The Department of Mathematics and Statistics proposes the following course for inclusion in University Studies, Unity and Diversity, Critical Analysis at Winona State University. This was approved by the full department on Thursday, January 4, 2001.

Course: Industrial Design of Experiments I (STAT 321) 3 s.h.

This is an existing course, previously approved by A2C2.

Department Contact Person for this course:

Dan Rand; email drand@winona.edu

Discussion of relevance of Critical Analysis objectives (a, b, c, d below) for STAT 321:

1. evaluate the validity and reliability of information;

This course concerns itself with designing the collection of data, and then analyzing it. There is an emphasis on obtaining valid, reliable data through repeated measures. Methods for recognize outliers, or invalid data, are included.

2. analyze modes of thought, expressive works, arguments, explanations, or theories;

The nature of the course is methods for testing of hypotheses. Critiques of data analysis and conclusions reached by others is required through case studies.

c. recognize possible inadequacies or biases in the evidence given to support arguments or conclusions;

In testing data to support hypotheses, a step is taken to quantitatively assign the risk of reaching wrong decisions. Mathematical models for explaining the relationship of input to output variables are analyzed for lack of fit to collected data. This lack of fit includes inadequacies and biases.

d. advance and support claims.

The scientific method is fully utilized in this course:

Conclusions are drawn based on statistical evidence.

DEPARTMENT OF MATHEMATICS AND STATISTICS

Course Outline / Syllabus -Statistics 321

Course Title: Industrial Design of Experiments I

Number of Credits: 3

STAT 321 is a University Studies Course satisfying requirements for Critical Analysis. Following is a list of the Critical Analysis objectives that will be covered in this course, with reference to "Topics Covered" below, by Roman numeral:

a. evaluate the validity and reliability of information by measuring variability in data (topics II, III, IV, V, VI, VII);

b. analyze modes of thought, expressive works, arguments, explanations, or theories by performing hypothesis tests by analysis of experimental data (topics II, III, VII);

c. recognize possible inadequacies or biases in the evidence given to support arguments or

conclusions by assigning risks to decision-making (topics II, III, IV);

d. advance and support claims by analysis of experimental data that may support or negate hypotheses (topics II, III, V, VI, VII, VIII).

Possible Textbooks:

Montgomery, Douglas C., Design and Analysis of Experiments. New York, Wiley, 1997.

Wheeler, Donald J., Understanding Industrial Experimentation. Knoxville, Tennessee: Statistical Process Controls, Inc., 1988

Topics Covered: I. Introduction to design of experiments

II. Review of simple comparative experiments

III. Introduction to ANOVA
      A. Basic fixed model
      B. Model checking

IV. Introduction to two-factor (fixed effects model) factorial designs
      k

V. Introduction to the 2 factorial design

VI. Two level fractional factorial designs
      A. 1/2 and 1/4 fraction
            k-p
      B. General 2 fraction
      C. Resolution of designs
      D. Yates' Algorithm

VII. Introduction to response surface methods and EVOP [AS TIME PERMITS]
        k

VIII. Introduction to 3 factorial designs and fractional designs

IX. Introduction to nested designs

Method of Instruction: Lectures, case studies, discussion and problem solving sessions, computer session(s) and industrial plant session(s) (when available).

Evaluation Procedure: Hour exams/quizzes/final/homework

Dean's Signature Date