Statistics for Quality Program 1: Comparative Experiments - Demonstrating Change or Equivalence
A LifeScience Alley Featured Program
3/19/2010 |
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Time
Registration: 7:30 am - 8:00 am
Program: 8:00 am - 11:30 am
Event Description
Statistics for Quality is a series of programs geared toward technical individuals who deal with data and make decisions based upon data. This includes scientists, engineers, technicians, and personnel from research and development, manufacturing, quality, analytical, engineering, supply management, and regulatory affairs departments. Statistics ''green belts'' and ''black belts'' will also benefit. If you wish to register for all six Statistics for Quality programs and save 10%, please follow the bundle link above.
Program 1: Comparative Experiments - Demonstrating Change or Equivalence
March 19, 2010
A common situation in industry is to conduct experiments to demonstrate that a change, perhaps an improvement, has occurred, or that no change has occurred, thereby establishing equivalence. Thus, comparative experiments are conducted to show that the new process has less variability than the old, that the new formulation is better than the old, or to show that two product designs or two measurement methods are equivalent. Such problems are often analyzed using a t-test, an F-test or an ANOVA. Such hypothesis tests, when blindly applied, can lead to disastrous results. For one company, this SOP caused an unnecessary delay of three months and an unnecessary expenditure of over $250,000!
The purpose of this seminar is to provide practical guidance to conduct comparative experiments, and answer questions such as:
• What is wrong with the t-test and the F-test as usually practiced?
• What to replace these tests with?
• How to make multiple comparisons?
• How much data to collect?
• How to reduce sample sizes?
• How to use software to design and analyze comparative experiments?
Speakers
Anand Joglekar, PhD, Founder & President, Joglekar Associates, Inc.
Anand Joglekar, PhD is a leading statistics educator and consultant to industry. He has implemented statistical methods in areas such as research, product design, process (more)
Topics
The following topics will be addressed:
Location
Hamline University Minneapolis Directions / Map
Visit Hamline's website.