Overview:  
As the technology for obtaining, transmitting, and evaluating industrial data increases in power and sophistication, statistical decision-making tools using modern software, are now at the finger tips of every professional. This introductory course demonstrates how to analyze common business and industrial data ( e.g., process, product, test, marketing, sales, and field data) using a widely-used statistical software package. Topics include descriptive statistics, exploratory data analysis, data validation, outlier detection, data manipulations, correlation and scatter plots, interval estimation, comparisons of means and variances, analysis of 2x2 tables, linear regression, and basic forecasting.

Throughout the course participants practice these methodologies in-class using one of the following statistical software packages: JMPTM, Statgraphics PlusTM, or MinitabTM. The course finishes with a workshop requiring participants to analyze a set of multi-variable data that they participated in collecting earlier in the course.

How You will Benefit:
By the end of the course, participants will have gained:

• A firm grasp of the basic concepts and tools of practical data analyses.
• Ability to choose the best statistical methods to use to effectively summarize a set of data and correctly interpret the resulting analysis.
• The skill to select the most appropriate graphical methods to illuminate the salient features in a set of business or industrial data.
• Knowledge of methods of bi-variate statistical association (correlation, regression, comparisons) that lead to decisions of statistical significance.
• Experience using a leading PC-based statistical software system.

Course Outline:
Day 1

PDA: Generic Five-Step Model for Data Analysis
• Attributes of “Good” Data, Types of Data, Types of Variables
Step #1: Validate the Data
• How to Identify Erroneous Data, Dealing with “Aberrant” Data
Step #2: Summarize the Data
• Assessing Location & Dispersion
• Graphical Assessment of Distribution: Common Summary Plots
Step #3: Search for Structure in the Data
• Relationships between Variables
• Scatter plots, Correlation, Linear Regression, Stratification
• Graphical assessment of the inter-relationships of three variables

Day 2

Step #4: Make Comparisons and Formal Conclusions
• Concepts and Assumptions of Statistical Decision-making
• Confidence Intervals for the Mean, Standard Deviation, & Proportion
• Comparison of Population Means: Independent & Paired Samples
• Comparison of Population Variances & Proportions
• Decision-making on the Regression Model
Step #5: Present Results & State Conclusions
• Putting it All Together: Final Workshop

Who Should Attend:
Engineers, scientists, technicians, supervisors, business analysts, and other personnel in engineering, manufacturing, quality assurance, and related disciplines who are responsible for data-based decision-making in the industrial workplace. No pre-requisites are necessary for course participation except that a basic proficiency in use of Windows-based PC software is expected.

Cost:
Two-day course cost: $600 per student for OBA members and $700 per student for nono-members.  Tuition includes: class, bound presentation, and lunch for both days.

***10% Discount for all student who register for both the Practical Data Analysis class and Analysing Multi-factor class on 4/6/10 & 4/13/10***
PLEASE RESISTER FOR THIS CLASS THROUGH DAY 1 OF 2 Posting

Instructor Bio:  Don Lewis Ph.D.
Don Lewis is Principal, Lewis Consulting LLC, whose mission is to enable clients to improve their competitive performance through effective application of proven quantitative decision-making methodologies.  Since establishing his consulting practice in 1986, Don has trained and mentored over five thousand technical professionals to apply quantitative methods, such as Statistical Process Control and Design of Experiments, in their project work.  His consulting experience accrues from 50+ organizations across a diverse group of industries, including biosciences.  Clients have achieved significant performance improvement, including proprietary breakthroughs, as a result of implementing his services. 

Recently, as a Lead Instructor in Motorola University’s Digital Six Sigma Black Belt training program, Don has trained over two hundred and fifty Motorola Black Belts throughout the U.S., Europe, and Asia.  Since 2003 his Northwest Lean Six Sigma clients have saved over $16 million in project work completed in conjunction with his training programs.  He is an Adjunct Professor in both the Department of Management of Science & Technology at the OGI School of Science & Engineering in Portland, Oregon and the Atkinson Graduate School of Management at Willamette University.  Don is also a chapter author of the recently published "Encyclopedia of Statistics in Quality and Reliability."  He received his B.A. in mathematics from Claremont McKenna College and Ph.D. in biostatistics from the University of North Carolina at Chapel Hill.  Don is an ASQ Certified Six Sigma Black Belt.