QSC 254 - Experimental Design

Description
In this course, students learn the statistical concepts of experimental design, starting with the classical approach and working up to the latest experimental design techniques of Taguchi and Shainin. Application and modification of specific experimental designs are also covered.

Credit Hours: 3
Contact Hours: 3
Prerequisites/Other Requirements: None
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: Yes
Course Fees: $15.00
Number of Times Course can be taken for credit:
Programs Where This Course is a Requirement:
None

Other Courses Where This Course is a Prerequisite: None
Other Courses Where this Course is a Corequisite: None
Other Courses Where This course is included in within the Description: None

General Education Requirement:
None
General Education Learner Outcomes (GELO):
NA
Course Learning Outcomes:
1. Explain the difference between Classical, Taguchi, and Shaninin Approaches to experimental design.

2. Calculate simple statistics and delta effects for various variables or factors.

3. Follow the 12 step procedure for conducting a design of experiments.

4. Calculate and interpret Analysis of Variance information.

5. Given orthogonal arrays or linear graphs, a student will be able to select the appropriate design model.

6. Interpret 3-dimensional systems for maximum and minimum system conditions.

7. Setup up simple design models using three different DOE strategies.

8. Calculate and interpret regression analysis for multi-factor models.

9. Explain the Taguchi Philosophy and Methodology.

10. Identify quality sources for data and information pertinent to a problem or issue being examined.

11. Identify the best solution to a problem or issue.
Course Outline:
I. Introduction

II. Design of Experiments Foundation

III. Conducting simple experimental designs and analysis

IV. Design types

V. Statistical Techniques

VI. Analysis of Experimental Data

VII. Taguchi Philosophy, Design, and Analysis

VIII. Understanding the Shainin Approach

Approved for Online and Hybrid Delivery?:
No
Instructional Strategies:
Lecture: 30-50%

Class room projects: 10-20%

Questions and answers: 10-20%

Computer work: 5-20%
Mandatory Course Components:
None
Name of Industry Recognize Credentials: None

Course-Specific Placement Test:
Course Aligned with ARW/IRW Pairing: N/A

Mandatory Department Assessment Measures:
None

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
People Soft Course ID Number: 101560
Course CIP Code: 48.9999
Maximum Course Enrollment:
School:
Department: Manufacturing
Discipline: MN
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details:
None

Major Course Revisions: Prerequisite
Last Revision Date Effective: 20250224T17:16:03
Course Review & Revision Year: 2029-2030