Forecasting and time series analysis / Douglas C. Montgomery

Public ISBD Titre : Forecasting and time series analysis Type de document : texte imprimé Auteurs : Douglas C. Montgomery, Auteur ; Lynwood A. Johnson, Auteur Editeur : New york, St louis, san francisco... : McGraw-Hill Company Année de publication : 1976 Importance : 304 p. Présentation : ill. Format : 24 cm. ISBN/ISSN/EAN : 9780070428573 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-02 Théorie et traitement du signal Note de contenu : Contents 1. INTRODUCTION TO FORECASTING SYSTEMS 2. REGRESSION METHODS AND MOVING AVERAGES 3. EXPONENTIAL SMOOTHING METHODS 4. DISCOUNTED LEAST SQUARES AND DIRECT SMOOTHING 5. SMOOTHING MODELS FOR SEASNOAL DATA 6. FORECASTING 7. ANALYSIS OF FORECAST ERRORS 8. ADAPTIVE-CONTROL FORECASTING METHODS 9. THE BOX-JENKINS MODELS 10. BAYESIAN METHODS IN FORECASTING Forecasting and time series analysis [texte imprimé] / Douglas C. Montgomery, Auteur ; Lynwood A. Johnson, Auteur . - New york, St louis, san francisco... : McGraw-Hill Company, 1976 . - 304 p. : ill. ; 24 cm. ISSN : 9780070428573 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-02 Théorie et traitement du signal Note de contenu : Contents 1. INTRODUCTION TO FORECASTING SYSTEMS 2. REGRESSION METHODS AND MOVING AVERAGES 3. EXPONENTIAL SMOOTHING METHODS 4. DISCOUNTED LEAST SQUARES AND DIRECT SMOOTHING 5. SMOOTHING MODELS FOR SEASNOAL DATA 6. FORECASTING 7. ANALYSIS OF FORECAST ERRORS 8. ADAPTIVE-CONTROL FORECASTING METHODS 9. THE BOX-JENKINS MODELS 10. BAYESIAN METHODS IN FORECASTING

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Probability and statistics in engineering / William W. Hines

Public ISBD Titre : Probability and statistics in engineering Type de document : texte imprimé Auteurs : William W. Hines, Auteur ; Douglas C. Montgomery, Auteur ; David M. Goldsman, Auteur Mention d'édition : 4th. ed. Editeur : USA : John wiley & Sons Année de publication : 2003 Importance : 655 p. Présentation : couv. ill. en coul. Format : 26 cm. ISBN/ISSN/EAN : 978-0-471-24087-7 Langues : Anglais (eng) Catégories : MATHEMATIQUES Index. décimale : 04-05 Probabilité et statistique Résumé : Now with even more examples with real data, real-world applications, and computer exercise, the Fourth Edition of this accessible text prepares you for situations you're likely to encounter as a professionakl engineer. Together with new co-authors David Goldsman and Connie Borror, William Hines and Douglas Montgomery have refined their highly effective pedagogical framework to make their text even more user friendly. This Fourth Edition also features a new chapter on statistical methods for computer situation, as well exceptionally clear statistical coverage, expanded discussions of quiality control, experimental design, and different types of interval estimation, and coverage of such special topics as nonparametric statistics, p-values in hypothetical testing, and residual analysis. Highlights of the Fourth Edition: New examples and applications provide a real-world perspective on how engineers use probability and statistics in professional practice. Over 600 exercises, including many new computation problems, provide opportunities for hands-on learning.An entirely new chapter on statistical methods for computer simulation methods covers Monte Carlo experimentation, random number and variate generation, and simulation output data analysis. New chapter organization starts with probability theory and progresses through random variables, discrete and continuous distributions, and normal distribution, before introducing statistics and data description techniques. Each chapter starts with an introduction that describes the importance of the topic and features interesting historical information related to the topic. End-of-chapter summaries reinforce the main topics and goals of the chapter. Note de contenu : Contents 1. An Introduction to Probability 2. One-Dimensional Random Variables 3. Functions of One Random Variable and Expectation 4. Joint Probability Distributions 5. Some Important Discrete Distributions 6. Some Important Continuous Distributions 7. The Normal Distribution 8. Introduction to Statistics and Data Description 9. Random Samples and Sampling Distributions 10. Parameter Estimation 11. Tests of Hypotheses 12. Design and Analysis of Single-Factor Experiments: The Analysis of Variance 13. Design of Experiments with Several Factors 14. Simple Linear Regression and Correlation 15. Multiple Regression 16. Nonparametric Statistics 17. Statistical Quality Control and Reliability Engineering 18. Stochastic Processes and Queueing 19. Computer Simulation. -Appendix -Index Probability and statistics in engineering [texte imprimé] / William W. Hines, Auteur ; Douglas C. Montgomery, Auteur ; David M. Goldsman, Auteur  . -  4th. ed. . - USA : John wiley & Sons, 2003 . - 655 p. : couv. ill. en coul. ; 26 cm. ISBN : 978-0-471-24087-7 Langues : Anglais (eng) Catégories : MATHEMATIQUES Index. décimale : 04-05 Probabilité et statistique Résumé : Now with even more examples with real data, real-world applications, and computer exercise, the Fourth Edition of this accessible text prepares you for situations you're likely to encounter as a professionakl engineer. Together with new co-authors David Goldsman and Connie Borror, William Hines and Douglas Montgomery have refined their highly effective pedagogical framework to make their text even more user friendly. This Fourth Edition also features a new chapter on statistical methods for computer situation, as well exceptionally clear statistical coverage, expanded discussions of quiality control, experimental design, and different types of interval estimation, and coverage of such special topics as nonparametric statistics, p-values in hypothetical testing, and residual analysis. Highlights of the Fourth Edition: New examples and applications provide a real-world perspective on how engineers use probability and statistics in professional practice. Over 600 exercises, including many new computation problems, provide opportunities for hands-on learning.An entirely new chapter on statistical methods for computer simulation methods covers Monte Carlo experimentation, random number and variate generation, and simulation output data analysis. New chapter organization starts with probability theory and progresses through random variables, discrete and continuous distributions, and normal distribution, before introducing statistics and data description techniques. Each chapter starts with an introduction that describes the importance of the topic and features interesting historical information related to the topic. End-of-chapter summaries reinforce the main topics and goals of the chapter. Note de contenu : Contents 1. An Introduction to Probability 2. One-Dimensional Random Variables 3. Functions of One Random Variable and Expectation 4. Joint Probability Distributions 5. Some Important Discrete Distributions 6. Some Important Continuous Distributions 7. The Normal Distribution 8. Introduction to Statistics and Data Description 9. Random Samples and Sampling Distributions 10. Parameter Estimation 11. Tests of Hypotheses 12. Design and Analysis of Single-Factor Experiments: The Analysis of Variance 13. Design of Experiments with Several Factors 14. Simple Linear Regression and Correlation 15. Multiple Regression 16. Nonparametric Statistics 17. Statistical Quality Control and Reliability Engineering 18. Stochastic Processes and Queueing 19. Computer Simulation. -Appendix -Index

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Statistical Quality Control / Douglas C. Montgomery

Public ISBD Titre : Statistical Quality Control : a modern introduction Type de document : texte imprimé Auteurs : Douglas C. Montgomery, Auteur Mention d'édition : 6 th. ed. Editeur : River Street, Hoboken : John Wiley Année de publication : 2009 ISBN/ISSN/EAN : 978-0-470-23397-9 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-07 Théorie de la commande: commandes des processus Résumé : The trusted guide to the statistical methods for quality control. Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Introduction to Statistical Quality Control, Sixth Edition gives you a sound understanding of the principles of statistical quality control (SQC) and how to apply them in a variety of situations for quality control and improvement. With this text, you′ll learn how to apply state–of–the–art techniques for statistical process monitoring and control, design experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques. You′ll appreciate the significant updates in the Sixth Edition including: ∗ In–depth attention to DMAIC, the problem–solving strategy of Six Sigma. It will give you an excellent framework to use in conducting quality improvement projects. ∗ New examples that illustrate applications of statistical quality improvement techniques in non–manufacturing settings. Many examples and exercises are based on real data. ∗ New developments in the area of measurement systems analysis ∗ New features of Minitab V15 incorporated into the text ∗ Numerous new examples, exercises, problems, and techniques to enhance your absorption of the material Note de contenu : Contents Part I: Introduction · Chapter 1:Modern Quality Management and Improvement · Chapter 2: The DMAIC Problem solving process Part II: Statistical Methods Useful in Quality Control and Improvement · Chapter 3: Statistical Models or Quality Control and Improvement · Chapter 4: Statistical Inferences in Quality Control and Improvement Part III: Basic Methods of Statistical Process Control and Capability Analysis Chapter 5: How SPC Works · Chapter 6: Variables Control Charts Chapter 7: Attributes Control Charts · Chapter 8: Determining Process and Measurement System Capability Part IV: Other Statistical Process-Monitoring and Control Techniques Chapter 9: Time-Weighted Control Charts · Chapter 10: Advanced Control Charting Techniques · Chapter 11: Multivariate SPC Chapter 12: Process Adjustment and Process monotoring Part V: Process Design and Improvement with Designed Experiments Chapter 13: Basic Experimental Design for Process Improvements Chapter 14: Process Optimization Part VI: Acceptance Sampling · Chapter 15: Basic Acceptance Sampling Procedures Chapter 16: Additional Sampling Procedures Appendix Statistical Quality Control : a modern introduction [texte imprimé] / Douglas C. Montgomery, Auteur  . -  6 th. ed. . - River Street, Hoboken : John Wiley, 2009. ISBN : 978-0-470-23397-9 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-07 Théorie de la commande: commandes des processus Résumé : The trusted guide to the statistical methods for quality control. Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Introduction to Statistical Quality Control, Sixth Edition gives you a sound understanding of the principles of statistical quality control (SQC) and how to apply them in a variety of situations for quality control and improvement. With this text, you′ll learn how to apply state–of–the–art techniques for statistical process monitoring and control, design experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques. You′ll appreciate the significant updates in the Sixth Edition including: ∗ In–depth attention to DMAIC, the problem–solving strategy of Six Sigma. It will give you an excellent framework to use in conducting quality improvement projects. ∗ New examples that illustrate applications of statistical quality improvement techniques in non–manufacturing settings. Many examples and exercises are based on real data. ∗ New developments in the area of measurement systems analysis ∗ New features of Minitab V15 incorporated into the text ∗ Numerous new examples, exercises, problems, and techniques to enhance your absorption of the material Note de contenu : Contents Part I: Introduction · Chapter 1:Modern Quality Management and Improvement · Chapter 2: The DMAIC Problem solving process Part II: Statistical Methods Useful in Quality Control and Improvement · Chapter 3: Statistical Models or Quality Control and Improvement · Chapter 4: Statistical Inferences in Quality Control and Improvement Part III: Basic Methods of Statistical Process Control and Capability Analysis Chapter 5: How SPC Works · Chapter 6: Variables Control Charts Chapter 7: Attributes Control Charts · Chapter 8: Determining Process and Measurement System Capability Part IV: Other Statistical Process-Monitoring and Control Techniques Chapter 9: Time-Weighted Control Charts · Chapter 10: Advanced Control Charting Techniques · Chapter 11: Multivariate SPC Chapter 12: Process Adjustment and Process monotoring Part V: Process Design and Improvement with Designed Experiments Chapter 13: Basic Experimental Design for Process Improvements Chapter 14: Process Optimization Part VI: Acceptance Sampling · Chapter 15: Basic Acceptance Sampling Procedures Chapter 16: Additional Sampling Procedures Appendix

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