Industrial Statistics : Practical Methods and Guidance for Improved Performance

by Anand M. Joglekar (Joglekar Associates, MN, USA)

• ISBN13: 9780470584125
• ISBN10: 0470584122
• Format: eBook
• Copyright: 2010-04-01
• Publisher: Wiley

Summary

Helps you fully leverage statistical methods to improve industrial performanceIndustrial Statistics guides you through ten practical statistical methods that have broad applications in many different industries for enhancing research, product design, process design, validation, manufacturing, and continuous improvement. As you progress through the book, you'll discover some valuable methods that are currently underutilized in industry as well as other methods that are often not used correctly.With twenty-five years of teaching and consulting experience, author Anand Joglekar has helped a diverse group of companies reduce costs, accelerate product development, and improve operations through the effective implementation of statistical methods. Based on his experience working with both clients and students, Dr. Joglekar focuses on real-world problem-solving. For each statistical method, the book:Presents the most important underlying concepts clearly and succinctlyMinimizes mathematical details that can be delegated to a computerIllustrates applications with numerous practical examplesOffers a "Questions to Ask" section at the end of each chapter to assist you with implementationThe last chapter consists of 100 practical questions followed by their answers. If you're already familiar with statistical methods, you may want to take the test first to determine which methods to focus on.By helping readers fully leverage statistical methods to improve industrial performance, this book becomes an ideal reference and self-study guide for scientists, engineers, managers and other technical professionals across a wide range of industries. In addition, its clear explanations and examples make it highly suited as a textbook for undergraduate and graduate courses in statistics.

Table of Contents

Preface

Basic Statistics: How To Reduce Financial Risk?

Capital Market Returns

Sample Statistics

Population Parameters

Confidence Intervals and Sample Sizes

Correlation

Portfolio Optimization

Questions to Ask

Why Not To Do The Usual T-Test And What To Replace It With?

What is a t-Test and what is Wrong with It?

Confidence Interval is Better Than a t-Test

How Much Data to Collect?

Reducing Sample Size

Paired Comparison

Comparing Two Standard Deviations

Recommended Design and Analysis Procedure

Questions to Ask

Design Of Experiments: Is It Not Going To Cost Too Much And Take Too Long?

Why Design Experiments?

Factorial Designs

Success Factors

Fractional Factorial Designs

Plackett-Burman Designs

Applications

Optimization Designs

Questions to Ask

What Is The Key To Designing Robust Products And Processes?

The Key to Robustness

Robust Design Method

Signal-to-Noise Ratios

Achieving Additivity

Alternate Analysis Procedure

Implications for R&D

Questions to Ask

Setting Specifications: Arbitrary Or Is There A Method To It?

Understanding Specifications

Empirical Approach

Functional Approach

Minimum Life Cycle Cost Approach

Questions to Ask

How To Design Practical Acceptance Sampling Plans And Process Validation Studies?

Single-Sample Attribute Plans

Selecting AQL and RQL

Other Acceptance Sampling Plans

Designing Validation Studies

Questions to Ask

Managing And Improving Processes: How To Use An At-A-Glance-Display?

Statistical Logic of Control Limits

Selecting Subgroup Size

Selecting Sampling Interval

Out-of-Control Rules

Process Capability and Performance Indices

At-A-Glance-Display

Questions to Ask

How To Find Causes Of Variation By Just Looking Systematically?

Manufacturing Application

Variance Components Analysis

Planning for Quality Improvement

Structured Studies

Questions to Ask

Is My Measurement System Acceptable And How To Design, Validate, And Improve It?

Acceptance Criteria

Designing Cost-Effective Sampling Schemes

Designing a Robust Measurement System

Measurement System Validation

Repeatability and Reproducibility (R&R) Study

Questions to Ask

How To Use Theory Effectively?

Empirical Models

Mechanistic Models

Mechanistic Model for Coat Weight CV

Questions to Ask

Questions And Answers

Questions

Answers

Appendix: Tables

ReferenceS

Index

Table of Contents provided by Publisher. All Rights Reserved.