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.)

 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)

 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.
 Prerequisites:
    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.
 Topics:
    1. Introduction to SAS®: overview
    2. Completely Randomized Design: one-way ANOVA, properties and assumptions, statistical model, regression approach, design specifications, evaluating assumptions, power calculations and sample size determination, and nonparametric method
    3. Multiple Comparisons: procedures and properties
    4. Randomized Complete Block Design: partial two-way ANOVA, properties and assumptions, statistical model, regression approach, design specifications and evaluation, and nonparametric method
    5. Latin Square Design: partial three-way ANOVA, properties and assumptions, statistical model, regression approach, design specifications and evaluation
    6. Random Assignment of Treatments to Experimental Units: assignment of treatments with CRD, RBD, and LSD
    7. Completely Randomized Designs with Factorial Treatments: complete two-way ANOVA, properties and assumptions, statistical model, regression approach, design specifications, and extension to k factors
    8. Analysis of Variance for Unbalanced and/or Incomplete Designs: matrix approach to multiple linear regression, application to ANOVA models–estimation, ANOVA, and multiple comparisons
    9. Random-Effects and Mixed-Effects Designs: ANOVA, properties and assumptions, multiple comparisons, statistical model, and design specifications
    10. Nested Designs: ANOVA, properties and assumptions, multiple comparisons, statistical model, and design specifications
    11. Nonparametric Methods
    12. Using Regression Analysis to Perform an Analysis of Variance

 

Revised: February 2011

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