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Course Criteria
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3.00 Credits
Stochastic processes including discrete, continuous and conditional probability concepts. Definitions and properties of stochastic processes. Markov processes and chains, basic properties, transition matrices and steady state properties. Reliability renewal and queueing processes, expected waiting times, single and multiserver queues.
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3.00 Credits
Analysis of variance including completely randomized design, randomized block design, factorial designs, and interaction; regression analysis including linear regression and multiple regression, model checking and analysis of residuals, and model building; nonparametric statistics; power of a test. Computer application by use of a statistical package with programming capabilities. Major project required.
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3.00 Credits
Statistical distributions; one- and two-population tests about means, including t-tests and paired-difference tests; one- and two-population tests about the variance; contingency tables and goodness-of-fit tests; non-parametric tests; analysis of variance and simple experimental designs; linear regression and residual diagnostics.
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3.00 Credits
Constructing and analyzing statistical experimental designs; blocking, randomization, replication and interaction; complete and incomplete block designs; factorial experiments; repeated measures; confounding effects.
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3.00 Credits
Applied methods in regression analysis. Topics include univariate linear regression, techniques of multiple regression and model building, ANOVA as regression analysis, analysis of covariance, model selection and diagnostic checking techniques, nonlinear regression, and logistic regression.
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3.00 Credits
Design of finite population sample surveys. Stratified, systematic, and multistage cluster sampling designs. Sampling with probability proportional to size. Auxiliary variables, ratio and regression estimators, non-response bias.
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3.00 Credits
Basic graphical techniques and control charts. Experimentation in quality assurance. Sampling issues. Other topics include process capability studies, error analysis, SPRT, estimation and reliability.
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3.00 Credits
Autoregressive, moving average, autoregressive-moving average, and integrated autoregressive-moving average processes, seasonal models, autocorrelation function, estimation, model checking, forecasting, spectrum, spectral estimators.
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3.00 Credits
Techniques and applications of nonparametric statistical methods, estimates, confidence intervals, one sample tests, two sample tests, several sample tests, tests of fit, nonparametric analysis of variance, correlation tests, chi-square test of independence and homogeneity, sample size determination for some nonparametric tests.
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3.00 Credits
Statistical programming techniques, data manipulation, and presentation of analyses using SAS software package.
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