STAT 325 Experimental Design (3)

 Catalog Description:

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.)

    1. Understanding of basic one-way and two-way ANOVA's and a multiple comparison procedure.
    2. Understanding of multiple linear regression including inferential analyses.
    3. Familiarity with one-sample and two-sample nonparametric hypothesis testing.
    4. 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, 6th edition, Duxbury, 2001 (ISBN: 9780495017585)
Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences, 8th edition, Cengage, 2012 (ISBN: 9780538733526)

 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 experimental design 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.
    1. Introduction to SAS: overview
    2. Completely Randomized Designs and One-Way Analysis of Variance
    3. Multiple Comparisons
    4. Randomized Complete Block Designs and Partial Two-Way Analysis of Variance
    5. Latin Square Designs and Partial Three-Way Analysis of Variance
    6. Random Assignment of Treatments to Experimental Units
    7. Completely Randomized Designs with Factorial Treatments and Complete Two-Way Analysis of Variance
    8. Analysis of Variance for Unbalanced and/or Incomplete Designs
    9. Random-Effects and Mixed-Effects Designs
    10. Nested Designs
    11. Nonparametric Methods
    12. Using Regression Analysis to Perform an Analysis of Variance


Revised: October 2013; February 2011

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