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Getting It Right : R & D Methods For Science and Engineering

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Title: Getting It Right : R & D Methods For Science and Engineering
Author: PETER BOCK
ISBN: 0121088529 / 9780121088521
Format: Hard Cover
Pages: 406
Publisher: Academic Press
Availability: In Stock
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  • Description
  • Contents

Peter Bock developed Getting IT Right in direct response to discussions with the directors of R&D departments in large industrial firms who are frustrated by the lack of coherent and consistent methodologies in the R&D projects they manage.

The Symptoms: Think about your R&D work environment. Do any of these statements ring a bell?

· Research results cannot be reproduced
· Knowledge is precarious and kept primarily in people’s heads
· Speculations are represented as rigorous conclusions
· Data collection is haphazard and hindered politically
· Experiment methods are chaotic: try this, try that
· Project processes cannot be audited because there are no logs or records
· Documentation is incomplete or nonexistent
· Statistical reduction of data is naïve
· Data visualization techniques are poor
· Project activities are isolated and inbred

The Problem: Most engineers and scientists have little formal training in the design and conduct of complex R&D projects. As the focus in industry and academia shifts rapidly toward projects involving complex human-machine interaction, traditional R&D methods are coming up short.

The Solution: The complexity of industrial and academic research and development projects in the 21st century demands a consistent and coherent methodology for their design and execution. This book presents a complete and rigorous methodology for planning and conducting R&D projects, both large and small. The emphasis is focused squarely on Getting It Right:

· Strict adherence to a modernized version of the classical Scientific Method
· Consistent and precise terminology, summarized in an extensive Glossary
· Hierarchical and recursive project planning
· Stepwise refinement and inside-out task design
· Sensible assignment of project resources and management roles
· Political and technical issues for experiment data acquisition
· Fail-soft methods for recursive error recovery
· Rigorous design of carefully controlled experiments
· Comprehensive analysis of potential sources of bias and error
· Statistical methods for data analysis and reduction
· Data visualization techniques and documentation standards
· Highlighted Tips for dealing with common critical problems
· Real-world case studies augmented by detailed figures and tables
· Useful summaries of essential concepts
· Philosophical, ethical, and epistemological constraints and perspectives

Acknowledgments
Foreword
Biographies

Part 1 : Introduction
Chapter 1 : Research and Development

  • 1.1 Motivation
  • 1.2 Background
  • 1.3 R & D Problems
  • 1.4 Primary Objective

Chapter 2 : Process and Preparation

  • 2.1 The Methodology
  • 2.2 Tools and Resources

Part 2 : Project Organization
Chapter 3 : The Project Hierarchy

  • 3.1 Bottoms Up
  • 3.2 Top-Down Project Planning

Chapter 4 : The Project Task

  • 4.1 Task Domain
  • 4.2 Task Method
  • 4.3 Task Range

Part 3 : Knowledge Representation
Chapter 5 : An Epistemological Journey


Chapter 6 : Categories and Types of Knowledge

  • 6.1 Speculative Knowledge
  • 6.2 Presumptive Knowledge
  • 6.3 Stipulative Knowledge
  • 6.4 Conclusive Knowledge

Chapter 7 : Roles of Knowledge Propositions

  • 7.1 Governing Propositions
  • 7.2 Factors
  • 7.3 Range Knowledge

Chapter 8 : Limits of Knowledge

  • 8.1 Accuracy and Error
  • 8.2 Uncertainty
  • 8.3 Precision
  • 8.4 Knowledge, Truth, and Humility

Part 4 : The Scientific Method
Chapter 9 : Overview

  • 9.1 History of the Scientific Method
  • 9.2 The Modern Scientific Method

Chapter 10 : Analysis

  • 10.1 Describe Problem
  • 10.2 Set Performance Criteria
  • 10.3 Investigate Related Work
  • 10.4 State Objective

Chapter 11 : Hypothesis

  • 11.1 Specify Solution
  • 11.2 Set Goals
  • 11.3 Define Factors
  • 11.4 Postulate Performance Metrics

Chapter 12 : Synthesis

  • 12.1 Implement Solution
  • 12.2 Design Experiments
  • 12.3 Conduct Experiments
  • 12.4 Reduce Results

Chapter 13 : Validation

  • 13.1 Compute Performance
  • 13.2 Draw Conclusions
  • 13.3 Prepare Documentation
  • 13.4 Solicit Peer Review

Appendices
A : Bibliography
B : Glossary
C : Tips
D : Summaries and Guidelines
E : Case-Study Figures and Tables
F : Sample Experiment Protocol
G : An Algorithm for Discovery
Index

 
 
 
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