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Course Criteria
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3.00 Credits
This course serves as an introduction to Generalized Linear Models (GLMs). It instructs students in a unifying theory that combines the areas of linear models, non-linear models, regression, categorical data, and analysis of variance. Prerequisites: All other biostatistics courses except Time-To-Event Data Analysis STAT 8320. 3.000 Credit Hours 3.000 Lecture hours 0.000 Lab hours 0.000 Other hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
Introduction to statistical methods in the analysis of DNA and protein sequence data. This course exposes students to applications of statistical theory to assembling biological sequences, making inferences about single sequences, and comparing two or more sequences. Statistical foundations of BLAST tests are covered. 3.000 Credit Hours 3.000 Lecture hours 0.000 Lab hours 0.000 Other hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
The statistical analysis of complex phenotypes. Topics include genotypic value, genetic variance, and linear models. Environmental variance, genotype by environment interaction, threshold models and generalized linear mixed models, mapping quantitative trait loci (QTL), and variance component estimation. 3.000 Credit Hours 3.000 Lecture hours 0.000 Lab hours 0.000 Other hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
This course provides a hands-on exposure to programming, data management and report generation with one of the most popular statistical software packages. Prerequisite: College algebra 2.000 Credit Hours 2.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
Fundamentals of random variables and probability theory; discrete and continuous distributions; exponential families; joint, marginal, and conditional distributions; functions of random variables; transformation and change of variables; order statistics; convergence concepts; central limit theorem; sampling distributions. Prerequisites: Multivariable Calculus and Matrix Algebra. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
Introduction to modeling and analyzing expression data of microarrays. Methods of cluster analysis will be covered as ways to attempt to group genes of the same biochemical pathways together. Students will also learn to test hypotheses related to microarray designs, with emphasis on determining which genes are differentially expressed between two populations. 3.000 Credit Hours 3.000 Lecture hours 0.000 Lab hours 0.000 Other hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
Advanced statistical analyses specific for medical and health data and designs involving humans. Topics included are linkage analyses, association studies, linkage disequilibrium mapping, segregation analyses, and gene and environment interaction. 3.000 Credit Hours 3.000 Lecture hours 0.000 Lab hours 0.000 Other hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
This course will continue the investigation of simple linear regression from the introduction to Biostatistics course with extension to multiple linear regression models. Model selection, validation, diagnostics and remedial measures will be covered. SAS will be used for applying these methods to biomedical data. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
Point and interval estimation; hypothesis and significance testing maximum likelihood and moment estimators; Bayes estimators; unbiased estimators; sufficiency and completeness; Fisher information; uniformly most powerful tests; likelihood ratio tests; asymptotic inference; introduction to Bayesian inference. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
One-way analysis of variance (ANOVA), multiple treatment comparisons, ANOVA diagnostics, factorial ANOVA, randomized complete block designs, analysis of covariance (ANCOVA), ANOVA with unbalanced data, random and mixed effect models, repeated measures designs, nested designs and response surface methods. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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