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LISA Statistics Short Course: Statistical Analysis in R
(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.
Summer 2014 Schedule:
Wednesday, June 18: Basics of R;
Wednesday, June 25: Statistical Analysis in R;
Wednesday, July 2: Graphics in R;
Wednesday, July 9: T-tests, ANOVA, and Rank Based Tests;
Wednesday, July 16: SQL in R;
Wednesday, July 23: Parametric Versus Semi/Nonparametric Regression Models;
Wednesday, July 30: Fractorial Experiments: Blocking, Confounding, and Fractional Factorial Designs;
Wednesday, June 25;
Instructor: Ian Crandell;
Title: Statistical Analysis in R;
Course Information:
R is a free computing and graphical software/environment for statistical analysis. This short course is designed to provide a basic statistical analysis in R using two data. The first data set comes from a study of low birth weights. This set was collected at Baystate Medical Center, Springfield, Massachusetts and includes 189 observations. We are interested in understanding the variables that predict the likelihood of a mother giving birth to a baby with low-birth weight (defined as a baby weighing less than 2500 grams). The prediction variables are the mother's age, smoking and hypertension status, and the weight of the mother. Analyses described include t-tests (one-sample, two sample, paired tests), ANOVA, generalized linear regression (logistic regression), and nonparametric tests. The second data set summarizes the heart attacks among 275 males, where the binary response is whether or not a person has heart attack. There are six continuous predictors: age, systolic blood pressure, diastolic blood pressure, cholesterol level, height (inches), and weight (pounds).
Both of these data sets can be found at the following links and will be provided in the course.
http://www.umass.edu/statdata/statdata/stat-logistic.html
http://coedpages.uncc.edu/cpflower/rsch8140/logistic_regression_example.htm
The course format includes a lecture portion covering statistical concepts, and the computer laboratory component covers usage of R to perform the analyses described above. The attendee can write, modify, and execute R codes for the statistical analysis.
This class is the second in a three-course series that assumes no previous coding experience in R or any other language. Experience using R or attending Part I of this series is suggested but not required for this course. The intended audience for this course includes researchers who want to gain basic exposure to statistical analysis in R with the ultimate goal of incorporating R into their research programs.
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540-231-8354