and methods required for effective clinical trial and clinical experiment design and analysis. Topics include graphical and numerical exploratory data analysis, and comparative tests of categorical, ordinal, and continuous data, simple, multiple linear and logistic regression analysis, design of experiments and sampling theory. Additional topics include diagnostic tests, relative risk, odd ratio, and estimation of effective dose. Students will learn to properly interpret output from statistical software. Graduate standing required.
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5754 - Internship in Statistics
Full time, supervised intership experience at a company or government agency performing statistical analysis. May be repeated for a maximum of 3 hours toward an M.S. degree and 6 hours toward a Ph.D. degree. Graduate standing in statistics and permission of department required.
Credit Hours: 1 to 6
Lecture Hours: 1 to 6
Level(s): Graduate
Instruction Type: Lecture
STAT 5894 - Final Examination
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 5904 (UAP 5904) (TA 5904) (PPWS 5904) (PHYS 5904) (PAPA 5904) (NR 5904) (NE 5904) (MSE 5904) (MKTG 5904) (MINE 5904) (ME 5904) (ISE 5904) (HTM 5904) (HORT 5904) (GIA 5904) (GEOG 5904) (FST 5904) (FOR 5904) (FIW 5904) (ESM 5904) (ENT 5904) (ENGR 5904) (EDPE 5904) (EDHL 5904) (EDCT 5904) (EDCI 5904) (ECPE 5904) (ECE 5904) (DASC 5904) (CSES 5904) (CS 5904) (COMM 5904) (CHEM 5904) (CHE 5904) (CEE 5904) (BSE 5904) (BMES 5904) (BC 5904) (ARCH 5904) (APSC 5904) (AOE 5904) (ALS 5904) (AHRM 5904) (AEE 5904) (AAEC 5904) - Project and Report
. I,II,III,IV,V
Credit Hours: 1 to 19
Level(s): Graduate
Instruction Type: Research
STAT 5924 - Graduate Seminar
Special topics in statistical theory and applications. May be taken for credit two times (max. 2C). I,II
Credit Hours: 1
Lecture Hours: 1
Level(s): Graduate
Instruction Type: Lecture
STAT 5974 - Independent Study
Credit Hours: 1 to 19
Lecture Hours: 1 to 19
Level(s): Graduate
Instruction Type: Independent Study
STAT 5984 - Special Study
Credit Hours: 1 to 19
Lecture Hours: 1 to 19
Level(s): Graduate
Instruction Type: Lecture, Lecture 1, Lecture 2, Lecture 3
STAT 5994 - Research and Thesis
Credit Hours: 1 to 19
Level(s): Graduate
Instruction Type: Research
STAT 6105 (MATH 6105) - Measure and Probability
Development of measure theoretic foundations of probability theory. 6105: sigma fields, probability, and general measures; random variables, measurability and distributions, integration, and expectation; product measures; Radon-Nikodym theorem and conditioning. 6106: Random variables and strong and weak laws of large numbers; characteristic functions, central limit theorem and martingales; stochastic processes and Brownian motion. 6105 parially duplicates Math 5225. Must be enrolled in PhD program. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6114 - Advanced Topics in Statistical Inference
Advanced course in the theory of inference for graduate students in statistics and other qualified graduate students. Develops foundations, sufficiency, information, estimation, hypothesis testing, invariance, and unbiasedness. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6404 - Advanced Topics in Nonparametric Statistics
Topics of current interest in research for nonparametric theory and methods, using recent advanced texts and journal articles. Even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6424 - Multivariate Statistical Analysis
Foundations of multivariate analysis. Distribution theory of vectors and matrices, inequalities, limit theory, the structure of some multivariate location-scale parameter families, derived distributions, invariant distributions, the principle of invariance in estimation and testing for multivariate location and scale parameters, and robust aspects of normal-theory multivariate procedures. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6494 - Advanced Topics in Mathematical Statistics
Advanced treatment beyond standard course offerings in topics such as theory of inference, nonparametrics, sequential analysis, and limit theory. May be repeated for credit with different topics. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6504 - Experimental Design and Analysis II
Theoretical treatment of construction and analysis of various types of incomplete block and factorial designs. I
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6514 - Advanced Topics in Regression
Advanced notions in modern regression techniques and diagnostics. The underlying theory and concepts associated with estimation methods for handling collinearity. Theory behind modern criteria for selection of candidate models. The development of single and multiple outlier and influence diagnostics. Odd years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6574 - Response Surface Design and Analysis II
Advanced techniques and theory in response surface analysis and design. Robustness of designs. Thorough study of the notion of rotatability. Optimal design criteria and designs for estimating slopes of response surfaces. Mixture designs. Study of model misspecification. Even years. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 6634 (EDRE 6634) - Advanced Statistics for Education
Multiple regression procedures for analyzing data as applied in educational settings, including curvilinear regressions, dummy variables, multicollinearity, and introduction to path analysis. II
Credit Hours: 3
Lecture Hours: 3
Level(s): Graduate
Instruction Type: Lecture
STAT 7994 - Research and Dissertation
Credit Hours: 1 to 19
Level(s): Graduate
Instruction Type: Research
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