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 Descriptive 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.

Learning Objectives:

Effectively summarize data and communicate results with descriptive 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

DAY 01(8:30 AM - 2:30 PM)

8:30 â€“ 9:00 AM: Registration

9:00 AM: Session Start Time

Descriptive Statistics & Distributions

Data Types

Populations & Samples

Central Tendency and Variation

Probability Distributions

The Normal Distribution

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 - 2:30 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

Instructor Profile:

Steven Wachs
Principal Statistician, Integral Concepts, Inc

Steven Wachs has 30 years of wide-ranging industry experience in both technical and management positions. He 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.

Mr. Wachs 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. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.