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  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: STAT 331 or departmental permission. Topics include point and interval estimation, tests of hypotheses, introduction to linear models, likelihood techniques, and regression and correlation analysis.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: STAT 431/531. Sampling from finite populations is discussed. Topics such as simple random sampling, stratified random sampling and ratio and regression estimation are included. Also discussed are aspects of systematic sampling, cluster sampling, and multi-stage sampling.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 330 or 310W-331 or 431/531. Suggested corequisite: STAT 405/505. Topics include experiments with a single factor, multiple comparisons, randomized blocks, Latin squares, incomplete block designs, multifactor factorial experiments, fractional replications, nested designs, experiments to study variance: random and mixed effects, and split plot designs.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 330 or 310W or 431/531. Suggested corequisite: STAT 405/505. Topics include theory of least squares, simple linear regression, multiple regression (including its matrix formulation), applications of these techniques to real life data, residual analysis, selection of variables, multicollinearity issues, regression on dummy variables, and analysis of covariance.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 431/531. An introduction to statistical methods used in the design, conduct, and analysis of clinical trials. Topics include: study designs, treatment allocation, sample size and power, clinical life tables, log rank test, cross-over designs, and sequential methods of monitoring clinical trials.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 310W or 330 or permission of the instructor. Topics include basic probability distributions, modeling the number of exceedances using the binomial and Poisson distributions, modeling environmental data using the normal process, environmental monitoring, impact assessment, assessing site reclamation, concept of autocorrelation, diffusion and dispersion of pollutants, distributions with respect to space and time, applications to measuring indoor air quality, water quality, etc. Emphasis will be on the applications of these tools to environmental data using statistical software.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 431/531. Suggested corequisite: STAT 405/505. Topics include general linear models, the weighted least squares (WLS), the maximum likelihood (ML), the restricted maximum likelihood (REML) methods of estimation, analysis of continuous response repeated measures data, parametric models for covariance structure, general estimating equations (GEE) and quasi least squares (QLS), models for discrete longitudinal data: marginal, random effects, and transition models. Limitations of existing approaches will be discussed. Emphasis will be on the application of these tools to data related to the biological and health sciences. Methods will be implemented using statistical software.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: STAT 330 or 331 or departmental permission. Topics include the theory and applications of binomial tests and rank tests, including the tests of McNemar, Mann-Whitney, Friedman, Kruskal-Wallis, and Smirnov.
  • 3.00 Credits

    Lecture 3 hours; 3 credits. Prerequisite: A grade of C or better in STAT 431/531. Suggested corequisite: STAT 405/505. Topics include relative risk and odds ratio measures for 2 x 2 tables, the chi-square and Mantel-Haenszel tests, Fisher?s exact test, analysis of sets of 2 x 2 tables using Cochran-Mantel-Haenszel methodology, analysis of I x J and sets of I x J tables for both nominal and ordinal data, logistic regression including the logit and probit models, and building and applying loglinear models. Emphasis will be on the application of these statistical tools to data related to the health and social sciences. Interpretation of computer output will be stressed.
  • 0.00 - 3.00 Credits

    No course description available.
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