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Course Criteria
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3.00 Credits
Prerequisite: MATH 263; STAT 203 or 335 This course provides a calculus based introduction to probability theory, including topics such as combinatorial analysis, random walk, conditional probability, and a variety of statistical distributions. Outcome: Students obtain the theoretical background in probability needed for further study in probability and statistics.
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3.00 Credits
Prerequisite: STAT 304 This course is a continuation of Probability and Statistics I and applies the techniques of calculus and probability to the study of advanced topics in statistics. Outcome: Students obtain the theoretical background in statistics needed for graduate level work in probability and statistics.
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3.00 Credits
Prerequisite: MATH 212; STAT 203 or 335 This course discusses topics such as finite-state Markov processes and Markov chains, classification of states, long-run behavior, continuous time processes, birth and death processes, random walks, and Brownian motion. Outcome: Students will obtain a background in stochastic processes that will allow them to apply them in areas like genetics, population growth, inventory, cash management, and gambling theory.
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3.00 Credits
Prerequisite: STAT 203 or 335 This course discusses comparative experiments, analysis of variance, fixed and random effects models, and a variety of design models; packaged computer programs such as SPSS or SAS will be used extensively. Outcome: Students will obtain the background in statistical design and analysis of experiments needed to apply them in their own areas of interest.
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3.00 Credits
Prerequisite: 203 or 335 This course discusses simple and multiple linear regression methods, multiple comparison estimation procedures, residual analysis, and other methods for studying the aptness of a proposed regression model; packaged computer programs such as SPSS and SAS will be used extensively Outcome: Students will obtain an extensive background in the applications of regression analysis.
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3.00 Credits
Prerequisite: STAT 203 or 335 An introduction to modern-day extensions of simple linear regression and ANOVA to the chi-square test including logistic regression and log-linear modelling techniques based on generalized linear models. Methods for matched-pair, small datasets, ordinal and multi-category data also discussed. This course focuses on applications using real-life data sets, and uses popular software packages.
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4.00 Credits
Prerequisite: BIOL 102; MATH 132 or 162 This course provides an introduction to the statistical methods used in designing biological experiments and in data analysis, including computer laboratory assignments with biological data. Outcome: Students interested in research in the life sciences will obtain a background in the appropriate use of statistical methods as an experimental tool.
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3.00 Credits
Prerequisite: STAT 335 This course provides an overview of advanced topics in applied statistics with particular application in biology and medicine, including the interpretation of Minitab, SAS and S-Plus computer output. Outcome: Students interested in applied statistical methods will obtain skill in experimental design, linear regression, categorical data analysis and logistic analysis, nonlinear regression, bioassay and drug synergy methods, as well as multivariate and survival statistical methods.
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4.00 Credits
Prerequisite: STAT 203 or 335 This course develops the mathematical and statistical methods necessary to analyze and interpret genomic and proteomic data, including signal analysis, sequence alignment methods, data-base search methods useful in bioinformatics and data mining. Outcome: Students will obtain the quantitative skills used in BLAST, including inference, stochastic processes and hidden Markov models, random walks, microarray analysis and biological sequence analysis.
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3.00 Credits
Prerequisite: COMP 125 or 170; STAT 203 or 335 This course uses simulation languages such as AWESIM and SAS/QSIM to model probabilistic discrete event systems such as queuing systems, financial systems and biological systems. Outcome: Students will obtain an understanding of using technology to model probabilistic systems for which there is no closed form expressions for their evolution.
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