 |
Joglekar Associates Inc.
2820 Fountain Lane North
Minneapolis, MN 55447
Ph: 763-476-1449
Fx: 763-475-3823 |
|
Publications
Book
Statistical Methods for Six Sigma - in R&D
and Manufacturing
Author: Anand Joglekar Publisher: John Wiley
& Sons, Inc.
 Written
by a recognized and well-established educator in the field, Statistical
Methods for Six Sigma – in R&D and Manufacturing
is specifically meant for engineers, scientists, technical managers
and other technical professionals in industry. Featuring an emphasis
on practical learning, applications and improvement, Dr. Joglekar’s
text shows today’s industry professionals how to:
- Summarize and interpret data to make
decisions
- Determine the amount of data to collect
- Compare product and process designs
- Build equations relating inputs and outputs
- Establish specifications and validate processes
- Reduce risk and cost of process control
- Quantify and reduce economic loss due to variability
- Estimate process capability and plan process
improvements
- Identify key causes and their contribution
to variability
- Analyze and improve measurement systems
This long awaited guide
for students and professionals in research, development, quality
and manufacturing does not presume any prior knowledge of statistics
and covers a large number of useful statistical methods compactly,
in a language and depth necessary to make successful applications.
The book also contains a wealth of case studies and examples, and
features a unique test called “what color is your belt?”
to evaluate the reader’s understanding of the subject.
Recent Industry Presentations
These are some seminars we recently delivered
to a variety of industrial audiences. Each seminar is one to two
hours long. The seminars were delivered for various R&D and
manufacturing groups, often with examples customized to the audience.
- Does my project really need six-sigma quality?
- The role of statistical methods in six sigma
- How much data to collect? — attribute
data
- How much data to collect? — variable
data
- Confidence intervals vs. hypothesis testing
- Why design experiments?
- Projective properties of screening designs
- Multi-level designs using Taguchi linear graphs
- Mixture designs
- Mixture or ratios?
- The principle of robust design and consequences
for R&D
- The importance of data transformation
- Normality and data transformation
- Analysis of validation and pre-validation
experiments
- Size matters — how good is your Cpk,
really?
- How stable and capable are our production
processes?
- The statistics of limits— spec, control,
action, release...
- Regression — simple and weighted
- Is my R-square too low?
- Understanding variance components
- Making decisions using variance components
- Variance transmission, robustness and specifications
- Specifications and implied constraints on
Sw and Sb
- Acceptance criteria for measurement systems
- Accelerated stability tests
- The statistics of method transfer
- Why does random failure imply exponential
time to failure
- Does acceptance sampling buy us what we think?
- The theory of significant figures
- Understanding key software outputs
|
