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LISA Statistics Short Course: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP
(Research)
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 2016 Schedule:
Tuesday, March 15, 4:00-6:00 pm: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size;
Tuesday, March 22, 4:00-6:00 pm: Data Analytics - Classification;
Tuesday, March 29, 4:00-6:00 pm: Basics of R;
Tuesday, April 5, 4:00-6:00 pm: Statistical Analysis Using R;
Tuesday, April 12, 4:00-6:00 pm: Better Data Visualization in R Using the ggplot2 Package;
Tuesday, April 19, 4:00-6:00 pm: Introduction to Web Scraping in R;
Tuesday, April 26, 4:00-6:00 pm: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP;
Tuesday, April 26, 4:00-6:00 pm;
Location: 1100 Torgersen Hall;
Instructor: Sumin Shen;
Title: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP;
Multivariate Analysis of Variance (MANOVA) is a way to test hypothesis on two or more dependent variables. For example, you might want to test the hypothesis that four different teaching methods have the same effect on both the students' attendance and acquisition from the class. The primary goal of this short course is to provide a guide to MANOVA for researches who are interested in multivariate method analysis.
In univariate analysis of variance (ANOVA), we are looking for the effect of factors (a.k.a. predictors) on a single dependent variable. The question you are interested is whether or not there is difference in the single dependent variable in terms of the factors. MANOVA is an extension of ANOVA in terms of the number of dependent variables. When there are more than one dependent variable, MANOVA is one of the methods to be considered. The JMP software will be used in this course.
This course covers:
1. ANOVA and MANOVA
2. Differences between ANOVA and MANOVA
3. When should I use MANOVA?
4. MANOVA In JMP
Data sets:
1. ANOVA in a study where 33 subjects were administered three different types of analgesics (A, B, and C). The subjects were asked to rate their pain levels on a sliding scale. The study interest is to find out the effect of types of analgesics and gender on the rating scale. The data set is the Analgesics.jmp in Sample Data Library in JMP.
2. MANOVA in a multiple response model where the response variables are distances traveled and durability for three bands of golf balls. In this study, a robotic golfer hit a random sample of ten balls of each brand in a random sequence. The data set is the Golf Balls.jmp in Sample Data Library in JMP.
3. MANOVA in repeated measures where the response variables are measured at several points over time. In this study, the cholesterol is measured every one month over a 6-month period. a new drug, which is supposed to reduce cholesterol, and a placebo is applied in the study. The research interest is whether or not there is a treatment (drug) effect over time. The data set is in the folder.
Below is a result from the repeated measures example by MANOVA in JMP.
www.lisa.stat.vt.edu/sites/default/files/images/2016-04-26-MANOVA.png
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540-231-8354