Statistical Analysis for Process and Product Development: 2-Day In-person Seminar
Steven Wachs, VP and Principal Statistician
San Francisco, CA
| Thursday, October 27, 2016 | Friday, October 28, 2016
||Course "Statistical Analysis for Process and Product Development" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.
This course is designed to help scientists and engineers apply statistical methods used assist decision making in process and product development. Variability must be considered when utilizing data to arrive at conclusions.
- This course will cover Basic Statistics and Graphical Methods used to summarize data.
- You will learn how to apply Hypothesis Testing methods to determine whether groups are statistically equivalent or not with respect to key process characteristics such as process averages and variability.
- The use of confidence intervals when estimating key parameters will be covered.
- When planning studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered.
- Finally, an introduction to Design of Experiments (DOE) is provided. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods.
This highly interactive course will allow participants the opportunity to practice applying statistical methods with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts.
Seminar Fee Includes:
USB with seminar presentation
Hard copy of presentation
$100 Gift Cert for next seminar
Participants are requested to bring a laptop with Minitab Version 17 software installed.
- Effectively summarize data and communicate results with basic statistics and graphical techniques
- Apply Hypothesis Testing to test whether two or more groups of data are statistically equivalent or not.
- Estimate key process parameters with associated confidence intervals to express estimate uncertainty
- Determine appropriate sample sizes for estimation and hypothesis testing
- Understand key concepts related to Design of Experiments
- Apply experiments to determine cause and effect relationships and model process behaviour
Who will Benefit:
- Product and Process Engineers
- Quality Engineers
- Personnel involved in product development and validation
|Day 1 (8:30 AM – 4:30 PM)
||Day 2 (8:30 AM – 4:30 PM)
- 8:30 – 9:00 AM: Registration
- 9:00 AM: Session Start Time
- Basic Statistics & Distributions
- Data Types
- Populations & Samples
- Central Tendency and Variation
- Probability Distributions
- The Normal Distribution
- Graphical Analysis
- Pareto Charts
- Run Charts
- Boxplots and Individual Value Plots
- Scatter Plots
- Hypothesis Testing Concepts
- Test Statistics, Crit. Values, p-values
- One and Two Sided Tests
- Type I and Type II Errors
- Estimation and Confidence Intervals
- Hypothesis Tests for One and Two Groups
- Testing Means (1 sample t ,2 sample t and paired t tests)
- Testing Variances (Chi-Square, F test)
- Testing Proportions (overview)
- Tolerance Intervals
- Equivalence Tests
- Hypothesis Tests for Multiple (>2) Groups
- Testing Means (ANOVA)
- Multiple Comparisons
- Testing Variances (Bartlett’s and Levene’s Test)
- Testing for Normality
- Power & Sample Size
- Type II Errors and Power
- Factors affecting Power
- Computing Sample Sizes
- Power Curves
- Sample Sizes for Estimation
- Introduction to Experimental Design
- What is DOE?
- Sequential Experimentation
- When to use DOE
- Common Pitfalls in DOE
- A Guide to Experimentation
- Planning an Experiment
- Implementing an Experiment
- Analyzing an Experiment
- Case Studies
- Two Level Factorial Designs
- Design Matrix and Calculation Matrix
- Calculation of Main & Interaction Effects
- Interpreting Effects
- Using Center Points
- Identifying Significant Effects
- Determining which effects are statistically significant
- Analyzing Replicated and Non-replicated Designs
- Developing Mathematical Models
- Developing First Order Models
- Residuals /Model Validation
- Optimizing Responses
Meet Your Instructor
VP and Principal Statistician
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.
Credentials and Honors
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986
Summa Cum Laude, University of Michigan, 1986
Tau Beta Pi - National Engineering Honor Society, 1986
Associates Fellowship and Highest Honors, University of Pittsburgh 1992
Six Sigma Blackbelt Certification, Ford Motor Company 2001
Ford Education Fellowship, 2001
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San Francisco, CA (Venue to be announced shortly)
October 27-28, 2016
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