BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IDRE Stats - ECPv4.6.23//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IDRE Stats
X-ORIGINAL-URL:https://stats.idre.ucla.edu
X-WR-CALDESC:Events for IDRE Stats
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190207T090000
DTEND;TZID=America/Los_Angeles:20190207T120000
DTSTAMP:20190123T220041
CREATED:20190103T154851Z
LAST-MODIFIED:20190103T154851Z
UID:30985-1549530000-1549540800@stats.idre.ucla.edu
SUMMARY:Analyzing and Visualizing Interactions in SAS
DESCRIPTION: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 GLM / PLM for the following regression models: quadratic\, continuous-by-continuous\, categorical-by-continuous\, categorical-by-categorical\, and categorical-categorical-continuous interaction. The notes for the workshop are here. The sign-up link is here. \n
URL:https://stats.idre.ucla.edu/event/analyzing-and-visualizing-interactions-in-sas/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190214T090000
DTEND;TZID=America/Los_Angeles:20190214T120000
DTSTAMP:20190123T220041
CREATED:20190103T154929Z
LAST-MODIFIED:20190103T154929Z
UID:30987-1550134800-1550145600@stats.idre.ucla.edu
SUMMARY:Meta-analysis in Stata
DESCRIPTION: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 is here. \n
URL:https://stats.idre.ucla.edu/event/meta-analysis-in-stata/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190218
DTEND;VALUE=DATE:20190219
DTSTAMP:20190123T220041
CREATED:20190103T155209Z
LAST-MODIFIED:20190103T155209Z
UID:30995-1550448000-1550534399@stats.idre.ucla.edu
SUMMARY:Walk-in consulting closed
DESCRIPTION:
URL:https://stats.idre.ucla.edu/event/walk-in-consulting-closed-8/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190221T090000
DTEND;TZID=America/Los_Angeles:20190221T120000
DTSTAMP:20190123T220041
CREATED:20190103T155007Z
LAST-MODIFIED:20190103T155007Z
UID:30989-1550739600-1550750400@stats.idre.ucla.edu
SUMMARY:Introduction to Regression in R
DESCRIPTION: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 is assumed that participants have had at least a one quarter/semester course in regression (linear models) or a general statistical methods course that covers simple and multiple regression and have access to a regression textbook that explains the theoretical background of the materials covered in this seminar. These materials also assume basic familiarity with R\, for example that you have taken the Introduction to R seminar or have equivalent knowledge of R. The seminar begins with simple regression and generalize the methods to multiple regression\, followed by regression diagnostics\, regression with categorical predictors\, and finally a quick look at the generalized linear model and logistic regression. The sign-up link is here. \n
URL:https://stats.idre.ucla.edu/event/introduction-to-regression-in-r/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190228T090000
DTEND;TZID=America/Los_Angeles:20190228T120000
DTSTAMP:20190123T220041
CREATED:20190103T155046Z
LAST-MODIFIED:20190103T155046Z
UID:30991-1551344400-1551355200@stats.idre.ucla.edu
SUMMARY:R Markdown Basics
DESCRIPTION: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 for rendering the document. The seminar does not assume any previous experience with R Markdown\, but attendees who wish to participate in seminar demonstrations should come with RStudio and R Markdown installed on their computers. The sign-up link is here. \n
URL:https://stats.idre.ucla.edu/event/r-markdown-basics/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190318
DTEND;VALUE=DATE:20190323
DTSTAMP:20190123T220041
CREATED:20190103T155341Z
LAST-MODIFIED:20190103T155341Z
UID:30997-1552867200-1553299199@stats.idre.ucla.edu
SUMMARY:Walk-in consulting closed
DESCRIPTION:
URL:https://stats.idre.ucla.edu/event/walk-in-consulting-closed-9/
END:VEVENT
END:VCALENDAR