Conference Description:

Statistical Analysis for Process and Product Development


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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:

Lunch

AM-PM Tea/Coffee

Seminar Material

USB with seminar presentation

Hard copy of presentation

Attendance Certificate

$100 Gift Cert for next seminar


Course Information:

Participants are requested to bring a laptop with Minitab Version 17 software installed.



Learning Objectives:

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:

Scientists

Product and Process Engineers

Quality Engineers

Personnel involved in product development and validation


AGENDA

Day 01(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
Histograms
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



Day 02(8:30 AM - 4:30 PM)

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?
Definitions
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


SPEAKER

Steven Wachs
Principal Statistician, Integral Concepts, Inc

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.



Please contact the event manager Marilyn ([email protected] ) below for:

- Multiple participant discounts
- Price quotations or visa invitation letters
- Payment by alternate channels (PayPal, check, Western Union, wire transfers etc)
- Event sponsorships