STAT 325 Experimental Design (3)
Experimental design and analysis for a variety of problems: completely
randomized, randomized complete block, Latin square, completely randomized with
factorial treatments, unbalanced and/or incomplete, random effects, mixed
effects, nested; multiple comparisons; introduction to SAS®.
Prerequisite: STAT 292. (Offered spring semester, odd years.)
| Required Course Materials:
R. Ott and M. Longnecker, An Introduction to Statistical Methods and
Data Analysis, 6th edition, Duxbury, 2001 (ISBN: 9780495017585)
Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences, 7th edition, Brooks/Cole, 2008 (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 experimental design 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.
- Understanding of basic one-way and two-way ANOVA's and a multiple
- Understanding of multiple linear regression including inferential
- Familiarity with one-sample and two-sample nonparametric hypothesis
- Ability to use the computer to perform statistical analyses.
- Introduction to SAS®: overview
- Completely Randomized Design: one-way ANOVA, properties and assumptions,
statistical model, regression approach, design specifications, evaluating
assumptions, power calculations and sample size determination, and
- Multiple Comparisons: procedures and properties
- Randomized Complete Block Design: partial two-way ANOVA, properties and
assumptions, statistical model, regression approach, design specifications
and evaluation, and nonparametric method
- Latin Square Design: partial three-way ANOVA, properties and assumptions,
statistical model, regression approach, design specifications and
- Random Assignment of Treatments to Experimental Units: assignment of
treatments with CRD, RBD, and LSD
- Completely Randomized Designs with Factorial Treatments: complete two-way
ANOVA, properties and assumptions, statistical model, regression approach,
design specifications, and extension to k factors
- Analysis of Variance for Unbalanced and/or Incomplete Designs: matrix
approach to multiple linear regression, application to ANOVA models–estimation,
ANOVA, and multiple comparisons
- Random-Effects and Mixed-Effects Designs: ANOVA, properties and
assumptions, multiple comparisons, statistical model, and design
- Nested Designs: ANOVA, properties and assumptions, multiple comparisons,
statistical model, and design specifications
- Nonparametric Methods
- Using Regression Analysis to Perform an Analysis of Variance
Revised: February 2011
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