4:00pm to 6:00pm |
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LISA Statistics Short Course: Advanced Topics in R: parallel processing, structural equation modeling, and the bootstrap
(Academic)
LISA SHORT COURSES IN STATISTICS
LISA (Virginia Tech's Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data. See www.lisa.stat.vt.edu/?q=short_courses for instructions on how to REGISTER and to learn more.
Spring 2014 Schedule:
Tuesday & Thursday, February 11 & 13: Basics of R;*
Tuesday & Thursday, February 18 & 20: Statistical Analysis in R;*
Tuesday & Thursday, February 25 & 27: Graphics in R;*
Tuesday & Wednesday, March 4 & 5: Introduction to JMP;*
Tuesday, March 18: Advanced Topics in R: parallel processing, structural equation modeling, and the bootstrap;
Tuesday, April 1: Survey Design and Analysis;
Tuesday, April 10: Accelerating statistical calculations using inexpensive graphics cards;
Tuesday, April 15: Multiple Imputation and Missing Data;
*Two sessions of the same course to accommodate more attendees.
Tuesday, March 18;
Instructor: Ian Crandell;
Title: Advanced Topics in R: parallel processing, structural equation modeling, and the bootstrap;
Course Information:
R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the snowfall package, applications of the bootstrap, and structural equation modeling (SEM). The course will cover the basic theory and application of the bootstrap, show how it can be parallelized across a single multicore machine, and conclude with an application of the bootstrap to SEM. These techniques will be illustrated using a dataset of cognitive abilities, personality traits, and vocational interests of 8- subjects. The prerequisites for the course are a basic familiarity with R, familiarity with the apply suite of functions, and the ability to define one's own functions.
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