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February 2019

Analyzing and Visualizing Interactions in SAS

February 7 @ 9:00 am - 12:00 pm

In regression, we are often interested in an interaction, which is the modification of the effect of an independent variable by another. To understand the magnitude, direction, and significance of the interaction we need to decompose it into simple effects. Simple effects can be analyzed in three ways, by 1) testing each effect against zero, 2) testing differences among effects, and 3) visualizing each effect with graphs. This seminar will demonstrate how to do all three analyses in SAS PROC…

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Meta-analysis in Stata

February 14 @ 9:00 am - 12:00 pm

Meta-analysis is the synthesis of results from previous studies.  It is used to increase power, obtain a better estimate of an effect size, and sometimes to resolve conflicting conclusions in the literature.  In this workshop, we will discuss how the data for a meta-analysis are collected and organized, as well as how such data are analyzed and graphed.  We will also discuss some of the limitations meta-analysis and what should be included in a meta-analysis for publication.  The sign-up link…

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Walk-in consulting closed

February 18
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Introduction to Regression in R

February 21 @ 9:00 am - 12:00 pm

Regression analysis is one of the most powerful statistical techniques that is used to explain variability in a response (dependent) variable as a function of one or more predictor (explanatory or independent) variables.  The aim of this seminar is to help participants increase their skills in using regression analysis with R.  The seminar does not teach regression, per se, but focuses on how to perform regression analyses using R. However, it gives a very brief review on the theoretical background as necessary.  It…

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R Markdown Basics

February 28 @ 9:00 am - 12:00 pm

R Markdown files integrate text, Markdown, and R code into dynamic documents that weave together plain text, formatted text, and the output of the R code.  The resulting dynamic reports can be produced in many formats, including HTML documents, HTML slideshows, LaTeX pdf, Beamer slideshows, MS Word doc, books, scientific articles, and websites.   This seminar covers basic coding and conventions of the 3 frameworks upon which R Markdown depends:  Markdown for formatting text, knitr for R code chunks, and YAML…

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March 2019

Walk-in consulting closed

March 18 - March 22
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April 2019

Introduction to ggplot2

April 18 @ 9:00 am - 12:00 pm

Description:  The ggplot2 package is a widely-used and well-supported system for creating eye-catching graphics in R.  In this interactive seminar, you will learn the underlying grammar of graphics that forms the philosophical framework of ggplot2, giving you the power to create publication-quality figures intuitively.  The workshop is interactive, in which attendees are encouraged to participate in R coding to create their own statistical graphics. The workshop will be held in Math Sciences 5628 (the IDRE Visualization Portal) The notes for…

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walk-in consulting closed 4/23

April 23
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Causal Inference for Complex Observational Data

April 23 @ 9:00 am - 10:30 am

Observational data often have issues which present challenges for the data analyst.  The treatment status or exposure of interest is often not assigned randomly.  Data are sometimes missing not at random (MNAR) which can lead to sample selection bias.  And many statistical models for these data must account for unobserved confounding.  This talk will demonstrate how to use standard maximum likelihood estimation to fit extended regression models (ERMs) that deal with all of these common issues alone or simultaneously.

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Introduction to Bayesian Statistics Using Stata

April 23 @ 10:30 am - 11:30 am

Bayesian analysis has become a popular tool for many statistical applications. Yet many data analysts have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC)…

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