STAT 324 Advanced Statistical Methods (3)
Analysis of covariance, multivariate analysis of variance, discriminant
analysis, stepwise regression, logistic regression, factor analysis, and
introduction to SAS®. (Offered spring semester, even years.)
STAT 292 Statistics for Mathematical Sciences II
- Understanding of basic one-way and two-way ANOVA's and multiple
- Understanding of multiple linear regression including inferential analyses.
- Understanding of two-sample hypothesis test for means and variances.
- Understanding of linear correlation.
- 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)
L. Marlin Eby, Ph. D., Professor of Mathematics and Statistics
- Students majoring in mathematics or minoring in statistics.
- Students wanting a more rigorous advanced statistical methods course.
- This course can be used to meet the elective requirement for mathematics
- To intuitively understand each concept.
- To understand, when possible and appropriate, the rigor of a mathematical
- To integrate topics by identifying commonalties.
- To understand the limitations of each analysis through consideration of
- To express general concepts in terms of the application.
- To communicate results, clearly and completely, in a manner appropriate to
- To use the computer for analysis only after understanding how to perform
the analysis manually.
- Introduction to SAS®: an overview.
- Analysis of Covariance: one-way ANOCOVA–properties and assumptions,
multiple comparisons, statistical model, regression approach; extension to
- Multivariate Analysis of Variance: one-way MANOVA–properties and
assumptions, multiple comparisons, statistical model, comparison of
covariance matrices; extension to two-way MANOVA.
- Discriminant Analysis: properties and assumptions, univariate model
development and validation, multivariate model development and validation,
and stepwise discriminant analysis.
- Stepwise Regression: variable selection techniques in building a multiple
linear regression model.
- Logistic Regression: models and assumptions; model selection, estimation,
and validation; prediction.
- 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