STAT 324 Advanced Statistical Methods (3)

 Catalog Description:

Analysis of covariance, multivariate analysis of variance, discriminant analysis, stepwise regression, logistic regression, factor analysis, and introduction to SAS®. (Offered spring semester, even years.)

 Prerequisites:

STAT 292 Statistics for Mathematical Sciences II

  1. Understanding of basic one-way and two-way ANOVA's and multiple comparison procedure.
  2. Understanding of multiple linear regression including inferential analyses.
  3. Understanding of two-sample hypothesis test for means and variances.
  4. Understanding of linear correlation.
  5. Ability to use the computer to perform statistical analyses.
 Required Course Materials:

There is no new text for this course.

Prerequisite course material:  J. Devore, Probability and Statistics for Engineering and the Science, 7th edition, Brooks/Cole, 2007 (ISBN 0-495-38217-5)

 Course Coordinator:

L. Marlin Eby, Ph. D., Professor of Mathematics and Statistics

 Course Audience:
    1. Students majoring in mathematics or minoring in statistics.
    2. Students wanting a more rigorous advanced statistical methods course.
    3. This course can be used to meet the elective requirement for mathematics majors.
 Course Objectives:
    1. To intuitively understand each concept.
    2. To understand, when possible and appropriate, the rigor of a mathematical proof.
    3. To integrate topics by identifying commonalties.
    4. To understand the limitations of each analysis through consideration of assumptions.
    5. To express general concepts in terms of the application.
    6. To communicate results, clearly and completely, in a manner appropriate to nonquantitative audiences.
    7. To use the computer for analysis only after understanding how to perform the analysis manually.
 Topics:
    1. Introduction to SAS®: an overview.
    2. Analysis of Covariance: one-way ANOCOVA–properties and assumptions, multiple comparisons, statistical model, regression approach; extension to two-way ANOCOVA.
    3. Multivariate Analysis of Variance: one-way MANOVA–properties and assumptions, multiple comparisons, statistical model, comparison of covariance matrices; extension to two-way MANOVA.
    4. Discriminant Analysis: properties and assumptions, univariate model development and validation, multivariate model development and validation, and stepwise discriminant analysis.
    5. Stepwise Regression: variable selection techniques in building a multiple linear regression model.
    6. Logistic Regression: models and assumptions; model selection, estimation, and validation; prediction.
    7. Factor Analysis: covariance and correlation matrices; properties and assumptions; model; principal components factor analysis–determination of number of factors, factor extraction, rotation, and interpretation.
 

Revised: August 2010