1 / 23

Stats 848.3

Dive into multivariate data analysis covering categorical data, correlation structure, linear regression, ANOVA, classification techniques & more. Learn methods like discriminant analysis, cluster analysis, and correlation structure analysis.

paulpearson
Download Presentation

Stats 848.3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Stats 848.3 Multivariate Data Analysis

  2. Instructor: W.H.Laverty Office: 235 McLean Hall Phone: 966-6096 Lectures: M W F 12:30pm - 1:20pm Arts 106 Evaluation: Assignments,Tests - 40% Final Examination - 60%

  3. Course Outline

  4. I Introduction • A brief introduction to multivariate problems

  5. II. Analysis of Multivariate Categorical Data • chi-squared tests; log-linear model • multidimensional contingency tables • fixed margin and logit models • causal (path) analysis for categorical variables

  6. III. Analysis of Correlation Structure • Bivariate correlation • Partial correlation • multiple correlation • canonical correlation

  7. IV. Multivariate Linear Regression Analysis • path analysis

  8. V. Multivariate Analysis of Variance and Covariance • MANOVA and MANOCOVA for factorial experiments • Profile analysis and repeated measures designs

  9. VI. Advanced Analysis of Correlation Structure • Principal Components • Factor analysis

  10. VII. Classification and Grouping Techniques • Discriminant analysis • Cluster analysis

  11. Introduction

  12. Multivariate Data • We have collected data for each case in the sample or population on not just one variable but on several variables – X1, X2, … Xp • This is likely the situation – very rarely do you collect data on a single variable. • The variables maybe • Discrete (Categorical) • Continuous (Numerical) • The variables may be • Dependent (Response variables) • Independent (Predictor variables)

  13. Independent variables Dependent Variables Categorical Continuous Continuous & Categorical Categorical Multiway frequency Analysis (Log Linear Model) Discriminant Analysis Discriminant Analysis Continuous ANOVA (single dep var) MANOVA (Mult dep var) MULTIPLE REGRESSION (single dep variable) MULTIVARIATE MULTIPLE REGRESSION (multiple dependent variable) ANACOVA (single dep var) MANACOVA (Mult dep var) Continuous & Categorical ?? ?? ?? A chart illustrating Statistical Procedures

  14. Multivariate Techniques Multivariate Techniques can be classified as follows: • Techniques that are direct analogues of univariate procedures. • There are univariate techniques that are then generalized to the multivariate situarion • e. g. The two independent sample t test, generalized to Hotelling’s T2 test • ANOVA (Analysis of Variance) generalized to MANOVA (Multivariate Analysis of Variance)

  15. Techniques that are purely multivariate procedures. • Correlation, Partial correlation, Multiple correlation, Canonical Correlation • Principle component Analysis, Factor Analysis • These are techniques for studying complicated correlation structure amongst a collection of variables

  16. Techniques for which a univariate procedures could exist but these techniques become much more interesting in the multivariate setting. • Cluster Analysis and Classification • Here we try to identify subpopulations from the data • Discriminant Analysis • In Discriminant Analysis, we attempt to use a collection of variables to identify the unknown population for which a case is a member

  17. An Example: A survey was given to 132 students • Male=35, • Female=97 They rated, on a Likert scale • 1 to 5 • their agreement with each of 40 statements. All statements are related to the Meaning of Life

  18. Questions and Statements

  19. Statements - continued

  20. Cluster Analysis of n = 132 university students using responses from Meaning of Life questionnaire (40 questions)

  21. Discriminant Analysis of n = 132 university students into the three identified populations

More Related