Introduction to Design of Experiments (DOE)

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DATE & TIME

January 26, 2012, at 1:00 p.m. EDT | 10:00 a.m. PDT
Please check your time zone for the correct local time.

Can't wait until January 26? View the On-Demand version of this webinar at any time:
>>Click here for the On-Demand Version of this Webinar

LENGTH

One hour

PRICE

$199

SUMMARY
This basic Introduction to Design of Experiments (DOE) webinar, which requires no prior knowledge of statistics, covers fundamentals such as factors and levels, hypothesis testing, the need for random samples, and interpretation of results.

This webinar will not go deeply into analysis of experimental results but will illustrate the concepts graphically with box and whisker plots and similar media.

Course participants with no prior knowledge of DOE will understand enough basics to interact effectively with subject matter experts such as industrial statisticians, quality engineers, and Six Sigma practitioners. Managers will understand presentations of experimental results. An understanding of the concepts will also be good preparation if the attendee later takes a course in industrial statistics or an ASQ certification review course.

COURSE OUTLINE

Understand the reason for DOE: a cost-effective and scientific technique to improve quality and identify defect sources.

Cost-effective because it minimizes the number of samples that must be used
Scientific because it quantifies the uncertainty of the reported results

Understand the foundation of hypothesis testing, which also applies to SPC and acceptance sampling.

Understand the quantifiable risks of error in any statistical activity. These are specifically Type I vs. Type II, alpha vs. beta, or producer's risk vs. consumer's risk. The first is the chance of concluding that there is a problem or a difference when none exists, and the second is the chance of not detecting a problem or difference that does exist.
Realize that the only way to reduce both risks simultaneously is to obtain and assess more data. (This is why replication is important.)

Understand the concepts of factors, levels, and interactions.
Understand the importance of specimen randomization, blocking, and similar techniques to keep extraneous variation out of the experiment.
Understand the concept of interactions (the sum of the whole is greater or less than the sum of the parts).
Interpret experimental results in terms of significance level and P value (chance that any observed differences in results are due to luck or random chance).

WHAT YOU WILL LEARN FROM THIS COURSE

Recognize situations such as process troubleshooting or process improvement in which DOE could be a valuable tool.
Understand the concepts of factors, levels, and interactions.
Know how screening experiments such as factorial designs can save an enormous amount of time and money (awareness only, will not go into detail).
Know the need to keep extraneous variation sources out of the experiment, so the results will be meaningful. (Concepts of randomization and blocking.)
Know how to plan the experiment by defining the null and alternate hypothesis. Understand the quantifiable risks (Type I and Type II, alpha and beta, or producer's risk and consumer's risk) of obtaining the wrong conclusion.
This will equip the attendee to understand all applications of industrial statistics, including statistical process control and acceptance sampling, as well as DOE.
The cost to minimize both risks simultaneously is to assess a bigger sample; nothing is free! (Concept of replication.)
Know how to interpret the results of the experiment; does it show a significant difference between the control and experimental treatments, or between materials, methods, or tools?

BONUS!

Unlimited viewing of the recorded version of this webinar for 14 days. So even if you forget to attend or can't make the live version, you can still get the information you need.
Certificate of attendance for each PAID participant
Get your specific questions answered in a live Q&A session during the webinar
FREE copy of the SPC Control Chart Simulator software
Our money-back guarantee

PRESENTER

William A. Levinson, P.E., is the principal of Levinson Productivity Systems, P.C. He is an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt. He holds degrees in chemistry and chemical engineering from Penn State and Cornell Universities, and degrees in business administration and applied statistics from Union College, and he has given presentations at the ASQ World Conference, TOC World 2004, and other national conferences on productivity and quality.

Levinson is also the author of several books on quality, productivity, and management. Henry Ford's Lean Vision is a comprehensive overview of the lean manufacturing and organizational management methods that Ford employed to achieve unprecedented bottom line results, and Beyond the Theory of Constraints describes how Ford's elimination of variation from material transfer and processing times allowed him to come close to running a balanced factory at full capacity. Statistical Process Control for Real-World Applications shows what to do when the process doesn't conform to the traditional bell curve assumption.

HOW DOES THIS ALL WORK?
After you register for the Webinar, you will receive a confirmation e-mail. It will contain a Web link to access the visual portion of the event through your Web browser. It will also contain an a phone number and access code that you will use to hear the audio portion of the event.

PRICING
We are so confident that you will find this webinar valuable that we offer a 100% money back guarantee, making this a risk-free investment. If you are dissatisfied with the webinar, just let us know by phone or e-mail within 24 hours of the conclusion of the webinar, and we will issue you a full refund.

PLEASE NOTE: Participation in this webinar is just $199 per site and allows access to one Internet connection and one phone line.

Price: $199.00

This product qualifies for quantity discount pricing.

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