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Course Criteria
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3.00 Credits
I, II, SS; 3 cr (r-N-A). An introduction to regression with emphasis on the practical rather than the theoretical aspects. Begins with fitting a straight line, converts this problem into matrix terms and then proceeds to fitting and evaluation of general linear models. P: Cons inst.
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3.00 Credits
Irr.; 3 cr (N-A). Autocorrelation, elements of spectral analysis; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications in the social sciences and environmental studies. P: Stat 301 or equiv, or cons inst.
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3.00 Credits
Irr.; 3 cr (N-A). Distribution free statistical procedures or methods valid under nonrestrictive assumptions: basic tools; counting methods; order statistics, ranks; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; methods for discrete data with zeros and ties; computer techniques and programs; discussion and comparison with parametric methods. P: Stat 201 or 301 or 224 or cons inst.
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3.00 Credits
I, II; 3 cr (r-N-I). The course will provide students in the life sciences with an introduction to modern statistical practice. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications. P: Math 112 & 113 or Math 114. Open to Fr. Stdts may receive cr for no more than one of the following crses: Stat 201, 224, 301, 324, & 371.
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3.00 Credits
I; 3 cr (N-I). An elementary development of the statistical theory (and methods) used to design and analyze the results from sample surveys. Topics: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, unequal probability sampling, sampling on successive occasions, non-sampling errors, analytical sample surveys. For illustration and clarification, examples drawn from diverse areas of application. P: Stat 224, 201, 301 or an equiv intro statistics course.
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3.00 Credits
II; 3 cr (N-A). Methods of analyzing multidimensional contingency tables, emphasis on practical applications. The use of computing packages for analysis of such data. Model selection, testing goodness of fit, estimation of parameters, measures of association and methods for detecting sources of significance. P: Stat 301 or cons inst.
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3.00 Credits
I, II, SS; 3 cr (N-A). Concepts of randomization, blocking, confounding, transformations, replication; block designs, factorial and fractional methodology, evolutionary operation, and response-surface methodology. P: Stat 224.
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3.00 Credits
Irr.; 3 cr (N-A). Engineering reliability, analysis of failure data, estimates of hazard rates and failure distributions for the reliability of components and/or systems, acceptance sampling plans for quality control. P: Stat 224 or cons inst.
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3.00 Credits
I, II, SS; 3 cr (N-A). Probability in discrete sample spaces; combinatorial analysis; conditional probabilities, stochastic independence, Laplace limit theorem, Poisson distribution, laws of large numbers, random variables, central limit theorem, applications. P: Math 223 or 234.
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3.00 Credits
II; 3 cr (I). Introduction to statistical methods used in pharmaceutical and related biomedical applications. Topics include exploratory data analysis of random samples, theory of probability and population reference distributions, statistical inference and hypothesis testing, regression methods, and survival analysis techniques. P: Admission to School of Pharmacy, Pharm.D. prgm.
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