Purpose
The four-level sequence of courses in this series is intended to provide engineering, technical, and manufacturing personnel with an in-depth and working knowledge of industrial statistics and experimental design methods and techniques. Various aspects of this sequence combine to provide the most unique series of its type in the country, specifically:
- There are no prerequisites to initiating the series, and the participants are assumed to enter the first course in the series with no prior knowledge of statistical theory;
- The course sequence stresses applied versus theoretical methods and tools;
- The course uses over 85 industrial examples and data sets to explore the tools and methods taught, all of which have been gathered in actual industrial applications; and
- The course sequence is computer-based, to allow for the maximum amount of content to be covered, and ensures that participants will be capable of applying the knowledge and skills acquired in their own positions after the course sequence is successfully completed.
Participants completing the course sequence will be capable of properly gathering and analyzing data, as well as correctly designing and executing experiments in the industrial setting.
Time Requirement
5 days
Number of Participants
A maximum of 45 participants is recommended for this course sequence, with no more than two participants assigned to each computer.
Prerequisites
Experimental Design and Industrial Statistics — Level I
Primary Resource Materials
Experimental Design and Industrial Statistics — Level II manual
Content Outline
- The Seven-Step Procedure for Statistically Testing Hypotheses and Assumptions
- Testing for Differences/Changes in a Single Process or Population
- Changes in Central Tendency
- Changes in Dispersion
- Changes in Proportions
- An Introduction to Correlation and Association: 20 Major Measures (Indices) for Assessing Relationships
- Changes in Correlation
- Assessing Changes in Levels of Association
- Testing for Differences/Changes in Two Processes or Populations
- The Concept of Independent versus Dependent Data Sets: Implications and Constructs
- Testing for Differences in Central Tendency-Independent and Dependent Data
- Testing for Differences in Dispersion-Independent and Dependent Data
- Testing for Differences in Proportions-Independent and Repeated Measures
- Testing for Differences in Two Associative Levels-Independent and Dependent Correlation Coefficients
- In-Class Group Review Activity: A Case Study for the Design of a Small Motor