Statistics in Engineering
A Practical approach
Published on 1. July 1994
Book
Paperback/Softback
X, 446 pages
978-0-412-49220-4 (ISBN)
Description
Statistics in Engineering provides a succinct introduction to statistics. The ideas are introduced with examples set in their practical context. The underlying mathematics are given in an informal way and are included for those who find that mathematical justification helps their understanding of concepts, and for anyone who needs to take the subject further The author indicates sections that can be omitted without any loss of continuity. The book is kept as simple as possible, and assumes only some familiarity with elementary calculus and matrices.
The first seven chapters of the book cover a typical 40-hour statistics module taken by engineering or science students who are beginning the subject. This includes the basic ideas, relationships between variables, and the design and analysis of experiments. The final chapter looks at some important engineering situations that are not fully covered by the methods of the preceding chapters.
The first seven chapters of the book cover a typical 40-hour statistics module taken by engineering or science students who are beginning the subject. This includes the basic ideas, relationships between variables, and the design and analysis of experiments. The final chapter looks at some important engineering situations that are not fully covered by the methods of the preceding chapters.
More details
Series
Language
English
Place of publication
Boston, MA
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Research
Illustrations
23
23 s/w Abbildungen
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
700 gr
ISBN-13
978-0-412-49220-4 (9780412492204)
DOI
10.1007/978-1-4899-6623-0
Schweitzer Classification
Other editions
New editions
Andrew Metcalfe | David Green | Tony Greenfield
Statistics in Engineering
With Examples in MATLAB (R) and R, Second Edition
Book
01/2019
2nd Edition
Chapman & Hall/CRC
€131.70
Shipment within 15-20 days
Content
Why Understand Statistics? Probability in Engineering Decisions: Introduction. Defining Probability. The Addition Rule of Probability. Conditional Probability. Arrangements and Choices. Decision Trees. Summary. Justifying Engineering Decisions: Presenting Data. Summarising Data. Fatigue Damage. Shape of Distributions. Summary. Modelling Variability: Discrete Probability Distributions. Continuous Probability Distributions. Modelling Rainfall. Summary. Combining Variables: Introductions. Sample Covariance and Correlation. Joint Probability Distributions. Population Covariance and Correlation. Linear Combination of Random Variables. Distribution of the Sample Mean. Statistical Process Control Charts. Nonlinear Functions of Random Variables. Summary. Precision of Estimates: Precision of Means. Precision of Standard Deviations. Comparing Standard Deviations. Comparing Means. Sample Size. Proportions. Random Breath Tests? Summary. Asset Management Plan: Background. Statistical Issues. Sampling Scheme. Unit Cost Formulae. Zone Costs. Stratum Costs. Total Costs of Local Distribution Networks. Discussion. Summary. Making Predictions from One Variable: Linear Regression. Intrinsically Linear Models. Conditional Distributions. Relationship Between Correlation and Regression. Fitting Straight Lines When Both Variables Subject to Error. Calibration Lines. Summary. Making Predictions from Several Explanatory Variables: Regression on Two Explanatory Variables. Multiple Regression Model. Categorical Variables. Chrome Plating of Rods for Hydraulic Machinery. Summary. Design of Experiments: Evolutionary Operation. More Than Two Factors. Comparing Several Means. Experimental Design for Welded Joints. Summary. Modelling Variability in Time and Space: Evaluation of Mini-Roundabouts as a Road Safety Measure. Predicting Short Term Flood Risk. Spectral Analysis for Design of Offshore Structures. Endnote. Appendices: Appendix A: Mathematical Explanations of Key Results: Derivation of Poisson Distribution. Central Limit Theorem. Derivation of EVGI Distribution. Estimated Variance of Ratio Estimator. Multiple Regression Model. Du Mouchel's Algorithm. Appendix B: Reference Guide: Notation. Glossary. Problem Solving Guide. Suggested Short Course. Appendix C: Summary of Minitab Commands Used in Text. Appendix D: Data Sets. Tables. Appendix E: Statistical Tables. Appendix F: Answers to Selected Exercises.