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 odd years, spring semester.)

 Required Course Materials:

R. Ott and M. Longnecker, An Introduction to Statistical Methods and Data Analysis, 5th edition, ISBN 0-534-25122-6, Duxbury, 2001.

Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences, 7th edition, ISBN 0-495-38217-5, Brooks/Cole, 2008.

 Course Coordinator:

L. Marlin Eby, Ph.D., Associate 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.
 Resources :
  1. Computer labs with MINITAB®, a highly respected and internationally-known interactive system and SAS®, the premier system of its kind in the world–integrating statistical analysis, data management, and report writing.
  2. Murray Library.
  3. Outside speakers.
  4. Discussion of career and graduate school opportunities in Statistics.
  5. Discussion of recommended supporting courses.
 Pedagogy :
  1. Breadth of exposure to topics is desirable, but it should never be realized at the expense of understanding.
  2. Primarily lecture with an open format allowing for substantial instructor-student interaction.
  3. Multiple handouts designed to facilitate the organization and synthesis of topics.
  4. Two-part homework projects: Part 1 manual solution and Part 2 SAS® solution to Part 1 plus an expanded solution facilitated by usage of the computer.
 

Revised: January 2008