Business analytics: applied modelling & prediction
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
London
Sage
[2024]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxi, 674 Seiten Illustrationen, Diagramme |
| ISBN: | 9781529774092 9781529774108 |
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CONTENTS Acknowledgements About the author Preface Online resources xiii xv xvii xxiii 1 Decision-making under uncertainty 1.1 Introduction 1.2 Uncertainty: friend or foe? 1.3 Modern decision-making 1.4 Advances in technology 1.5 Data: the new oil? 1.6 Qualitative versus quantitative analysis 1.7 Uncertainty in the news 1.8 Simplicity versus complexity - the need for models 1.9 Model assumptions 1.10 To switch, or not to switch? 1.11 Chapter overview 1.12 Key vocabulary 1.13 Suggested reading 1.14 Exercises 1 3 4 6 8 8 9 13 15 18 22 29 29 30 30 2 Descriptive statistics 2.1 Introduction 2.2 Qualitative and quantitative variables 2.3 Continuous and discrete variables 2.4 The sample distribution 2.5 Measures of central tendency 2.6 Measures of dispersion 2.7 Summary of common Excel functions 2.8 Chapter overview 2.9 Key vocabulary 2.10 Datasets 2.11 Exercises 2.12 Proofs 31 33 34 37 40 43 53 60 60 61 61 61 63 3 Data visualisation 3.1 Introduction 3.2 Getting started 3.3 On your marks, get set, viz! 3.4 Show Me (what works!) 3.5 Adding and editing text and dynamic titles 3.6 Maps 67 69 71 77 79 80 81
vl BUSINESS ANALYTICS 3.7 3.8 3.9 3.10 3.11 Calculated fields and parameters Dig deeper Chapter overview Key vocabulary Exercises 83 87 89 89 89 Probability 4.1 Introduction 4.2 Quantifying probabilities 4.3 The concept of probability . 4.4 Combinatorial theory: counting 4.5 Relative frequency 4.6 Randomness 4.7 Properties of probability 4.8 Conditional probability and Bayes' theorem 4.9 Chapter overview 4.10 Key vocabulary 4.11 Exercises 91 93 95 97 99 103 104 104 111 120 120 120 Probability distributions 5.1 Introduction 5.2 Random variables 5.3 Probability distribution 5.4 Binomial distribution 5.5 Cumulative distribution functions 5.6 Poisson distribution 5.7 Poisson approximation to the binomial 5.8 Expected value of a discrete random variable 5.9 Variance of a discrete random variable 5.10 Distributions related to the binomial distribution 5.11 What is a continuous random variable? 5.12 Probability density function and cumulative distribution function 5.13 (Continuous) uniform distribution 5.14 Exponential distribution 5.15 Normal distribution 5.16 Normal approximation to the binomial 5.17 Summary of probability distributions 5.18 Chapter overview 5.19 Key vocabulary 5.20 Exercises 5.21 Proofs 123 125 126 127 130 132 134 136 139 141 143 145 147 150 151 154 162 163 164 164 164 168 Decision tree analysis and game theory Introduction 6.1 To advertise, or not to advertise? 6.2 6.3 To drill, or not to drill? 6.4 Risk attitudes 171 173 174 176 178
CONTENTS 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 Putting a price on information Game theory Cournot competition Bertrand competition Stackelberg leadership Chapter overview Key vocabulary Exercises vH 181 190 197 201 203 205 206 206 Sampling and sampling distributions Introduction 7.1 Sampling 7.2 Classification of sampling techniques 7.3 Non-random sampling techniques 7.4 Random sampling techniques 7.5 Sampling distributions 7.6 Sampling distribution of the sample mean 7.7 Sampling distribution of the sample proportion 7.8 Sampling distribution of the sample variance 7.9 7.10 Chapter overview 7.11 Key vocabulary 7.12 Exercises 211 213 214 217 218 221 227 230 240 241 243 243 244 Opinion research 8.1 Introduction 8.2 Measurement and scaling 8.3 Levels of measurement 8.4 Scaling techniques 8.5 Chapter overview 8.6 Key vocabulary 8.7 Exercises 247 249 250 252 254 260 260 260 Estimation 9.1 Introduction 9.2 Estimation criteria: bias, variance and mean squared error 9.3 Unbiased estimators 9.4 Interval estimation 9.5 General formulae for normally distributed statistics 9.6 Confidence interval for a single proportion 9.7 Sample size determination 9.8 Difference between two population proportions 9.9 Difference between two population means 9.10 Summary of common Excel functions 9.11 Chapter overview 9.12 Key vocabulary 9.13 Exercises 9.14 Proof 263 265 267 271 273 275 281 282 284 286 291 291 291 291 295
viil BUSINESS ANALYTICS 10 Hypothesis testing 10.1 Introduction 10.2 Statistical juries 10.3 Type I and Type II errors 10.4 p-values, effect size and sample size influences 10.5 Testing a population mean claim 10.6 Hypothesis test for a single mean (σ2 known) 10.7 Hypothesis test for a single mean (σ2 unknown) 10.8 Hypothesis test for a single proportion 10.9 Difference between two population proportions 10.10 Difference between two population means 10.11 Summary of common Excel functions 10.12 Chapter overview 10.13 Key vocabulary 10.14 Exercises ., 297 299 300 301 305 312 315 316 318 320 323 328 328 329 329 11 Bivariate analysis 11.1 Introduction 11.2 Cross-tabulation analysis 11.3 Side-by-side box plots 11.4 Correlation (is not causation!) 11.5 Chapter overview 11.6 Key vocabulary 11.7 Exercises 335 338 338 347 348 363 363 363 12 Analysis of variance 12.1 Introduction 12.2 Testing for equality of three population means 12.3 One-way analysis of variance 12.4 From one-way to two-way ANOVA 12.5 Two-way analysis of variance 12.6 Chapter overview 12.7 Key vocabulary 12.8 Exercises 367 370 370 374 387 387 397 397 397 13 Linear regression 13.1 Introduction 13.2 Parameter estimation 13.3 Analysis of variance - regression style 13.4 A general test for the effect of a variable 13.5 Hypothesis testing and confidence intervals for regression model parameters 13.6 Prediction 13.7 Elasticities 13.8 Tests of assumptions and robustness 401 403 408 412 415 417 421 426 433
CONTENTS 14 Ix 13.9 Chapter overview 13.10 Key vocabulary 13.11 Exercises 434 434 435 Multiple regression 14.1 Introduction 14.2 Visualising relationships between three variables 14.3 The multiple linear regression model 14.4 To include, or not to include? The perilsof variable misspecification 14.5 Interactions 14.6 Dummy variables 14.7 Multicollinearity 14.8 Model selection techniques 14.9 Case study: Gender discrimination in pay 14.10 Chapter overview 14.11 Key vocabulary 14.12 Exercises 14.13 Optional material on multiple linear regression model statistical inference 439 442 442 449 453 456 457 461 463 465 468 468 469 470 15 Time-series analysis and forecasting 15.1 Introduction 15.2 Classifying forecasts 15.3 Decomposing a time series 15.4 Assessing forecast accuracy 15.5 Trend-based time-series models 15.6 Forecasting in Tableau 15.7 Chapter overview 15.8 Key vocabulary 15.9 Exercises 473 476 478 482 492 494 498 500 501 501 16 Discriminant analysis 16.1 Introduction 16.2 Discriminant analysis model 16.3 Two-group discriminant analysis: empirical example 16.4 Three-group discriminant analysis: empirical example 16.5 Chapter overview 16.6 Key vocabulary 16.7 Exercises 505 508 510 514 525 533 534 534 17 Factor analysis 17.1 Introduction 17.2 Factor analysis model 17.3 Factor analysis: full empirical workthrough 17.4 Factor analysis: further example 537 539 544 546 559
BUSINESS ANALYTICS X 17.5 17.6 17.7 Chapter overview Key vocabulary Exercises 566 569 569 18 Cluster analysis 18.1 Introduction 18.2 Cluster analysis 18.3 Clustering procedures 18.4 Cluster analysis: full empirical example 18.5 Cluster analysis: further example 18.6 Chapter overview 18.7 Key vocabulary 18.8 Exercises 571 573 574 575 581 589 592 593 594 19 Constrained optimisation models 19.1 Introduction 19.2 Optimisation models 19.3 Product mix 19.4 Sensitivity analysis 19.5 Conjoint analysis 19.6 Chapter overview 19.7 Key vocabulary 19.8 Exercises 595 598 599 600 605 607 621 621 621 20 Monte Carlo simulation 20.1 Introduction 20.2 Basics of simulation 20.3 Probability distributions for input variables 20.4 Pseudo-random number generators 20.5 Simulating from common probability distributions in Excel 20.6 Simulation case study 20.7 Chapter overview 20.8 Key vocabulary 20.9 Exercises 623 626 629 632 633 638 640 643 644 644 Appendix A: Common Excel functions Using Excel functions A.l A.2 Descriptive statistics Discrete distributions A.3 A.4 Continuous distributions Statistical inference A.5 647 647 648 649 650 651 Appendix В: Compendium of probability distributions B.l Discrete uniform distribution B.2 Bernoulli distribution 653 654 655
CONTENTS в.з В.4 В.5 В.6 В.7 В.8 Index Binomial distribution Poisson distribution Geometrie distribution Continuous uniform distribution Exponential distribution Normal distribution xi 656 658 659 660 661 662 665 |
| adam_txt |
CONTENTS Acknowledgements About the author Preface Online resources xiii xv xvii xxiii 1 Decision-making under uncertainty 1.1 Introduction 1.2 Uncertainty: friend or foe? 1.3 Modern decision-making 1.4 Advances in technology 1.5 Data: the new oil? 1.6 Qualitative versus quantitative analysis 1.7 Uncertainty in the news 1.8 Simplicity versus complexity - the need for models 1.9 Model assumptions 1.10 To switch, or not to switch? 1.11 Chapter overview 1.12 Key vocabulary 1.13 Suggested reading 1.14 Exercises 1 3 4 6 8 8 9 13 15 18 22 29 29 30 30 2 Descriptive statistics 2.1 Introduction 2.2 Qualitative and quantitative variables 2.3 Continuous and discrete variables 2.4 The sample distribution 2.5 Measures of central tendency 2.6 Measures of dispersion 2.7 Summary of common Excel functions 2.8 Chapter overview 2.9 Key vocabulary 2.10 Datasets 2.11 Exercises 2.12 Proofs 31 33 34 37 40 43 53 60 60 61 61 61 63 3 Data visualisation 3.1 Introduction 3.2 Getting started 3.3 On your marks, get set, viz! 3.4 Show Me (what works!) 3.5 Adding and editing text and dynamic titles 3.6 Maps 67 69 71 77 79 80 81
vl BUSINESS ANALYTICS 3.7 3.8 3.9 3.10 3.11 Calculated fields and parameters Dig deeper Chapter overview Key vocabulary Exercises 83 87 89 89 89 Probability 4.1 Introduction 4.2 Quantifying probabilities 4.3 The concept of probability . 4.4 Combinatorial theory: counting 4.5 Relative frequency 4.6 Randomness 4.7 Properties of probability 4.8 Conditional probability and Bayes' theorem 4.9 Chapter overview 4.10 Key vocabulary 4.11 Exercises 91 93 95 97 99 103 104 104 111 120 120 120 Probability distributions 5.1 Introduction 5.2 Random variables 5.3 Probability distribution 5.4 Binomial distribution 5.5 Cumulative distribution functions 5.6 Poisson distribution 5.7 Poisson approximation to the binomial 5.8 Expected value of a discrete random variable 5.9 Variance of a discrete random variable 5.10 Distributions related to the binomial distribution 5.11 What is a continuous random variable? 5.12 Probability density function and cumulative distribution function 5.13 (Continuous) uniform distribution 5.14 Exponential distribution 5.15 Normal distribution 5.16 Normal approximation to the binomial 5.17 Summary of probability distributions 5.18 Chapter overview 5.19 Key vocabulary 5.20 Exercises 5.21 Proofs 123 125 126 127 130 132 134 136 139 141 143 145 147 150 151 154 162 163 164 164 164 168 Decision tree analysis and game theory Introduction 6.1 To advertise, or not to advertise? 6.2 6.3 To drill, or not to drill? 6.4 Risk attitudes 171 173 174 176 178
CONTENTS 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 Putting a price on information Game theory Cournot competition Bertrand competition Stackelberg leadership Chapter overview Key vocabulary Exercises vH 181 190 197 201 203 205 206 206 Sampling and sampling distributions Introduction 7.1 Sampling 7.2 Classification of sampling techniques 7.3 Non-random sampling techniques 7.4 Random sampling techniques 7.5 Sampling distributions 7.6 Sampling distribution of the sample mean 7.7 Sampling distribution of the sample proportion 7.8 Sampling distribution of the sample variance 7.9 7.10 Chapter overview 7.11 Key vocabulary 7.12 Exercises 211 213 214 217 218 221 227 230 240 241 243 243 244 Opinion research 8.1 Introduction 8.2 Measurement and scaling 8.3 Levels of measurement 8.4 Scaling techniques 8.5 Chapter overview 8.6 Key vocabulary 8.7 Exercises 247 249 250 252 254 260 260 260 Estimation 9.1 Introduction 9.2 Estimation criteria: bias, variance and mean squared error 9.3 Unbiased estimators 9.4 Interval estimation 9.5 General formulae for normally distributed statistics 9.6 Confidence interval for a single proportion 9.7 Sample size determination 9.8 Difference between two population proportions 9.9 Difference between two population means 9.10 Summary of common Excel functions 9.11 Chapter overview 9.12 Key vocabulary 9.13 Exercises 9.14 Proof 263 265 267 271 273 275 281 282 284 286 291 291 291 291 295
viil BUSINESS ANALYTICS 10 Hypothesis testing 10.1 Introduction 10.2 Statistical juries 10.3 Type I and Type II errors 10.4 p-values, effect size and sample size influences 10.5 Testing a population mean claim 10.6 Hypothesis test for a single mean (σ2 known) 10.7 Hypothesis test for a single mean (σ2 unknown) 10.8 Hypothesis test for a single proportion 10.9 Difference between two population proportions 10.10 Difference between two population means 10.11 Summary of common Excel functions 10.12 Chapter overview 10.13 Key vocabulary 10.14 Exercises ., 297 299 300 301 305 312 315 316 318 320 323 328 328 329 329 11 Bivariate analysis 11.1 Introduction 11.2 Cross-tabulation analysis 11.3 Side-by-side box plots 11.4 Correlation (is not causation!) 11.5 Chapter overview 11.6 Key vocabulary 11.7 Exercises 335 338 338 347 348 363 363 363 12 Analysis of variance 12.1 Introduction 12.2 Testing for equality of three population means 12.3 One-way analysis of variance 12.4 From one-way to two-way ANOVA 12.5 Two-way analysis of variance 12.6 Chapter overview 12.7 Key vocabulary 12.8 Exercises 367 370 370 374 387 387 397 397 397 13 Linear regression 13.1 Introduction 13.2 Parameter estimation 13.3 Analysis of variance - regression style 13.4 A general test for the effect of a variable 13.5 Hypothesis testing and confidence intervals for regression model parameters 13.6 Prediction 13.7 Elasticities 13.8 Tests of assumptions and robustness 401 403 408 412 415 417 421 426 433
CONTENTS 14 Ix 13.9 Chapter overview 13.10 Key vocabulary 13.11 Exercises 434 434 435 Multiple regression 14.1 Introduction 14.2 Visualising relationships between three variables 14.3 The multiple linear regression model 14.4 To include, or not to include? The perilsof variable misspecification 14.5 Interactions 14.6 Dummy variables 14.7 Multicollinearity 14.8 Model selection techniques 14.9 Case study: Gender discrimination in pay 14.10 Chapter overview 14.11 Key vocabulary 14.12 Exercises 14.13 Optional material on multiple linear regression model statistical inference 439 442 442 449 453 456 457 461 463 465 468 468 469 470 15 Time-series analysis and forecasting 15.1 Introduction 15.2 Classifying forecasts 15.3 Decomposing a time series 15.4 Assessing forecast accuracy 15.5 Trend-based time-series models 15.6 Forecasting in Tableau 15.7 Chapter overview 15.8 Key vocabulary 15.9 Exercises 473 476 478 482 492 494 498 500 501 501 16 Discriminant analysis 16.1 Introduction 16.2 Discriminant analysis model 16.3 Two-group discriminant analysis: empirical example 16.4 Three-group discriminant analysis: empirical example 16.5 Chapter overview 16.6 Key vocabulary 16.7 Exercises 505 508 510 514 525 533 534 534 17 Factor analysis 17.1 Introduction 17.2 Factor analysis model 17.3 Factor analysis: full empirical workthrough 17.4 Factor analysis: further example 537 539 544 546 559
BUSINESS ANALYTICS X 17.5 17.6 17.7 Chapter overview Key vocabulary Exercises 566 569 569 18 Cluster analysis 18.1 Introduction 18.2 Cluster analysis 18.3 Clustering procedures 18.4 Cluster analysis: full empirical example 18.5 Cluster analysis: further example 18.6 Chapter overview 18.7 Key vocabulary 18.8 Exercises 571 573 574 575 581 589 592 593 594 19 Constrained optimisation models 19.1 Introduction 19.2 Optimisation models 19.3 Product mix 19.4 Sensitivity analysis 19.5 Conjoint analysis 19.6 Chapter overview 19.7 Key vocabulary 19.8 Exercises 595 598 599 600 605 607 621 621 621 20 Monte Carlo simulation 20.1 Introduction 20.2 Basics of simulation 20.3 Probability distributions for input variables 20.4 Pseudo-random number generators 20.5 Simulating from common probability distributions in Excel 20.6 Simulation case study 20.7 Chapter overview 20.8 Key vocabulary 20.9 Exercises 623 626 629 632 633 638 640 643 644 644 Appendix A: Common Excel functions Using Excel functions A.l A.2 Descriptive statistics Discrete distributions A.3 A.4 Continuous distributions Statistical inference A.5 647 647 648 649 650 651 Appendix В: Compendium of probability distributions B.l Discrete uniform distribution B.2 Bernoulli distribution 653 654 655
CONTENTS в.з В.4 В.5 В.6 В.7 В.8 Index Binomial distribution Poisson distribution Geometrie distribution Continuous uniform distribution Exponential distribution Normal distribution xi 656 658 659 660 661 662 665 |
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| spelling | Abdey, James ca. 20./21. Jh. Verfasser (DE-588)1322590974 aut Business analytics applied modelling & prediction James Abdey London Sage [2024] © 2024 xxi, 674 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Betriebsdaten (DE-588)4145038-3 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Betriebsdaten (DE-588)4145038-3 s Datenanalyse (DE-588)4123037-1 s Prognoseverfahren (DE-588)4358095-6 s DE-604 Erscheint auch als Online-Ausgabe 978-1-5297-8774-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034806309&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Abdey, James ca. 20./21. Jh Business analytics applied modelling & prediction Datenanalyse (DE-588)4123037-1 gnd Betriebsdaten (DE-588)4145038-3 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4145038-3 (DE-588)4358095-6 |
| title | Business analytics applied modelling & prediction |
| title_auth | Business analytics applied modelling & prediction |
| title_exact_search | Business analytics applied modelling & prediction |
| title_exact_search_txtP | Business analytics applied modelling and prediction |
| title_full | Business analytics applied modelling & prediction James Abdey |
| title_fullStr | Business analytics applied modelling & prediction James Abdey |
| title_full_unstemmed | Business analytics applied modelling & prediction James Abdey |
| title_short | Business analytics |
| title_sort | business analytics applied modelling prediction |
| title_sub | applied modelling & prediction |
| topic | Datenanalyse (DE-588)4123037-1 gnd Betriebsdaten (DE-588)4145038-3 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| topic_facet | Datenanalyse Betriebsdaten Prognoseverfahren |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034806309&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT abdeyjames businessanalyticsappliedmodellingprediction |