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CourseBios312Objectives
(02 Jan 2013,
ChrisSlaughter
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---+++ Course Goals * Learn how to use modern regression methods to answer scientific questions * Become familiar with statistical concepts including exploratory data analysis, estimation, testing in linear, logistic, and survival models * Understand how the development of statistical methodology is motivated by biological and medical problems * Develop data analytic skills including familiarity with several statistical programs * Develop writing skills needed to communicate the results of a data analysis ---+++ Topics of Discussion * Introduction to Regression Models * Simple Linear Regression * A Review of Matrix Algebra and Important Results of Random Vectors * Precision, Effect Modification, and Confounding * Specification Issues in Regression Models: ANCOVA, ANOVA, Multicollinearity * Multivariable Regression * Model Selection * Case Studies in Linear Regression * Logistic Regression * Generalized Linear Models and Poisson Regression * Survival models * Bayesian Regression * Model Checking: diagnostics, transformations, influential observations, lack-of-fit test ---++ Software <!-- Stata 12: Powerful, with a good graphical user interface. UCLA has a good support site: http://www.ats.ucla.edu/stat/stata/. [$89 for a year and $145 for life, through GradPlan at http://www.stata.com/order/new/edu/gradplans/gp-campus.html. Buy Small Stata for $45 if you have to pay by yourself. Stata also is available at the College of Arts & Science Microcomputer Labs.]--> * You can use any software your want other than Excel. We will be demonstrating Stata and R. For Bayesian analysis, R and !JAGS will be used. * See SoftwareRecs for more software recommendations * Chun Li's [[ChunStataNotes][Stata notes]], [[Need2KnowCommands][Need-to-know commands]], and [[ChunRNotes][R notes]] * Leena Choi's [[LeenaStataNotes][Stata notes for classes]], [[IntroBiostatCourse2007StataLab][Stata Lab info]], and [[LeenaRNotes][R notes for classes]] ---+++ R information * R 2.8 (optional, free from http://www.r-project.org): Powerful, versatile, and actively maintained and updated. It may require a longer learning curve than Stata and SPSS, but the effort will pay off later on. To get a feel, look at one of the following: [[http://cran.r-project.org/doc/manuals/R-intro.pdf 1]] (and try the commands in "A Sample Session“) [[http://cran.r-project.org/other-docs.html 2]] [[RS][3]]. The Department of Biostatistics has a [[RClinic][free R Clinic]] every Thursday. Print the R [[http://www.rpad.org/Rpad/Rpad-refcard.pdf reference card]] to get a list of the most commonly used commands. * To download R: [[http://cran.wustl.edu/bin/linux Linux]] [[http://cran.wustl.edu/bin/macosx Mac]] [[http://cran.wustl.edu/bin/windows/base Windows]] * [[http://www.rstudio.com/][R Studio]] is a powerful and easy to use interface for R. I highly recommend it. ---+++ Comments on Other Software Packages * Stata: Powerful and good graphics with an SPSS-like menu system. A good support site is at http://www.ats.ucla.edu/stat/stata. Cost is $89 for a year and $145 for life, through [[http://www.stata.com/order/new/edu/gradplans/gp-campus.html GradPlan]]. Buy Small Stata for $45 if you have to pay by yourself. Stata also is available at the [[http://www.vanderbilt.edu/cas/computers/labinfo][College of Arts & Science Microcomputer Labs]]. * SAS: The oldest survivor, with strong legacy. Hard to learn and extend, with outdated structure and the worst graphics of any major package. * SPSS: Have “standard” methods and good graphical user interface. However, it is difficult to extend beyond the “standard” methods. * Honorable mention: Epi Info (free from CDC), S-Plus. * There is a long list of reasons not to use Excel. See ExcelProblems * See StatComp for more information about statistical computing including links to several online statistical and probability calculators -- Main.ChrisSlaughter - 07 Dec 2009
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Topic revision: r6 - 02 Jan 2013,
ChrisSlaughter
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