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An accidental statistician : the life and memories of George E.P. Box / / George E. P. Box
| An accidental statistician : the life and memories of George E.P. Box / / George E. P. Box |
| Autore | Box George E. P |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley and Sons, Inc., , 2013 |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina | 519.5092 |
| Soggetto topico | Statisticians - United States |
| ISBN |
1-118-51494-7
1-118-51493-9 1-299-44908-5 1-118-51492-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
An Accidental Statistician; Contents; Foreword; Second Foreword; Preface; Acknowledgments; From The Publisher; 1 Early Years; 2 Army Life; 3 ICI and the Statistical Methods Panel; 4 George Barnard; 5 An Invitation to the United States; 6 Princeton; 7 A New Life in Madison; 8 Time Series; 9 George Tiao and the Bayes Book; 10 Growing Up (Helen and Harry); 11 Fisher-Father and Son; 12 Bill Hunter and Some Ideas on Experimental Design; 13 The Quality Movement; 14 Adventures with Claire; 15 The Many Sides of Mac; 16 Life in England; 17 Journeys to Scandinavia; 18 A Second Home in Spain
19 The Royal Society of London20 Conclusion; 21 Memories; Index |
| Record Nr. | UNINA-9910139028303321 |
Box George E. P
|
||
| Hoboken, New Jersey : , : John Wiley and Sons, Inc., , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An accidental statistician : the life and memories of George E.P. Box / / George E. P. Box
| An accidental statistician : the life and memories of George E.P. Box / / George E. P. Box |
| Autore | Box George E. P |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley and Sons, Inc., , 2013 |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina | 519.5092 |
| Soggetto topico | Statisticians - United States |
| ISBN |
1-118-51494-7
1-118-51493-9 1-299-44908-5 1-118-51492-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
An Accidental Statistician; Contents; Foreword; Second Foreword; Preface; Acknowledgments; From The Publisher; 1 Early Years; 2 Army Life; 3 ICI and the Statistical Methods Panel; 4 George Barnard; 5 An Invitation to the United States; 6 Princeton; 7 A New Life in Madison; 8 Time Series; 9 George Tiao and the Bayes Book; 10 Growing Up (Helen and Harry); 11 Fisher-Father and Son; 12 Bill Hunter and Some Ideas on Experimental Design; 13 The Quality Movement; 14 Adventures with Claire; 15 The Many Sides of Mac; 16 Life in England; 17 Journeys to Scandinavia; 18 A Second Home in Spain
19 The Royal Society of London20 Conclusion; 21 Memories; Index |
| Record Nr. | UNINA-9910818055503321 |
|
Box George E. P
|
||
| Hoboken, New Jersey : , : John Wiley and Sons, Inc., , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao
| Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao |
| Autore | Box George E. P |
| Edizione | [Wiley classics library ed.] |
| Pubbl/distr/stampa | New York, : Wiley, 1992 |
| Descrizione fisica | 1 online resource (610 p.) |
| Disciplina |
519.54
519.542 |
| Altri autori (Persone) | TiaoGeorge C. <1933-> |
| Collana | Wiley Classics Library |
| Soggetto topico | Mathematical statistics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-25164-3
9786613813909 1-118-03319-1 1-118-03144-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
BAYESIAN INFERENCE IN STATISTICAL ANALYSIS; CONTENTS; Chapter 1 Nature of Bayesian Inference; 1.1 Introduction and summary; 1.1.1 The role of statistical methods in scientific investigation; 1.1.2 Statistical inference as one part of statistical analysis; 1.1.3 The question of adequacy of assumptions; 1.1.4 An iterative process of model building in statistical analysis; 1.1.5 The role of Bayesian analysis; 1.2 Nature of Bayesian inference; 1.2.1 Bayes' theorem; 1.2.2 Application of Bayes' theorem with probability interpreted as frequencies
1.2.3 Application of Bayes' theorem with subjective probabilities1.2.4 Bayesian decision problems; 1.2.5 Application of Bayesian analysis to scientific inference; 1.3 Noninformative prior distributions; 1.3.1 The Normal mean θ(σ2 known); 1.3.2 The Normal standard deviation σ(θ known); 1.3.3 Exact data translated likelihoods and noninformative priors; 1.3.4 Approximate data translated likelihood; 1.3.5 Jeffreys' rule, information measure, and noninformative priors; 1.3.6 Noninformative priors for multiple parameters; 1.3.7 Noninformative prior distributions: A summary 1.4 Sufficient statistics1.4.1 Relevance of sufficient statistics in Bayesian inference; 1.4.2 An example using the Cauchy distribution; 1.5 Constraints on parameters; 1.6 Nuisance parameters; 1.6.1 Application to robustness studies; 1.6.2 Caution in integrating out nuisance parameters; 1.7 Systems of inference; 1.7.1 Fiducial inference and likelihood inference; Appendix A1.1 Combination of a Normal prior and a Normal likelihood; Chapter 2 Standard Normal Theory Inference Problems; 2.1 Introduction; 2.1.1 The Normal distribution; 2.1.2 Common Normal-theory problems 2.1.3 Distributional assumptions2.2 Inferences concerning a single mean from observations assuming common known variance; 2.2.1 An example; 2.2.2 Bayesian intervals; 2.2.3 Parallel results from sampling theory; 2.3 Inferences concerning the spread of a Normal distribution from observations having common known mean; 2.3.1 The inverted χ2, inverted χ, and the log χ distributions; 2.3.2 Inferences about the spread of a Normal distribution; 2.3.3 An example; 2.3.4 Relationship to sampling theory results; 2.4 Inferences when both mean and standard deviation are unknown; 2.4.1 An example 2.4.2 Component distributions of p(θ, σ | y)2.4.3 Posterior intervals for θ; 2.4.4 Geometric interpretation of the derivation of p(θ | y); 2.4.5 Informative prior distribution of σ; 2.4.6 Effect of changing the metric of σ for locally uniform prior; 2.4.7 Elimination of the nuisance parameter σ in Bayesian and sampling theories; 2.5 Inferences concerning the difference between two means; 2.5.1 Distribution oft θ2 - θ1 when σ21 = σ22; 2.5.2 Distribution of θ2 - θ1 when σ21 and σ22 are not assumed equal; 2.5.3 Approximations to the Behrens-Fisher distribution; 2.5.4 An example 2.6 Inferences concerning a variance ratio |
| Record Nr. | UNINA-9910139190603321 |
|
Box George E. P
|
||
| New York, : Wiley, 1992 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao
| Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao |
| Autore | Box George E. P |
| Edizione | [Wiley classics library ed.] |
| Pubbl/distr/stampa | New York, : Wiley, 1992 |
| Descrizione fisica | 1 online resource (610 p.) |
| Disciplina |
519.54
519.542 |
| Altri autori (Persone) | TiaoGeorge C. <1933-> |
| Collana | Wiley Classics Library |
| Soggetto topico | Mathematical statistics |
| ISBN |
1-282-25164-3
9786613813909 1-118-03319-1 1-118-03144-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
BAYESIAN INFERENCE IN STATISTICAL ANALYSIS; CONTENTS; Chapter 1 Nature of Bayesian Inference; 1.1 Introduction and summary; 1.1.1 The role of statistical methods in scientific investigation; 1.1.2 Statistical inference as one part of statistical analysis; 1.1.3 The question of adequacy of assumptions; 1.1.4 An iterative process of model building in statistical analysis; 1.1.5 The role of Bayesian analysis; 1.2 Nature of Bayesian inference; 1.2.1 Bayes' theorem; 1.2.2 Application of Bayes' theorem with probability interpreted as frequencies
1.2.3 Application of Bayes' theorem with subjective probabilities1.2.4 Bayesian decision problems; 1.2.5 Application of Bayesian analysis to scientific inference; 1.3 Noninformative prior distributions; 1.3.1 The Normal mean θ(σ2 known); 1.3.2 The Normal standard deviation σ(θ known); 1.3.3 Exact data translated likelihoods and noninformative priors; 1.3.4 Approximate data translated likelihood; 1.3.5 Jeffreys' rule, information measure, and noninformative priors; 1.3.6 Noninformative priors for multiple parameters; 1.3.7 Noninformative prior distributions: A summary 1.4 Sufficient statistics1.4.1 Relevance of sufficient statistics in Bayesian inference; 1.4.2 An example using the Cauchy distribution; 1.5 Constraints on parameters; 1.6 Nuisance parameters; 1.6.1 Application to robustness studies; 1.6.2 Caution in integrating out nuisance parameters; 1.7 Systems of inference; 1.7.1 Fiducial inference and likelihood inference; Appendix A1.1 Combination of a Normal prior and a Normal likelihood; Chapter 2 Standard Normal Theory Inference Problems; 2.1 Introduction; 2.1.1 The Normal distribution; 2.1.2 Common Normal-theory problems 2.1.3 Distributional assumptions2.2 Inferences concerning a single mean from observations assuming common known variance; 2.2.1 An example; 2.2.2 Bayesian intervals; 2.2.3 Parallel results from sampling theory; 2.3 Inferences concerning the spread of a Normal distribution from observations having common known mean; 2.3.1 The inverted χ2, inverted χ, and the log χ distributions; 2.3.2 Inferences about the spread of a Normal distribution; 2.3.3 An example; 2.3.4 Relationship to sampling theory results; 2.4 Inferences when both mean and standard deviation are unknown; 2.4.1 An example 2.4.2 Component distributions of p(θ, σ | y)2.4.3 Posterior intervals for θ; 2.4.4 Geometric interpretation of the derivation of p(θ | y); 2.4.5 Informative prior distribution of σ; 2.4.6 Effect of changing the metric of σ for locally uniform prior; 2.4.7 Elimination of the nuisance parameter σ in Bayesian and sampling theories; 2.5 Inferences concerning the difference between two means; 2.5.1 Distribution oft θ2 - θ1 when σ21 = σ22; 2.5.2 Distribution of θ2 - θ1 when σ21 and σ22 are not assumed equal; 2.5.3 Approximations to the Behrens-Fisher distribution; 2.5.4 An example 2.6 Inferences concerning a variance ratio |
| Record Nr. | UNINA-9910677310003321 |
|
Box George E. P
|
||
| New York, : Wiley, 1992 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao
| Bayesian inference in statistical analysis [[electronic resource] /] / George E.P. Box, George C. Tiao |
| Autore | Box George E. P |
| Edizione | [Wiley classics library ed.] |
| Pubbl/distr/stampa | New York, : Wiley, 1992 |
| Descrizione fisica | 1 online resource (610 p.) |
| Disciplina |
519.54
519.542 |
| Altri autori (Persone) | TiaoGeorge C. <1933-> |
| Collana | Wiley Classics Library |
| Soggetto topico | Mathematical statistics |
| ISBN |
1-282-25164-3
9786613813909 1-118-03319-1 1-118-03144-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
BAYESIAN INFERENCE IN STATISTICAL ANALYSIS; CONTENTS; Chapter 1 Nature of Bayesian Inference; 1.1 Introduction and summary; 1.1.1 The role of statistical methods in scientific investigation; 1.1.2 Statistical inference as one part of statistical analysis; 1.1.3 The question of adequacy of assumptions; 1.1.4 An iterative process of model building in statistical analysis; 1.1.5 The role of Bayesian analysis; 1.2 Nature of Bayesian inference; 1.2.1 Bayes' theorem; 1.2.2 Application of Bayes' theorem with probability interpreted as frequencies
1.2.3 Application of Bayes' theorem with subjective probabilities1.2.4 Bayesian decision problems; 1.2.5 Application of Bayesian analysis to scientific inference; 1.3 Noninformative prior distributions; 1.3.1 The Normal mean θ(σ2 known); 1.3.2 The Normal standard deviation σ(θ known); 1.3.3 Exact data translated likelihoods and noninformative priors; 1.3.4 Approximate data translated likelihood; 1.3.5 Jeffreys' rule, information measure, and noninformative priors; 1.3.6 Noninformative priors for multiple parameters; 1.3.7 Noninformative prior distributions: A summary 1.4 Sufficient statistics1.4.1 Relevance of sufficient statistics in Bayesian inference; 1.4.2 An example using the Cauchy distribution; 1.5 Constraints on parameters; 1.6 Nuisance parameters; 1.6.1 Application to robustness studies; 1.6.2 Caution in integrating out nuisance parameters; 1.7 Systems of inference; 1.7.1 Fiducial inference and likelihood inference; Appendix A1.1 Combination of a Normal prior and a Normal likelihood; Chapter 2 Standard Normal Theory Inference Problems; 2.1 Introduction; 2.1.1 The Normal distribution; 2.1.2 Common Normal-theory problems 2.1.3 Distributional assumptions2.2 Inferences concerning a single mean from observations assuming common known variance; 2.2.1 An example; 2.2.2 Bayesian intervals; 2.2.3 Parallel results from sampling theory; 2.3 Inferences concerning the spread of a Normal distribution from observations having common known mean; 2.3.1 The inverted χ2, inverted χ, and the log χ distributions; 2.3.2 Inferences about the spread of a Normal distribution; 2.3.3 An example; 2.3.4 Relationship to sampling theory results; 2.4 Inferences when both mean and standard deviation are unknown; 2.4.1 An example 2.4.2 Component distributions of p(θ, σ | y)2.4.3 Posterior intervals for θ; 2.4.4 Geometric interpretation of the derivation of p(θ | y); 2.4.5 Informative prior distribution of σ; 2.4.6 Effect of changing the metric of σ for locally uniform prior; 2.4.7 Elimination of the nuisance parameter σ in Bayesian and sampling theories; 2.5 Inferences concerning the difference between two means; 2.5.1 Distribution oft θ2 - θ1 when σ21 = σ22; 2.5.2 Distribution of θ2 - θ1 when σ21 and σ22 are not assumed equal; 2.5.3 Approximations to the Behrens-Fisher distribution; 2.5.4 An example 2.6 Inferences concerning a variance ratio |
| Record Nr. | UNISA-996213512303316 |
|
Box George E. P
|
||
| New York, : Wiley, 1992 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper
| Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper |
| Autore | Box George E. P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2007 |
| Descrizione fisica | 1 online resource (873 p.) |
| Disciplina | 519.57 |
| Altri autori (Persone) | DraperNorman Richard |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Experimental design
Response surfaces (Statistics) Mixture distributions (Probability theory) Ridge regression (Statistics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-24228-8
9786613813404 0-470-07276-8 0-470-07275-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Response Surfaces, Mixtures, and Ridge Analyses; Contents; Preface to the Second Edition; 1. Introduction to Response Surface Methodology; 1.1. Response Surface Methodology (RSM); 1.2. Indeterminancy of Experimentation; 1.3. Iterative Nature of the Experimental Learning Process; 1.4. Some Classes of Problems (Which, How, Why); 1.5. Need for Experimental Design; 1.6. Geometric Representation of Response Relationships; 1.7. Three Kinds of Applications; 2. The Use Of Graduating Functions; 2.1. Approximating Response Functions; 2.2. An Example; Appendix 2A. A Theoretical Response Function
3. Least Squares for Response Surface Work3.1. The Method of Least Squares; 3.2. Linear Models; 3.3. Matrix Formulas for Least Squares; 3.4. Geometry of Least Squares; 3.5. Analysis of Variance for One Regressor; 3.6. Least Squares for Two Regressors; 3.7. Geometry of the Analysis of Variance for Two Regressors; 3.8. Orthogonalizing the Second Regressor, Extra Sum of Squares Principle; 3.9. Generalization to p Regressors; 3.10. Bias in Least-Squares Estimators Arising from an Inadequate Model; 3.11. Pure Error and Lack of Fit; 3.12. Confidence Intervals and Confidence Regions 3.13. Robust Estimation, Maximum Likelihood, and Least SquaresAppendix 3A. Iteratively Reweighted Least Squares; Appendix 3B. Justification of Least Squares by the Gauss-Markov Theorem; Robustness; Appendix 3C. Matrix Theory; Appendix 3D. Nonlinear Estimation; Appendix 3E. Results Involving V(y); Exercises; 4. Factorial Designs at Two Levels; 4.1. The Value of Factorial Designs; 4.2. Two-Level Factorials; 4.3. A 2(6) Design Used in a Study of Dyestuffs Manufacture; 4.4. Diagnostic Checking of the Fitted Models, 2(6) Dyestuffs Example; 4.5. Response Surface Analysis of the 2(6) Design Data Appendix 4A. Yates' Method for Obtaining the Factorial Effects for a Two-Level DesignAppendix 4B. Normal Plots on Probability Paper; Appendix 4C. Confidence Regions for Contour Planes (see Section 4.5); Exercises; 5. Blocking and Fractionating 2(k) Factorial Designs; 5.1. Blocking the 2(6) Design; 5.2. Fractionating the 2(6) Design; 5.3. Resolution of a 2(k-p) Factorial Design; 5.4. Construction of 2(k-p) Designs of Resolution III and IV; 5.5. Combination of Designs from the Same Family; 5.6. Screening, Using 2(k-p) Designs (Involving Projections to Lower Dimensions) 5.7. Complete Factorials Within Fractional Factorial Designs5.8. Plackett and Burman Designs for n = 12 to 60 (but not 52); 5.9. Screening, Using Plackett and Burman Designs (Involving Projections to Lower Dimensions); 5.10. Efficient Estimation of Main Effects and Two-Factor Interactions Using Relatively Small Two-Level Designs; 5.11. Designs of Resolution V and of Higher Resolution; 5.12. Application of Fractional Factorial Designs to Response Surface Methodology; 5.13. Plotting Effects from Fractional Factorials on Probability Paper; Exercises 6. The Use of Steepest Ascent to Achieve Process Improvement |
| Record Nr. | UNINA-9910143691403321 |
|
Box George E. P
|
||
| Hoboken, N.J., : John Wiley, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper
| Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper |
| Autore | Box George E. P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2007 |
| Descrizione fisica | 1 online resource (873 p.) |
| Disciplina | 519.57 |
| Altri autori (Persone) | DraperNorman Richard |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Experimental design
Response surfaces (Statistics) Mixture distributions (Probability theory) Ridge regression (Statistics) |
| ISBN |
1-282-24228-8
9786613813404 0-470-07276-8 0-470-07275-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Response Surfaces, Mixtures, and Ridge Analyses; Contents; Preface to the Second Edition; 1. Introduction to Response Surface Methodology; 1.1. Response Surface Methodology (RSM); 1.2. Indeterminancy of Experimentation; 1.3. Iterative Nature of the Experimental Learning Process; 1.4. Some Classes of Problems (Which, How, Why); 1.5. Need for Experimental Design; 1.6. Geometric Representation of Response Relationships; 1.7. Three Kinds of Applications; 2. The Use Of Graduating Functions; 2.1. Approximating Response Functions; 2.2. An Example; Appendix 2A. A Theoretical Response Function
3. Least Squares for Response Surface Work3.1. The Method of Least Squares; 3.2. Linear Models; 3.3. Matrix Formulas for Least Squares; 3.4. Geometry of Least Squares; 3.5. Analysis of Variance for One Regressor; 3.6. Least Squares for Two Regressors; 3.7. Geometry of the Analysis of Variance for Two Regressors; 3.8. Orthogonalizing the Second Regressor, Extra Sum of Squares Principle; 3.9. Generalization to p Regressors; 3.10. Bias in Least-Squares Estimators Arising from an Inadequate Model; 3.11. Pure Error and Lack of Fit; 3.12. Confidence Intervals and Confidence Regions 3.13. Robust Estimation, Maximum Likelihood, and Least SquaresAppendix 3A. Iteratively Reweighted Least Squares; Appendix 3B. Justification of Least Squares by the Gauss-Markov Theorem; Robustness; Appendix 3C. Matrix Theory; Appendix 3D. Nonlinear Estimation; Appendix 3E. Results Involving V(y); Exercises; 4. Factorial Designs at Two Levels; 4.1. The Value of Factorial Designs; 4.2. Two-Level Factorials; 4.3. A 2(6) Design Used in a Study of Dyestuffs Manufacture; 4.4. Diagnostic Checking of the Fitted Models, 2(6) Dyestuffs Example; 4.5. Response Surface Analysis of the 2(6) Design Data Appendix 4A. Yates' Method for Obtaining the Factorial Effects for a Two-Level DesignAppendix 4B. Normal Plots on Probability Paper; Appendix 4C. Confidence Regions for Contour Planes (see Section 4.5); Exercises; 5. Blocking and Fractionating 2(k) Factorial Designs; 5.1. Blocking the 2(6) Design; 5.2. Fractionating the 2(6) Design; 5.3. Resolution of a 2(k-p) Factorial Design; 5.4. Construction of 2(k-p) Designs of Resolution III and IV; 5.5. Combination of Designs from the Same Family; 5.6. Screening, Using 2(k-p) Designs (Involving Projections to Lower Dimensions) 5.7. Complete Factorials Within Fractional Factorial Designs5.8. Plackett and Burman Designs for n = 12 to 60 (but not 52); 5.9. Screening, Using Plackett and Burman Designs (Involving Projections to Lower Dimensions); 5.10. Efficient Estimation of Main Effects and Two-Factor Interactions Using Relatively Small Two-Level Designs; 5.11. Designs of Resolution V and of Higher Resolution; 5.12. Application of Fractional Factorial Designs to Response Surface Methodology; 5.13. Plotting Effects from Fractional Factorials on Probability Paper; Exercises 6. The Use of Steepest Ascent to Achieve Process Improvement |
| Record Nr. | UNINA-9910829973703321 |
|
Box George E. P
|
||
| Hoboken, N.J., : John Wiley, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper
| Response surfaces, mixtures, and ridge analyses [[electronic resource] /] / George E.P. Box, Norman R. Draper |
| Autore | Box George E. P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2007 |
| Descrizione fisica | 1 online resource (873 p.) |
| Disciplina | 519.57 |
| Altri autori (Persone) | DraperNorman Richard |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Experimental design
Response surfaces (Statistics) Mixture distributions (Probability theory) Ridge regression (Statistics) |
| ISBN |
1-282-24228-8
9786613813404 0-470-07276-8 0-470-07275-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Response Surfaces, Mixtures, and Ridge Analyses; Contents; Preface to the Second Edition; 1. Introduction to Response Surface Methodology; 1.1. Response Surface Methodology (RSM); 1.2. Indeterminancy of Experimentation; 1.3. Iterative Nature of the Experimental Learning Process; 1.4. Some Classes of Problems (Which, How, Why); 1.5. Need for Experimental Design; 1.6. Geometric Representation of Response Relationships; 1.7. Three Kinds of Applications; 2. The Use Of Graduating Functions; 2.1. Approximating Response Functions; 2.2. An Example; Appendix 2A. A Theoretical Response Function
3. Least Squares for Response Surface Work3.1. The Method of Least Squares; 3.2. Linear Models; 3.3. Matrix Formulas for Least Squares; 3.4. Geometry of Least Squares; 3.5. Analysis of Variance for One Regressor; 3.6. Least Squares for Two Regressors; 3.7. Geometry of the Analysis of Variance for Two Regressors; 3.8. Orthogonalizing the Second Regressor, Extra Sum of Squares Principle; 3.9. Generalization to p Regressors; 3.10. Bias in Least-Squares Estimators Arising from an Inadequate Model; 3.11. Pure Error and Lack of Fit; 3.12. Confidence Intervals and Confidence Regions 3.13. Robust Estimation, Maximum Likelihood, and Least SquaresAppendix 3A. Iteratively Reweighted Least Squares; Appendix 3B. Justification of Least Squares by the Gauss-Markov Theorem; Robustness; Appendix 3C. Matrix Theory; Appendix 3D. Nonlinear Estimation; Appendix 3E. Results Involving V(y); Exercises; 4. Factorial Designs at Two Levels; 4.1. The Value of Factorial Designs; 4.2. Two-Level Factorials; 4.3. A 2(6) Design Used in a Study of Dyestuffs Manufacture; 4.4. Diagnostic Checking of the Fitted Models, 2(6) Dyestuffs Example; 4.5. Response Surface Analysis of the 2(6) Design Data Appendix 4A. Yates' Method for Obtaining the Factorial Effects for a Two-Level DesignAppendix 4B. Normal Plots on Probability Paper; Appendix 4C. Confidence Regions for Contour Planes (see Section 4.5); Exercises; 5. Blocking and Fractionating 2(k) Factorial Designs; 5.1. Blocking the 2(6) Design; 5.2. Fractionating the 2(6) Design; 5.3. Resolution of a 2(k-p) Factorial Design; 5.4. Construction of 2(k-p) Designs of Resolution III and IV; 5.5. Combination of Designs from the Same Family; 5.6. Screening, Using 2(k-p) Designs (Involving Projections to Lower Dimensions) 5.7. Complete Factorials Within Fractional Factorial Designs5.8. Plackett and Burman Designs for n = 12 to 60 (but not 52); 5.9. Screening, Using Plackett and Burman Designs (Involving Projections to Lower Dimensions); 5.10. Efficient Estimation of Main Effects and Two-Factor Interactions Using Relatively Small Two-Level Designs; 5.11. Designs of Resolution V and of Higher Resolution; 5.12. Application of Fractional Factorial Designs to Response Surface Methodology; 5.13. Plotting Effects from Fractional Factorials on Probability Paper; Exercises 6. The Use of Steepest Ascent to Achieve Process Improvement |
| Record Nr. | UNINA-9910841435403321 |
|
Box George E. P
|
||
| Hoboken, N.J., : John Wiley, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical control by monitoring and feedback adjustment [[electronic resource] /] / George E.P. Box, Alberto Luceño, Maria del Carmen Paniagua-Quiñones
| Statistical control by monitoring and feedback adjustment [[electronic resource] /] / George E.P. Box, Alberto Luceño, Maria del Carmen Paniagua-Quiñones |
| Autore | Box George E. P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (358 p.) |
| Disciplina |
629.8/3
629.83 |
| Altri autori (Persone) |
LuceñoAlberto
Paniagua-QuiñonesMaría del Carmen |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Feedback control systems
Process control - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-27393-4
9786613273932 1-118-16453-9 1-118-16446-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical Control by Monitoring and Adjustment, Second Edition; Contents; Preface; 1 Introduction and Revision of Some Statistical Ideas; 1.1 Necessity for Process Control; 1.2 SPC and EPC; 1.3 Process Monitoring Without a Model; 1.4 Detecting a Signal in Noise; 1.5 Measurement Data; 1.6 Two Important Characteristics of a Probability Distribution; 1.7 Normal Distribution; 1.8 Normal Distribution Defined by μ and σ; 1.9 Probabilities Associated with Normal Distribution; 1.10 Estimating Mean and Standard Deviation from Data; 1.11 Combining Estimates of σ2
1.12 Data on Frequencies (Events): Poisson Distribution1.13 Normal Approximation to Poisson Distribution; 1.14 Data on Proportion Defective: Binomial Distribution; 1.15 Normal Approximation to Binomial Distribution; Appendix 1A: Central Limit Effect; Problems; 2 Standard Control Charts Under Ideal Conditions As a First Approximation; 2.1 Control Charts for Process Monitoring; 2.2 Control Chart for Measurement (Variables) Data; 2.3 Shewhart Charts for Sample Average and Range; 2.4 Shewhart Chart for Sample Range; 2.5 Process Monitoring With Control Charts for Frequencies 2.6 Data on Frequencies (Counts): Poisson Distribution2.7 Common Causes and Special Causes; 2.8 For What Kinds of Data Has the c Chart Been Used?; 2.9 Quality Control Charts for Proportions: p Chart; 2.10 EWMA Chart; 2.11 Process Monitoring Using Cumulative Sums; 2.12 Specification Limits, Target Accuracy, and Process Capability; 2.13 How Successful Process Monitoring can Improve Quality; Problems; 3 What Can Go Wrong and What Can We Do About It?; 3.1 Introduction; 3.2 Measurement Charts; 3.3 Need for Time Series Models; 3.4 Types of Variation; 3.5 Nonstationary Noise 3.6 Values for constants3.7 Frequencies and Proportions; 3.8 Illustration; 3.9 Robustness of EWMA; Appendix 3A: Alternative Forms of Relationships for EWMAs; Questions; 4 Introduction to Forecasting and Process Dynamics; 4.1 Forecasting with an EWMA; 4.2 Forecasting Sales of Dingles; 4.3 Pete's Rule; 4.4 Effect of Changing Discount Factor; 4.5 Estimating Best Discount Factor; 4.6 Standard Deviation of Forecast Errors and Probability Limits for Forecasts; 4.7 What to Do If You Do Not Have Enough Data to Estimate θ; 4.8 Introduction to Process Dynamics and Transfer Function 4.9 Dynamic Systems and Transfer Funtions4.10 Difference Equations to Represent Dynamic Relations; 4.11 Representing Dynamics of Industrial Process; 4.12 Transfer Function Models Using Difference Equations; 4.13 Stable and Unstable Systems; Problems; 5 Nonstationary Time Series Models for Process Disturbances; 5.1 Reprise; 5.2 Stationary Time Series Model in Which Successive Values Are Correlated; 5.3 Major Effects of Statistical Dependence: Illustration; 5.4 Random Walk; 5.5 How to Test a Forecasting Method; 5.6 Qualification of EWMA As a Forecast 5.7 Understanding Time Series Behavior with Variogram |
| Record Nr. | UNINA-9910139597903321 |
|
Box George E. P
|
||
| Hoboken, N.J., : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical control by monitoring and feedback adjustment [[electronic resource] /] / George E.P. Box, Alberto Luceño, Maria del Carmen Paniagua-Quiñones
| Statistical control by monitoring and feedback adjustment [[electronic resource] /] / George E.P. Box, Alberto Luceño, Maria del Carmen Paniagua-Quiñones |
| Autore | Box George E. P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (358 p.) |
| Disciplina |
629.8/3
629.83 |
| Altri autori (Persone) |
LuceñoAlberto
Paniagua-QuiñonesMaría del Carmen |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Feedback control systems
Process control - Statistical methods |
| ISBN |
1-283-27393-4
9786613273932 1-118-16453-9 1-118-16446-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical Control by Monitoring and Adjustment, Second Edition; Contents; Preface; 1 Introduction and Revision of Some Statistical Ideas; 1.1 Necessity for Process Control; 1.2 SPC and EPC; 1.3 Process Monitoring Without a Model; 1.4 Detecting a Signal in Noise; 1.5 Measurement Data; 1.6 Two Important Characteristics of a Probability Distribution; 1.7 Normal Distribution; 1.8 Normal Distribution Defined by μ and σ; 1.9 Probabilities Associated with Normal Distribution; 1.10 Estimating Mean and Standard Deviation from Data; 1.11 Combining Estimates of σ2
1.12 Data on Frequencies (Events): Poisson Distribution1.13 Normal Approximation to Poisson Distribution; 1.14 Data on Proportion Defective: Binomial Distribution; 1.15 Normal Approximation to Binomial Distribution; Appendix 1A: Central Limit Effect; Problems; 2 Standard Control Charts Under Ideal Conditions As a First Approximation; 2.1 Control Charts for Process Monitoring; 2.2 Control Chart for Measurement (Variables) Data; 2.3 Shewhart Charts for Sample Average and Range; 2.4 Shewhart Chart for Sample Range; 2.5 Process Monitoring With Control Charts for Frequencies 2.6 Data on Frequencies (Counts): Poisson Distribution2.7 Common Causes and Special Causes; 2.8 For What Kinds of Data Has the c Chart Been Used?; 2.9 Quality Control Charts for Proportions: p Chart; 2.10 EWMA Chart; 2.11 Process Monitoring Using Cumulative Sums; 2.12 Specification Limits, Target Accuracy, and Process Capability; 2.13 How Successful Process Monitoring can Improve Quality; Problems; 3 What Can Go Wrong and What Can We Do About It?; 3.1 Introduction; 3.2 Measurement Charts; 3.3 Need for Time Series Models; 3.4 Types of Variation; 3.5 Nonstationary Noise 3.6 Values for constants3.7 Frequencies and Proportions; 3.8 Illustration; 3.9 Robustness of EWMA; Appendix 3A: Alternative Forms of Relationships for EWMAs; Questions; 4 Introduction to Forecasting and Process Dynamics; 4.1 Forecasting with an EWMA; 4.2 Forecasting Sales of Dingles; 4.3 Pete's Rule; 4.4 Effect of Changing Discount Factor; 4.5 Estimating Best Discount Factor; 4.6 Standard Deviation of Forecast Errors and Probability Limits for Forecasts; 4.7 What to Do If You Do Not Have Enough Data to Estimate θ; 4.8 Introduction to Process Dynamics and Transfer Function 4.9 Dynamic Systems and Transfer Funtions4.10 Difference Equations to Represent Dynamic Relations; 4.11 Representing Dynamics of Industrial Process; 4.12 Transfer Function Models Using Difference Equations; 4.13 Stable and Unstable Systems; Problems; 5 Nonstationary Time Series Models for Process Disturbances; 5.1 Reprise; 5.2 Stationary Time Series Model in Which Successive Values Are Correlated; 5.3 Major Effects of Statistical Dependence: Illustration; 5.4 Random Walk; 5.5 How to Test a Forecasting Method; 5.6 Qualification of EWMA As a Forecast 5.7 Understanding Time Series Behavior with Variogram |
| Record Nr. | UNINA-9910831048103321 |
|
Box George E. P
|
||
| Hoboken, N.J., : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
