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.)
- Understanding of basic one-way and two-way ANOVA's and a multiple
comparison procedure.
- Understanding of multiple linear regression including inferential
analyses.
- Familiarity with one-sample and two-sample nonparametric hypothesis
testing.
- Ability to use the computer to perform statistical analyses.
Required Course Materials: |
R. Ott and M. Longnecker, An Introduction to Statistical Methods and
Data Analysis, 6^{th} edition, Duxbury, 2001 (ISBN: 9780495017585)
Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences,
8^{th} edition, Cengage, 2012 (ISBN: 9780538733526)
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
majors.
- To intuitively understand each concept.
- To understand, when possible and appropriate, the rigor of a mathematical
proof.
- To integrate topics by identifying commonalties.
- To understand the limitations of each analysis through consideration of
assumptions.
- To express general concepts in terms of the application.
- To communicate results, clearly and completely, in a manner appropriate to
nonquantitative audiences.
- To use the computer for analysis only after understanding how to perform
the analysis manually.
- Introduction to SAS^{®}: overview
- Completely Randomized Designs and One-Way Analysis of Variance
- Multiple Comparisons
- Randomized Complete Block Designs and Partial Two-Way Analysis of Variance
- Latin Square Designs and Partial Three-Way Analysis of Variance
- Random Assignment of Treatments to Experimental Units
- Completely Randomized Designs with Factorial Treatments and Complete Two-Way Analysis of Variance
- Analysis of Variance for Unbalanced and/or Incomplete Designs
- Random-Effects and Mixed-Effects Designs
- Nested Designs
- Nonparametric Methods
- Using Regression Analysis to Perform an Analysis of Variance
Revised: October 2013; February 2011
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