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Biblioteca

MARC Lista (tabellare)
25 need-to-know key performance indicators / / Bernard Marr
25 need-to-know key performance indicators / / Bernard Marr
Autore Marr Bernard
Edizione [1st edition]
Pubbl/distr/stampa Harlow, England : , : Pearson, , 2014
Descrizione fisica 1 online resource (225 pages) : illustrations
Disciplina 658.4/013
Soggetto topico Management - Statistical methods
Performance
Business planning
Benchmarking (Management)
ISBN 1-292-01649-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Contents -- About the author -- Publisher's acknowledgements -- Introduction -- Top 10 do's and don'ts of using key performance indicators -- Part 1: Measuring and understanding your customers -- 1 Net promoter score (NPS) -- 2 Customer profitability score -- 3 Customer retention rate -- 4 Conversion rate -- 5 Relative market share -- Part 2: Measuring and understanding your financial -- 6 Revenue growth rate -- 7 Net profit -- 8 Net profit margin -- 9 Gross profit margin -- 10 Operating profit margin -- 11 Return on investment (ROI) -- 12 Cash conversion cycle (CCC) -- Part 3: Measuring and understanding your internal processes -- 13 Capacity utilisation rate (CUR) -- 14 Project schedule variance (PSV) -- 15 Project cost variance (PCV) -- 16 Earned value (EV) metric -- 17 Order fulfilment cycle time (OFCT) -- 18 Delivery in full, on time (DIFOT) rate -- 19 Quality index -- 20 Process downtime level -- Part 4: Measuring and understanding your employees -- 21 Staff advocacy score -- 22 Employee engagement level -- 23 Absenteeism Bradford Factor -- 24 Human capital value added (HCVA) -- 25 360-degree feedback score -- 10 useful books to read next -- Glossary of terms -- Index.
Altri titoli varianti Twenty five need-to-know key performance indicators
Record Nr. UNINA-9910151789903321
Marr Bernard  
Harlow, England : , : Pearson, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
25 need-to-know management ratios / / Ciaran Walsh and Stuart Warner
25 need-to-know management ratios / / Ciaran Walsh and Stuart Warner
Autore Walsh Ciaran
Pubbl/distr/stampa Harlow, England : , : Pearson, , 2015
Descrizione fisica 1 online resource (193 pages)
Disciplina 658.001/512924
Soggetto topico Ratio analysis
Management - Statistical methods
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Twenty-five need-to-know management ratios
Record Nr. UNINA-9910153239003321
Walsh Ciaran  
Harlow, England : , : Pearson, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics and AI : impact, implementation, and the future of work / / by Tony Boobier
Advanced analytics and AI : impact, implementation, and the future of work / / by Tony Boobier
Autore Boobier Tony <1956->
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons, , 2018
Descrizione fisica 1 online resource (307 pages)
Disciplina 658.0072/7
Collana Wiley finance series
Soggetto topico Management - Statistical methods
Artificial intelligence - Industrial applications
ISBN 1-119-39093-1
1-119-39096-6
1-119-39092-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910271026903321
Boobier Tony <1956->  
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics and AI : impact, implementation, and the future of work / / by Tony Boobier
Advanced analytics and AI : impact, implementation, and the future of work / / by Tony Boobier
Autore Boobier Tony <1956->
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons, , 2018
Descrizione fisica 1 online resource (307 pages)
Disciplina 658.0072/7
Collana Wiley finance series
Soggetto topico Management - Statistical methods
Artificial intelligence - Industrial applications
ISBN 1-119-39093-1
1-119-39096-6
1-119-39092-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910816479403321
Boobier Tony <1956->  
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
Autore Baesens Bart
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (xv, 232 pages) : illustrations
Disciplina 658.4/038
Collana Wiley & SAS Business Series
Soggetto topico Big data
Management - Statistical methods
Management - Data processing
Decision making - Data processing
ISBN 1-118-89274-7
1-118-89271-2
1-119-20418-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Big data and analytics -- 2. Data collection, sampling, and preprocessing -- 3. Predictive analytics -- 4. Descriptive analytics -- 5. Survival analysis -- 6. Social network analytics -- 7. Analytics : putting it all to work -- 8. Example applications.
Record Nr. UNINA-9910141723703321
Baesens Bart  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
Autore Baesens Bart
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (xv, 232 pages) : illustrations
Disciplina 658.4/038
Collana Wiley & SAS Business Series
Soggetto topico Big data
Management - Statistical methods
Management - Data processing
Decision making - Data processing
ISBN 1-118-89274-7
1-118-89271-2
1-119-20418-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Big data and analytics -- 2. Data collection, sampling, and preprocessing -- 3. Predictive analytics -- 4. Descriptive analytics -- 5. Survival analysis -- 6. Social network analytics -- 7. Analytics : putting it all to work -- 8. Example applications.
Record Nr. UNINA-9910813918803321
Baesens Bart  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The analytics lifecycle toolkit : a practical guide for an effective analytics capability / / by Gregory S. Nelson
The analytics lifecycle toolkit : a practical guide for an effective analytics capability / / by Gregory S. Nelson
Autore Nelson Gregory S. <1964->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2018
Descrizione fisica 1 online resource (467 pages)
Disciplina 658.4/033
Collana Wiley & SAS Business Series
Soggetto topico Management - Statistical methods
Management - Data processing
Decision making - Data processing
ISBN 1-119-42510-7
1-119-42509-3
1-119-42508-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analytics overview -- The people of analytics -- Organizational context for analytics -- Data strategy, platforms, and architecture -- The analytics lifecycle toolkit -- Problem framing -- Data sensemaking -- Analytic model development -- Results activation -- Analytics product management -- Actioning analytics -- Core competencies for analytic teams -- The future of analytics.
Record Nr. UNINA-9910271037303321
Nelson Gregory S. <1964->  
Hoboken, New Jersey : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The analytics lifecycle toolkit : a practical guide for an effective analytics capability / / by Gregory S. Nelson
The analytics lifecycle toolkit : a practical guide for an effective analytics capability / / by Gregory S. Nelson
Autore Nelson Gregory S. <1964->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2018
Descrizione fisica 1 online resource (467 pages)
Disciplina 658.4/033
Collana Wiley & SAS Business Series
Soggetto topico Management - Statistical methods
Management - Data processing
Decision making - Data processing
ISBN 1-119-42510-7
1-119-42509-3
1-119-42508-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analytics overview -- The people of analytics -- Organizational context for analytics -- Data strategy, platforms, and architecture -- The analytics lifecycle toolkit -- Problem framing -- Data sensemaking -- Analytic model development -- Results activation -- Analytics product management -- Actioning analytics -- Core competencies for analytic teams -- The future of analytics.
Record Nr. UNINA-9910815447903321
Nelson Gregory S. <1964->  
Hoboken, New Jersey : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn
Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn
Autore Hahn Eugene D.
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (787 p.)
Disciplina 650.01/519542
Soggetto topico Management - Statistical methods
Commercial statistics
Bayesian statistical decision theory
Soggetto genere / forma Electronic books.
ISBN 1-118-93519-5
Classificazione MAT029010
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: 1 Introduction to Bayesian Methods 1 1.1 Bayesian Methods: An Aerial Survey 1 1.2 Bayes' Theorem 4 1.3 Bayes' Theorem and the Focus Group 6 1.4 The Flavors of Probability 9 1.5 Summary 12 1.6 Notation Introduced in This Chapter 12 2 A First Look at Bayesian Computation 13 2.1 Getting Started 13 2.2 Selecting the Likelihood Function 14 2.3 Selecting the Functional Form 18 2.4 Selecting the Prior 19 2.5 Finding the Normalizing Constant 20 2.6 Obtaining the Posterior 20 2.7 Communicating Findings 25 2.8 Predicting Future Outcomes 28 2.9 Summary 30 2.10 Exercises 31 2.11 Notation Introduced in This Chapter 32 3 Computer-Assisted Bayesian Computation 33 3.1 Getting Started 33 3.2 Random Number Sequences 34 3.3 Monte Carlo Integration 36 3.4 Monte Carlo Simulation for Inference 40 3.5 The Conjugate Normal Model 44 3.6 In Practice: The Conjugate Normal Model 50 3.7 Count Data and the Conjugate Poisson Model 57 3.8 Summary 61 3.9 Exercises 62 3.10 Notation Introduced in This Chapter 63 3.11 Appendix - In Detail: Finding Posterior Distributions for the Normal Model 63 4 MCMC and Regression Models 71 4.1 Introduction to Markov Chain Monte Carlo 71 4.2 Fundamentals of MCMC 73 4.3 Gibbs Sampling 75 4.4 Gibbs Sampling and the Simple Linear Regression Model 82 4.5 In Practice: The Simple Linear Regression Model 85 4.6 The Metropolis Algorithm 88 4.7 Hastings' Extension of the Metropolis Algorithm 97 4.8 Summary 102 4.9 Exercises 103 5 Estimating Bayesian Models with WinBUGS 105 5.1 An Introduction to WinBUGS 106 5.2 In Practice: A First WinBUGS Model 107 5.3 In Practice: Models for the Mean in WinBUGS 117 5.4 Examining the Prior with Sensitivity Analysis 125 5.5 In Practice: Examining Proportions in WinBUGS 136 5.6 Analysis of Variance Models 142 5.7 Higher-order ANOVA Models 155 5.8 Regression and ANCOVA Models in WinBUGS 163 5.9 Summary 171 5.10 Chapter Appendix: Exporting WinBUGS MCMC Output to R 171 5.11 Exercises 173 6 Assessing MCMC Performance in WinBUGS 175 6.1 Convergence Issues in MCMC Modeling 175 6.2 Output Diagnostics in WinBUGS 178 6.3 Reparameterizing to Improve Convergence 181 6.4 Number and Length of Chains 186 6.5 Metropolis-Hastings Acceptance Rates 197 6.6 Summary 199 6.7 Exercises 200 7 Model Checking and Model Comparison 203 7.1 Graphical Model Checking 203 7.2 Predictive Densities and Checking Model Assumptions 209 7.3 Variable Selection Methods 216 7.4 Bayes Factors and BIC 227 7.5 Deviance Information Criterion 234 7.6 Summary 241 7.7 Exercises 241 8 Hierarchical Models 243 8.1 Fundamentals of Hierarchical Models 243 8.2 The Random Coefficients Model 256 8.3 Hierarchical Models for Variance Terms 267 8.4 Functional Forms at Multiple Hierarchical Levels 273 8.5 In Detail: Modeling Covarying Hierarchical Terms 279 8.6 Summary 286 8.7 Exercises 286 8.8 Notation Introduced in This Chapter 288 9 Generalized Linear Models 289 9.1 Fundamentals of Generalized Linear Models 289 9.2 Count Data Models: Poisson Regression 292 9.3 Models for Binary Data: Logistic Regression 296 9.4 The Probit Model 303 9.5 In Detail: Multinomial Logistic Regression for Categorical Outcomes 306 9.6 Hierarchical Models for Count Data 314 9.7 Hierarchical Models for Binary Data 320 9.8 Summary 324 9.9 Exercises 325 9.10 Notation Introduced in This Chapter 327 10 Models for Difficult Data 329 10.1 Living with Outliers-Robust Regression Models 329 10.2 Handling Heteroscedasticity by Modeling Variance Parameters 340 10.3 Dealing with Missing Data 345 10.4 Types of Missing Data 349 10.5 Missing Covariate Data and Non-Normal Missing Data 357 10.6 Summary 358 10.7 Exercises 359 10.8 Notation Introduced in This Chapter 360 11 Introduction to Latent Variable Models 361 11.1 Not Seen but Felt 361 11.2 Latent Variable Models for Binary Data 362 11.3 Structural Break Models 366 11.4 In Detail: The Ordinal Probit Model 376 11.5 Summary 383 11.6 Exercises 383 A Common Statistical Distributions 385 Bibliography 389 Author Index 403 Subject Index 407 .
Record Nr. UNINA-9910465440303321
Hahn Eugene D.  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn
Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn
Autore Hahn Eugene D.
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (787 p.)
Disciplina 650.01/519542
Collana New York Academy of Sciences
Soggetto topico Management - Statistical methods
Commercial statistics
Bayesian statistical decision theory
ISBN 1-118-93519-5
Classificazione 336.1
650.01/519542
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: 1 Introduction to Bayesian Methods 1 1.1 Bayesian Methods: An Aerial Survey 1 1.2 Bayes' Theorem 4 1.3 Bayes' Theorem and the Focus Group 6 1.4 The Flavors of Probability 9 1.5 Summary 12 1.6 Notation Introduced in This Chapter 12 2 A First Look at Bayesian Computation 13 2.1 Getting Started 13 2.2 Selecting the Likelihood Function 14 2.3 Selecting the Functional Form 18 2.4 Selecting the Prior 19 2.5 Finding the Normalizing Constant 20 2.6 Obtaining the Posterior 20 2.7 Communicating Findings 25 2.8 Predicting Future Outcomes 28 2.9 Summary 30 2.10 Exercises 31 2.11 Notation Introduced in This Chapter 32 3 Computer-Assisted Bayesian Computation 33 3.1 Getting Started 33 3.2 Random Number Sequences 34 3.3 Monte Carlo Integration 36 3.4 Monte Carlo Simulation for Inference 40 3.5 The Conjugate Normal Model 44 3.6 In Practice: The Conjugate Normal Model 50 3.7 Count Data and the Conjugate Poisson Model 57 3.8 Summary 61 3.9 Exercises 62 3.10 Notation Introduced in This Chapter 63 3.11 Appendix - In Detail: Finding Posterior Distributions for the Normal Model 63 4 MCMC and Regression Models 71 4.1 Introduction to Markov Chain Monte Carlo 71 4.2 Fundamentals of MCMC 73 4.3 Gibbs Sampling 75 4.4 Gibbs Sampling and the Simple Linear Regression Model 82 4.5 In Practice: The Simple Linear Regression Model 85 4.6 The Metropolis Algorithm 88 4.7 Hastings' Extension of the Metropolis Algorithm 97 4.8 Summary 102 4.9 Exercises 103 5 Estimating Bayesian Models with WinBUGS 105 5.1 An Introduction to WinBUGS 106 5.2 In Practice: A First WinBUGS Model 107 5.3 In Practice: Models for the Mean in WinBUGS 117 5.4 Examining the Prior with Sensitivity Analysis 125 5.5 In Practice: Examining Proportions in WinBUGS 136 5.6 Analysis of Variance Models 142 5.7 Higher-order ANOVA Models 155 5.8 Regression and ANCOVA Models in WinBUGS 163 5.9 Summary 171 5.10 Chapter Appendix: Exporting WinBUGS MCMC Output to R 171 5.11 Exercises 173 6 Assessing MCMC Performance in WinBUGS 175 6.1 Convergence Issues in MCMC Modeling 175 6.2 Output Diagnostics in WinBUGS 178 6.3 Reparameterizing to Improve Convergence 181 6.4 Number and Length of Chains 186 6.5 Metropolis-Hastings Acceptance Rates 197 6.6 Summary 199 6.7 Exercises 200 7 Model Checking and Model Comparison 203 7.1 Graphical Model Checking 203 7.2 Predictive Densities and Checking Model Assumptions 209 7.3 Variable Selection Methods 216 7.4 Bayes Factors and BIC 227 7.5 Deviance Information Criterion 234 7.6 Summary 241 7.7 Exercises 241 8 Hierarchical Models 243 8.1 Fundamentals of Hierarchical Models 243 8.2 The Random Coefficients Model 256 8.3 Hierarchical Models for Variance Terms 267 8.4 Functional Forms at Multiple Hierarchical Levels 273 8.5 In Detail: Modeling Covarying Hierarchical Terms 279 8.6 Summary 286 8.7 Exercises 286 8.8 Notation Introduced in This Chapter 288 9 Generalized Linear Models 289 9.1 Fundamentals of Generalized Linear Models 289 9.2 Count Data Models: Poisson Regression 292 9.3 Models for Binary Data: Logistic Regression 296 9.4 The Probit Model 303 9.5 In Detail: Multinomial Logistic Regression for Categorical Outcomes 306 9.6 Hierarchical Models for Count Data 314 9.7 Hierarchical Models for Binary Data 320 9.8 Summary 324 9.9 Exercises 325 9.10 Notation Introduced in This Chapter 327 10 Models for Difficult Data 329 10.1 Living with Outliers-Robust Regression Models 329 10.2 Handling Heteroscedasticity by Modeling Variance Parameters 340 10.3 Dealing with Missing Data 345 10.4 Types of Missing Data 349 10.5 Missing Covariate Data and Non-Normal Missing Data 357 10.6 Summary 358 10.7 Exercises 359 10.8 Notation Introduced in This Chapter 360 11 Introduction to Latent Variable Models 361 11.1 Not Seen but Felt 361 11.2 Latent Variable Models for Binary Data 362 11.3 Structural Break Models 366 11.4 In Detail: The Ordinal Probit Model 376 11.5 Summary 383 11.6 Exercises 383 A Common Statistical Distributions 385 Bibliography 389 Author Index 403 Subject Index 407 .
Record Nr. UNINA-9910786894103321
Hahn Eugene D.  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui