**ISBN:** 0471203424

**Author:** Anand M. Joglekar

**Language:** English

**Publisher:** Wiley-Interscience; 1 edition (September 4, 2003)

**Pages:** 344

**Category:** Medicine

**Subcategory:** Medicine

**Rating:** 4.2

**Votes:** 561

**Size Fb2:** 1551 kb

**Size ePub:** 1288 kb

**Size Djvu:** 1750 kb

**Other formats:** doc lrf azw mobi

Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance. Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry.

Электронная книга "Statistical Methods for Six Sigma: In R&D and Manufacturing", Anand M. Joglekar. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Statistical Methods for Six Sigma: In R&D and Manufacturing" для чтения в офлайн-режиме.

Article in Journal of the American Statistical Association 99(December):1205-1206 · February 2004 with 50 Reads. How we measure 'reads'.

Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and .

Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry.

Statistical methods in this book include: variance components analysis .

for Six Sigma in R&D and Manufacturing by Anand M. and manufacturing does not presume any prior knowledge of statistics.

Statistical Methods for Six Sigma in R&D and Manufacturing by Anand M.

Автор: Anand M. Joglekar Название: Statistical Methods for Six Sigma: In R&D and . The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making.

The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making.

Anand M. Joglekar," Journal of the American Statistical Association, American Statistical Association, vol . 99, pages 1205-1206, December. Handle: RePEc:bes:jnlasa:v:99:y:2004:p:1205-1206. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. More services and features.

Statistical methods are a key ingredient in providing data-basedguidance to research and development as well as to manufacturing.Understanding the concepts and specific steps involved in eachstatistical method is critical for achieving consistent andon-target performance.

Written by a recognized educator in the field, StatisticalMethods for Six Sigma: In R&D and Manufacturing is specificallygeared to engineers, scientists, technical managers, and othertechnical professionals in industry. Emphasizing practicallearning, applications, and performance improvement, Dr. Joglekar?stext shows today?s industry professionals how to:

Summarize and interpret data to make decisionsDetermine the amount of data to collectCompare product and process designsBuild equations relating inputs and outputsEstablish specifications and validate processesReduce risk and cost-of-process controlQuantify and reduce economic loss due to variabilityEstimate process capability and plan process improvementsIdentify key causes and their contributions to variabilityAnalyze and improve measurement systemsThis long-awaited guide for students and professionals inresearch, development, quality, and manufacturing does not presumeany prior knowledge of statistics. It covers a large number ofuseful statistical methods compactly, in a language and depthnecessary to make successful applications. Statistical methods inthis book include: variance components analysis, variancetransmission analysis, risk-based control charts, capability andperformance indices, quality planning, regression analysis,comparative experiments, descriptive statistics, sample sizedetermination, confidence intervals, tolerance intervals, andmeasurement systems analysis. The book also contains a wealth ofcase studies and examples, and features a unique test to evaluatethe reader?s understanding of the subject.

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