Introduction to business analytics using simulation:
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| Format: | Book |
| Language: | English |
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London
Academic Press
[2023]
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| Edition: | second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xiii, 495 Seiten Illustrationen, Diagramme |
| ISBN: | 9780323917179 |
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Contents Preface. iii Acknowledgments.v CHAPTER 1 Business analytics is making decisions. i Introduction . 1 1.1 Business analytics is making decisions subject to uncertainty. 1 1.2 Components of business analytics.3 1.3 Uncertainty = probability = stochastic. 4 1.4 Example of decision making and the three stages of analytics. 4 1.5 Introduction to decision analysis.6 1.6 What is simulation? . 8 1.6.1 Why use simulation?. 8 1.6.2 Simulation applications. 9 1.7 Monte Carlo simulation
. 9 CHAPTER 2 Decision trees. 19 Introduction . 19 2.1 Introduction to decision making .20 2.1.1 Define the problem .20 2.1.2 Identify and weight the criteria.,20 2.1.3 Generate alternatives.21 2.1.4 Evaluate each alternative . 21 2.1.5 Compute the optimal decision . 21 2.2 Decision trees and expected value . 22 2.3 Overview of the decision-making process. 24 2.4 Sensitivity analysis . 26 2.5 Expected value of perfect information . . 31 2.6 Properties of decision trees. 33 2.6.1
Lineartransforms. 33 2.6.2 Equivalent decision structures . 34 2.7 Probability: the measure of uncertainty. 37 2.8 Where do the probabilities come from?. 38 2.9 2.10 2.11 2.12 Elements of probability. :.39 Probability notation . 40 Applications of decision analysis and decision trees. 44 Summary of the decision analysis process . 47 vii
viii Contents CHAPTER 3 Decision making and simulation. 57 Introduction . 57 3.1 Simulation to model uncertainty. 57 3.2 Monte Carlo simulation and random variables. 58 3.3 Simulation terminology.61 3.4 Overview of the simulation process.62 3.5 Random number generation in Excel . 63 3.6 Examples of simulation and decision making . 63 CHAPTER 4 Probability: measuring uncertainty.85 Introduction . 85 4.1 Probability: measuring likelih'dod. 85 4.2 Probability distributions. 86 4.3 General probability rules .87
4.4 Conditional probability and Bayes’ theorem. 91 CHAPTER 5 Subjective probability distributions. 101 Introduction . 102 5.1 Subjective probability distributions: probability from experience . 102 5.2 Two-point estimation: uniform distribution . .102 5.2.1 Discrete uniform distribution. 103 5.2.2 Continuous uniform distribution . 106 5.3 Three-point estimation: triangular distribution. 110 5.3.1 Simulating asymmetric triangular distribution. 112 5.3.2 Simulating an asymmetric triangular distribution. 114 5.4 Five-point estimates forsubjective probability distributions. 115 5.4.1 Simulating a five-point distribution. .118 5.4.2 Other estimates for subjective probability distributions. 120 CHAPTER 6 Empirical probability distributions. 131
Introduction. 131 6.1 Empirical probability distributions: probability from data. 132 6.2 Discrete empirical probability distributions . 132 6.3 Continuous empirical probability distributions . 141 CHAPTER 7 Theoretical probability distributions. 163 Introduction . 163 7.1 Theoretical/classical probability. 164 7.2 Review of notation for probability distributions . 164 7.3 Discrete theoretical distributions . 165
Contents 7.4 7.5 7.6 7.7 CHAPTER 8 ix 7.3.1 Uniform distribution. 165 7.3.2 Bernoulli distribution . . 166 7.3.3 Binomial distribution . 166 7.3.4 Poisson distribution. 173 Continuous probability distributions .180 7.4.1 Continuous uniform distribution . 180 7.4.2 Normal distribution. .180 Normal approximation of the binomial and Poisson distributions . 186 Using distributions in decision analysis. 190 Overview of probability distributions.198 Simulation accuracy: central limit theorem and sampling. 209 Introduction. 209 8.1 Introduction to sampling and the margin of error. 210 8.2 Linear properties of probability distributions
. 210 8.3 Adding distributions . 211 8.4 Samples. 224 8.5 Central limit theorem. 224 8.6 Confidence intervals and hypothesis testing for proportions. 232 8.6.1 Confidence intervals. 232 8.6.2 Hypothesis testing. 239 8.7 Confidence intervals and hypothesis testing for means. 247 8.7.1 Samples with n 30: use standard normal distribution . 247 8.7.2 Small (n 30) samples: use Student’s t-distribution . 251 CHAPTER 9 Simulation fit and significance: Chi-square and ANOVA. 273 Introduction . .275 9.1 Conditional probabilities—again. 275 9.1.1 Examples of conditional probability estimation procedures.275 9.2 Conditional probability for groups
.276 9.2.1 Examples of ANOVA and Chi-square situations . . 277 9.3 Chi-square (χ2): are the probability distributions the same?. 279 9.3.1 Chi-square: actual frequencies versus expected frequencies . 279 9.4 ANOVA: are the groups’ averages the same?. 281 9.4.1 Conducting an ANOVA: p-Value again . 293 9.4.2 Why is it called analysis of VARIANCE if it compares averages?. 297 9.4.3 An approximate comparison of more than two groups. 299 9.4.4 What if groups are, or are not, significantly different? . 301 9.5 ANOVA versus Chi-square: Likert scale. 309
x Contents CHAPTER 10 Regression. 327 Introduction . 329 10.1 Overview of regression . 329 10.1.1 Basic linear model.330 10.2 Measures of fit and significance. 334 10.2.1 Standard error of the slope: SE^ . 334 10.2.2 i-Stat. 334 10.2.3 Standard error of the estimate .335 10.2.4 Coefficient of determination: r2.336 10.3 Multiple regression . 343 10.4 Nonlinear regression: polynomials . 345 10.4.1 Nonlinear models: polynomials .345 10.4.2 Nonlinear models: nonlinear (logarithmic) transformations. 347 10.5 Indicator
variables. 349 10.6 Interaction terms . 363 10.7 Regression pitfalls. 367 10.7.1 Nonlinearity. 367 10.7.2 Extrapolation beyond the relevant rangę. 370 10.7.3 Correlation causality . 370 10.7.4 Reverse causality . 370 10.7.5 Omitted-variable bias.371 10.7.6 Serial correlation . 371 10.7.7 Multicollinearity . 371 10.7.8 Datamining . 372 10.7.9 Heteroscedasticity . 372 10.8 Review of regression. -373 CHAPTER 11 Forecasting. 389 Introduction. 389 11.1 Overview of
forecasting. 389 11.2 Measures of accuracy. 391 11.3 Components of time series data. 395 11.4 Forecasting trend.400 11.4.1 Linear trend . 400 11.4.2 Exponential trend . 400 11.4.3 Autoregression. 405 11.5 Forecasting seasonality . 411 11.5.1 Additive seasonality using indicator variables . 411 11.5.2 Ratio-to-moving-average method (X-ll, X-12). 414 11.5.3 Summary of the ratio-to-moving-average method . 421 11.6 Aggregating sales. 421 11.7 Review of forecasting withregression. 424
Contents xi Constrained linear optimization. 439 Introduction.439 12.1 Overview of constrained linear optimization. 440 12.1.1 Linearity rules. 441 CHAPTER 12 12.2 12.3 12.4 12.5 12.6 12.7 12.8 Components of linear programming. 442 General layout of a linear programming model in Excel. 443 Steps for the linear programming modeling . .446 Network models .455 Types of linear programming end conditions . 458 Sensitivity analysis terms. 459 Excel Solver messages. 464 Appendix 1 A1.1 A1.2 A1.3 Summary of simulation. 477 Overview of the simulation process. 477 Review of probability
distributions. 478 Methods to simulate probability distributions. 479 Al.3.1 Random numbers by single formula method. 479 Al.3.2Random numbers by method. 480 Appendix 2 Statistical tables. 483 A2.1 Normal distribution. .483 A2.2 Student’s ř-distribution. 485 Index 487 |
| adam_txt |
Contents Preface. iii Acknowledgments.v CHAPTER 1 Business analytics is making decisions. i Introduction . 1 1.1 Business analytics is making decisions subject to uncertainty. 1 1.2 Components of business analytics.3 1.3 Uncertainty = probability = stochastic. 4 1.4 Example of decision making and the three stages of analytics. 4 1.5 Introduction to decision analysis.6 1.6 What is simulation? . 8 1.6.1 Why use simulation?. 8 1.6.2 Simulation applications. 9 1.7 Monte Carlo simulation
. 9 CHAPTER 2 Decision trees. 19 Introduction . 19 2.1 Introduction to decision making .20 2.1.1 Define the problem .20 2.1.2 Identify and weight the criteria.,20 2.1.3 Generate alternatives.21 2.1.4 Evaluate each alternative . 21 2.1.5 Compute the optimal decision . 21 2.2 Decision trees and expected value . 22 2.3 Overview of the decision-making process. 24 2.4 Sensitivity analysis . 26 2.5 Expected value of perfect information . . 31 2.6 Properties of decision trees. 33 2.6.1
Lineartransforms. 33 2.6.2 Equivalent decision structures . 34 2.7 Probability: the measure of uncertainty. 37 2.8 Where do the probabilities come from?. 38 2.9 2.10 2.11 2.12 Elements of probability. :.39 Probability notation . 40 Applications of decision analysis and decision trees. 44 Summary of the decision analysis process . 47 vii
viii Contents CHAPTER 3 Decision making and simulation. 57 Introduction . 57 3.1 Simulation to model uncertainty. 57 3.2 Monte Carlo simulation and random variables. 58 3.3 Simulation terminology.61 3.4 Overview of the simulation process.62 3.5 Random number generation in Excel . 63 3.6 Examples of simulation and decision making . 63 CHAPTER 4 Probability: measuring uncertainty.85 Introduction . 85 4.1 Probability: measuring likelih'dod. 85 4.2 Probability distributions. 86 4.3 General probability rules .87
4.4 Conditional probability and Bayes’ theorem. 91 CHAPTER 5 Subjective probability distributions. 101 Introduction . 102 5.1 Subjective probability distributions: probability from experience . 102 5.2 Two-point estimation: uniform distribution . .102 5.2.1 Discrete uniform distribution. 103 5.2.2 Continuous uniform distribution . 106 5.3 Three-point estimation: triangular distribution. 110 5.3.1 Simulating asymmetric triangular distribution. 112 5.3.2 Simulating an asymmetric triangular distribution. 114 5.4 Five-point estimates forsubjective probability distributions. 115 5.4.1 Simulating a five-point distribution. .118 5.4.2 Other estimates for subjective probability distributions. 120 CHAPTER 6 Empirical probability distributions. 131
Introduction. 131 6.1 Empirical probability distributions: probability from data. 132 6.2 Discrete empirical probability distributions . 132 6.3 Continuous empirical probability distributions . 141 CHAPTER 7 Theoretical probability distributions. 163 Introduction . 163 7.1 Theoretical/classical probability. 164 7.2 Review of notation for probability distributions . 164 7.3 Discrete theoretical distributions . 165
Contents 7.4 7.5 7.6 7.7 CHAPTER 8 ix 7.3.1 Uniform distribution. 165 7.3.2 Bernoulli distribution . . 166 7.3.3 Binomial distribution . 166 7.3.4 Poisson distribution. 173 Continuous probability distributions .180 7.4.1 Continuous uniform distribution . 180 7.4.2 Normal distribution. .180 Normal approximation of the binomial and Poisson distributions . 186 Using distributions in decision analysis. 190 Overview of probability distributions.198 Simulation accuracy: central limit theorem and sampling. 209 Introduction. 209 8.1 Introduction to sampling and the margin of error. 210 8.2 Linear properties of probability distributions
. 210 8.3 Adding distributions . 211 8.4 Samples. 224 8.5 Central limit theorem. 224 8.6 Confidence intervals and hypothesis testing for proportions. 232 8.6.1 Confidence intervals. 232 8.6.2 Hypothesis testing. 239 8.7 Confidence intervals and hypothesis testing for means. 247 8.7.1 Samples with n 30: use standard normal distribution . 247 8.7.2 Small (n 30) samples: use Student’s t-distribution . 251 CHAPTER 9 Simulation fit and significance: Chi-square and ANOVA. 273 Introduction . .275 9.1 Conditional probabilities—again. 275 9.1.1 Examples of conditional probability estimation procedures.275 9.2 Conditional probability for groups
.276 9.2.1 Examples of ANOVA and Chi-square situations . . 277 9.3 Chi-square (χ2): are the probability distributions the same?. 279 9.3.1 Chi-square: actual frequencies versus expected frequencies . 279 9.4 ANOVA: are the groups’ averages the same?. 281 9.4.1 Conducting an ANOVA: p-Value again . 293 9.4.2 Why is it called analysis of VARIANCE if it compares averages?. 297 9.4.3 An approximate comparison of more than two groups. 299 9.4.4 What if groups are, or are not, significantly different? . 301 9.5 ANOVA versus Chi-square: Likert scale. 309
x Contents CHAPTER 10 Regression. 327 Introduction . 329 10.1 Overview of regression . 329 10.1.1 Basic linear model.330 10.2 Measures of fit and significance. 334 10.2.1 Standard error of the slope: SE^ . 334 10.2.2 i-Stat. 334 10.2.3 Standard error of the estimate .335 10.2.4 Coefficient of determination: r2.336 10.3 Multiple regression . 343 10.4 Nonlinear regression: polynomials . 345 10.4.1 Nonlinear models: polynomials .345 10.4.2 Nonlinear models: nonlinear (logarithmic) transformations. 347 10.5 Indicator
variables. 349 10.6 Interaction terms . 363 10.7 Regression pitfalls. 367 10.7.1 Nonlinearity. 367 10.7.2 Extrapolation beyond the relevant rangę. 370 10.7.3 Correlation causality . 370 10.7.4 Reverse causality . 370 10.7.5 Omitted-variable bias.371 10.7.6 Serial correlation . 371 10.7.7 Multicollinearity . 371 10.7.8 Datamining . 372 10.7.9 Heteroscedasticity . 372 10.8 Review of regression. -373 CHAPTER 11 Forecasting. 389 Introduction. 389 11.1 Overview of
forecasting. 389 11.2 Measures of accuracy. 391 11.3 Components of time series data. 395 11.4 Forecasting trend.400 11.4.1 Linear trend . 400 11.4.2 Exponential trend . 400 11.4.3 Autoregression. 405 11.5 Forecasting seasonality . 411 11.5.1 Additive seasonality using indicator variables . 411 11.5.2 Ratio-to-moving-average method (X-ll, X-12). 414 11.5.3 Summary of the ratio-to-moving-average method . 421 11.6 Aggregating sales. 421 11.7 Review of forecasting withregression. 424
Contents xi Constrained linear optimization. 439 Introduction.439 12.1 Overview of constrained linear optimization. 440 12.1.1 Linearity rules. 441 CHAPTER 12 12.2 12.3 12.4 12.5 12.6 12.7 12.8 Components of linear programming. 442 General layout of a linear programming model in Excel. 443 Steps for the linear programming modeling . .446 Network models .455 Types of linear programming end conditions . 458 Sensitivity analysis terms. 459 Excel Solver messages. 464 Appendix 1 A1.1 A1.2 A1.3 Summary of simulation. 477 Overview of the simulation process. 477 Review of probability
distributions. 478 Methods to simulate probability distributions. 479 Al.3.1 Random numbers by single formula method. 479 Al.3.2Random numbers by method. 480 Appendix 2 Statistical tables. 483 A2.1 Normal distribution. .483 A2.2 Student’s ř-distribution. 485 Index 487 |
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| spelling | Pinder, Jonathan P. Verfasser (DE-588)171534026 aut Introduction to business analytics using simulation Jonathan P. Pinder (School of Business Wake Forest University Winston-Salem, NC, United States) second edition London Academic Press [2023] © 2023 xiii, 495 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Simulation (DE-588)4055072-2 gnd rswk-swf Unsicherheit (DE-588)4186957-6 gnd rswk-swf Management (DE-588)4037278-9 s Entscheidungsfindung (DE-588)4113446-1 s Unsicherheit (DE-588)4186957-6 s Datenanalyse (DE-588)4123037-1 s Simulation (DE-588)4055072-2 s DE-604 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=033270102&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pinder, Jonathan P. Introduction to business analytics using simulation Entscheidungsfindung (DE-588)4113446-1 gnd Management (DE-588)4037278-9 gnd Datenanalyse (DE-588)4123037-1 gnd Simulation (DE-588)4055072-2 gnd Unsicherheit (DE-588)4186957-6 gnd |
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| title | Introduction to business analytics using simulation |
| title_auth | Introduction to business analytics using simulation |
| title_exact_search | Introduction to business analytics using simulation |
| title_exact_search_txtP | Introduction to business analytics using simulation |
| title_full | Introduction to business analytics using simulation Jonathan P. Pinder (School of Business Wake Forest University Winston-Salem, NC, United States) |
| title_fullStr | Introduction to business analytics using simulation Jonathan P. Pinder (School of Business Wake Forest University Winston-Salem, NC, United States) |
| title_full_unstemmed | Introduction to business analytics using simulation Jonathan P. Pinder (School of Business Wake Forest University Winston-Salem, NC, United States) |
| title_short | Introduction to business analytics using simulation |
| title_sort | introduction to business analytics using simulation |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Management (DE-588)4037278-9 gnd Datenanalyse (DE-588)4123037-1 gnd Simulation (DE-588)4055072-2 gnd Unsicherheit (DE-588)4186957-6 gnd |
| topic_facet | Entscheidungsfindung Management Datenanalyse Simulation Unsicherheit |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033270102&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT pinderjonathanp introductiontobusinessanalyticsusingsimulation |