Business analytics: methods, models, and decisions
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [und 23 weitere]
Pearson
[2017]
|
| Ausgabe: | Global edition, second edition |
| Schriftenreihe: | Always learning
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 652 Seiten Illustrationen, Diagramme |
| ISBN: | 9781292095448 129209544X |
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Datensatz im Suchindex
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| adam_text | Brief Contents Preface 17 About the Author 23 Credits 25 Part 1 Foundations of Business Analytics Chapter 1 Introduction to Business Analytics Chapter 2 Analytics on Spreadsheets 27 63 Part 2 Descriptive Analytics Chapter 3 Visualizing and Exploring Data 79 Chapter 4 Descriptive Statistical Measures 121 Chapter 5 Probability Distributions and Data Modeling Chapter 6 Sampling and Estimation 207 Chanter 7 Statistical Inference 231 157 Part 3 Predictive Analytics Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Trendlines and Regression Analysis Forecasting Techniques 259 299 Introduction to Data Mining 327 Spreadsheet Modeling and Analysis 367 Monte Carlo Simulation and Risk Analysis Part 4 Prescriptive Analytics Chapter 13 Linear Optimization 403 441 Chapter 14 Applications of Linear Optimization 483 Chapter 15 Integer Optimization 539 Chapter 1G Decision Analysis 579 Supplementary Chapter A (online) Nonlinear and Non-Smooth Optimization Supplementary Chapter В (online) Optimization Models with Uncertainty Appendix A 611 Glossary 635 Index 643
Contents Preface 17 About the Author Credits 25 23 Part 1: Foundations of Business Analytics Chapter 1 : introduction to Business Analytics 27 Learning Objectives 27 What Is Business Analytics? 30 Evolution of Business Analytics 31 Impacts and Challenges 34 Scope of Business Analytics 35 Software Support 38 Data for Business Analytics 39 Data Sets and Databases 40 · Big Data 41 ® Metrics and Data Classification 42 * Data Reliability and Validity 44 Models in Business Analytics 44 Decision Models 47 ® Model Assumptions 50 ® Uncertainty and Risk 52 · Prescriptive Decision Models 52 Problem Solving with Analytics 53 Recognizing a Problem 54 ® Defining the Problem 54 * Structuring the Problem 54 * Analyzing the Problem 55 · Interpreting Results and Making a Decision 55 · Implementing the Solution 55 Key Terms 56 · Fun with Analytics 57 · Problems and Exercises 57 · Case: Drout Advertising Research Project 59 · Case: Performance Lawn Equipment 60 Chapter 2: Analytics on Spreadsheets 63 Learning Objectives 63 Basic Excel Skills 65 Excel Formulas 66 * Copying Formulas 66 · Other Useful Excel Tips Excel Functions 68 Basic Excel Functions 68 ® Functions for Specific Applications 69 · Insert Function 70 * Logical Functions 71 Using Excel Lookup Functions for Database Queries 73 Spreadsheet Add-Ins for Business Analytics 76 Key Terms Equipment 76 * Problems and Exercises 78 76 · Case: Performance Lawn 67
8 Contento Part 2: Descriptive Analytics Chapter 3: Visualizing and Exploring Data 79 Learning Objectives 79 Data Visualization 80 Dashboards 81 * Tools and Software for Data Visualization 81 Creating Charts in Microsoft Excel 82 Column and Bar Charts 83 * Data Labels and Data Tables Chart Options 85 * Line Charts 85 · Pie Charts 85 · Area Charts 86 9 Scatter Chart 86 * Bubble Charts 88 * Miscellaneous Excel Charts 89 * Geographic Data 89 Other Excel Data Visualization Tools 90 Data Bars, Color Scales, and Icon Sets 90 * Sparklines 91 * Excel Camera Tool 92 Data Queries: Tables, Sorting, and Filtering 93 Sorting Data in Excel 94 * Pareto Analysis 94 * Filtering Data 96 Statistical Methods for Summarizing Data 98 Frequency Distributions for Categorical Data 99 ® Relative Frequency Distributions 100 * Frequency Distributions for Numerical Data 101 ® Excel Histogram Tool 101 ® Cumulative Relative Frequency Distributions 105 * Percentiles and Quartiles 106 9 Cross-Tabulations 108 Exploring Data Using PivotTables 110 PivotCharts 112 * Slicers and PivotTable Dashboards 113 Key Terms 116 * Problems and Exercises 117 * Case: Drout Advertising Research Project 119 * Case: Performance Lawn Equipment 120 Chapter 4: Descriptive Statistica! Measures 121 Learning Objectives 121 Populations and Samples 122 Understanding Statistical Notation 122 Measures of Location 123 Arithmetic Mean 123 * Median 124 * Mode 125 * Midrange 125 * Using Measures of Location in Business Decisions 126 Measures of Dispersion 127 Range 127 * Interquartile Range 127 * Variance 128 · Standard Deviation 129 *
Chebyshev’s Theorem and the Empirical Rules 130 9 Standardized Values 133 * Coefficient of Variation 134 Measures of Shape 135 Excel Descriptive Statistics Tool 136 Descriptive Statistics for Grouped Data 138 Descriptive Statistics for Categorical Data: The Proportion 140 Statistics in PivotTables 140
9 Contants Measures of Association 141 Covariance 142 * Correlation 143 · Excel Correlation Tool Outliers 146 Statistical Thinking in Business Decisions 148 Variability in Samples 149 145 Key Terms 151 · Problems and Exercises 152 · Case: Drout Advertising Research Project 155 * Case: Performance Lawn Equipment 155 Chapter 5: Probability Distributions and Data Modeling 157 Learning Objectives 157 Basic Concepts of Probability 158 Probability Rules and Formulas 160 » Joint and Marginal Probability 161 ® Conditional Probability 163 Random Variables and Probability Distributions 166 Discrete Probability Distributions 168 Expected Value of a Discrete Random Variable 169 ® Using Expected Value in Making Decisions 170 · Variance of a Discrete Random Variable 172 ® Bernoulli Distribution 173 * Binomial Distribution 173 * Poisson Distribution 175 Continuous Probability Distributions 176 Properties of Probability Density Functions 177 * Uniform Distribution 178 · Normal Distribution 180 · The NORM.INV Function 182 ® Standard Normal Distribution 182 ® Using Standard Normal Distribution Tables 184 · Exponential Distribution 184 · Other Useful Distributions 186 ® Continuous Distributions 186 Random Sampling from Probability Distributions 187 Sampling from Discrete Probability Distributions 188 · Sampling from Common Probability Distributions 189 · Probability Distribution Functions in Analytic Solver Platform 192 Data Modeling and Distribution Fitting 194 Goodness of Fit 196 * Distribution Fitting with Analytic Solver Platform 196 Key Terms Equipment 198 · Problems and Exercises 205
199 * Case: Performance Lawn Chapter 6: Sampling and Estimation 207 Learning Objectives 207 Statistical Sampling 208 Sampling Methods 208 Estimating Population Parameters 211 Unbiased Estimators 212 ® Errors in Point Estimation Sampling Error 213 Understanding Sampling Error 213 212
10 Contento Sampling Distributions 215 Sampling Distribution of the Mean 215 * Applying the Sampling Distribution of the Mean 216 Interval Estimates 216 Confidence Intervals 217 Confidence Interval for the Mean with Known Population Standard Deviation 218 * The /-Distribution 219 * Confidence Interval for the Mean with Unknown Population Standard Deviation 220 * Confidence Interval for a Proportion 220 * Additional Types of Confidence Intervals 222 Using Confidence Intervals for Decision Making 222 Prediction Intervals 223 Confidence Intervals and Sample Size 224 Key Terms 226 * Problems and Exercises 226 * Case: Drout Advertising Research Project 228 * Case: Performance Lawn Equipment 229 Chapter 7: Statistica! inference 231 Learning Objectives 231 Hypothesis Testing 232 Hypothesis-Testing Procedure 233 One-Sample Hypothesis Tests 233 Understanding Potential Errors in Hypothesis Testing 234 * Selecting the Test Statistic 235 * Drawing a Conclusion 236 Two-Tailed Test of Hypothesis for the Mean 238 /j-Values 238 ® One-Sample Tests for Proportions 239 · Confidence Intervals and Hypothesis Tests 240 Two-Sample Hypothesis Tests 241 Two-Sample Tests for Differences in Means 241 · Two-Sample Test for Means with Paired Samples 244 * Test for Equality of Variances 245 Analysis of Variance (ANOVA) 247 Assumptions of ANOVA 249 Chi-Square Test for Independence 250 Cautions in Using the Chi-Square Test 252 Key Terms 253 * Problems and Exercises 254 * Case: Drout Advertising Research Project 257 * Case: Performance Lawn Equipment 257 Part 3: Predictive Analytics Chapter 8: Trendlines
and Regression Analysis 259 Learning Objectives 259 Modeling Relationships and Trends in Data 260 Simple Linear Regression 264 Finding the Best-Fitting Regression Line 265 8 Least-Squares Regression Simple Linear Regression with Excel 269 » Regression as Analysis of Variance 271 * Testing Hypotheses for Regression Coefficients 271 * Confidence Intervals for Regression Coefficients 272 267
11 Contents Residual Analysis and Regression Assumptions 272 Checking Assumptions 274 Multiple Linear Regression 275 Building Good Regression Models 280 Correlation and Multicollinearity 282 * Practical Issues in Trendline and Regression Modeling 283 Regression with Categorical Independent Variables 284 Categorical Variables with More Than Two Levels 287 Regression Models with Nonlinear Terms 289 Advanced Techniques for Regression Modeling using XLMiner 291 Key Terms 294 * Problems and Exercises 294 * Case: Performance Lawn Equipment 298 Chapter 9: Forecasting Techniques 299 Learning Objectives 299 Qualitative and Judgmental Forecasting 300 Historical Analogy 300 * The Delphi Method 301 ® Indicators and Indexes 301 Statistical Forecasting Models 302 Forecasting Models for Stationary Time Series 304 Moving Average Models 304 · Error Metrics and Forecast Accuracy 308 · Exponential Smoothing Models 310 Forecasting Models for Time Series with a Linear Trend 312 Double Exponential Smoothing 313 * Regression-Based Forecasting for Time Series with a Linear Trend 314 Forecasting Time Series with Seasonality 316 Regression-Based Seasonal Forecasting Models 316 * Holt-Winters Forecasting for Seasonal Time Series 318 * Holt-Winters Models for Forecasting Time Series with Seasonality and Trend 318 Selecting Appropriate Time-Series-Based Forecasting Models 320 Regression Forecasting with Causal Variables 321 The Practice of Forecasting 322 Key Terms Equipment 324 · Problems and Exercises 326 324 * Case: Performance Lawn Chapter 10: Introduction to Data Mining 327 Learning Objectives 327
The Scope of Data Mining 329 Data Exploration and Reduction 330 Sampling 330 e Data Visualization 332 · Dirty Data 334 * Cluster Analysis 336 Classification 341 An Intuitive Explanation of Classification 342 ® Measuring Classification Performance 342 · Using Training and Validation Data 344 ® Classifying New Data 346
12 Contente Classification Techniques 346 ќ-Nearest Neighbors (fc-NN) 347 · Discriminant Analysis Regression 354 * Association Rule Mining 358 Cause-and-Effect Modeling 361 349 · Logistic Key Terms 364 · Problems and Exercises 364 * Case: Performance Lawn Equipment 366 Chapter 11 : Spreadsheet Modeling and Analysis 367 Learning Objectives 367 Strategies for Predictive Decision Modeling 368 Building Models Using Simple Mathematics 368 * Building Models Using Influence Diagrams 369 Implementing Models on Spreadsheets 370 Spreadsheet Design 370 · Spreadsheet Quality 372 Spreadsheet Applications in Business Analytics 375 Models Involving Multiple Time Periods 377 * Single-Period Purchase Decisions 379 * Overbooking Decisions 380 Model Assumptions, Complexity, and Realism 382 Data and Models 382 Developing User-Friendly Excel Applications 385 Data Validation 385 * Range Names 385 ՞ Form Controls 386 Analyzing Uncertainty and Model Assumptions 388 What-If Analysis 388 · Data Tables 390 * Scenario Manager 392 * Goal Seek 393 Model Analysis Using Analytic Solver Platform 394 Parametric Sensitivity Analysis 394 * Tornado Charts 396 Key Terms 397 * Problems and Exercises Equipment 402 397 * Case: Performance Lawn Chapter 12: Monte Cario Simuli itiön and Risk Analysis 40 Learning Objectives 403 Spreadsheet Models with Random Variables 405 Monte Carlo Simulation 405 Monte Carlo Simulation Using Analytic Solver Platform 407 Defining Uncertain Model Inputs 407 · Defining Output Cells 410 * Running a Simulation 410 · Viewing and Analyzing Results 412 New-Product Development Model 414
Confidence Interval for the Mean 417 * Sensitivity Chart 418 * Overlay Charts 418 * Trend Charts 420 · Box-Whisker Charts 420 * Simulation Reports 421 Newsvendor Model 421 The Flaw of Averages 421 * Monte Carlo Simulation Using Historical Data 422 * Monte Carlo Simulation Using a Fitted Distribution 423 Overbooking Model 424 The Custom Distribution in Analytic Solver Platform 425
13 Contents Cash Budget Model 426 Correlating Uncertain Variables 429 Key Terms 433 · Problems and Exercises Equipment 440 433 · Case: Performance Lawn Part 4: Prescriptive Analytics Chapter 13: Linear Optimization 441 Learning Objectives 441 Building Linear Optimization Models 442 Identifying Elements for an Optimization Model 442 * Translating Model Information into Mathematical Expressions 443 ® More about Constraints 445 * Characteristics of Linear Optimization Models 446 Implementing Linear Optimization Models on Spreadsheets 446 Excel Functions to Avoid in Linear Optimization 448 Solving Linear Optimization Models 448 Using the Standard Solver 449 * Using Premium Solver 451 ® Solver Answer Report 452 Graphical Interpretation of Linear Optimization 454 How Solver Works 459 How Solver Creates Names in Reports 461 Solver Outcomes and Solution Messages 461 Unique Optimal Solution 462 · Alternative (Multiple) Optimal Solutions 462 · Unbounded Solution 463 * Infeasibility 464 Using Optimization Models for Prediction and Insight 465 Solver Sensitivity Report 467 · Using the Sensitivity Report 470 * Parameter Analysis in Analytic Solver Platform 472 Key Terms 476 * Problems and Exercises 476 * Case: Performance Lawn Equipment 481 Chapter 14: Applications of Linear Optimization 483 Learning Objectives 483 Types of Constraints in Optimization Models 485 Process Selection Models 486 Spreadsheet Design and Solver Reports 487 Solver Output and Data Visualization 489 Blending Models 493 Dealing with Infeasibility 494 Portfolio Investment Models 497 Evaluating Risk versus Reward 499
« Scaling Issues in Using Solver 500 Transportation Models 502 Formatting the Sensitivity Report 504 * Degeneracy 506 Multiperiod Production Planning Models 506 Building Alternative Models 508 Multiperiod Financial Planning Models 511
14 Contents Models with Bounded Variables 515 Auxiliary Variables for Bound Constraints 519 A Production/Marketing Allocation Model 521 Using Sensitivity Information Correctly 523 Key Terms 525 · Problems and Exercises Equipment 537 525 * Case: Performance Lawn Chapter 15: Integer Optimization 539 Learning Objectives 539 Solving Models with General Integer Variables 540 Workforce-Scheduling Models 544 * Alternative Optimal Solutions 545 Integer Optimization Models with Binary Variables 549 Project-Selection Models 550 · Using Binary Variables to Model Logical Constraints 552 * Location Models 553 9 Parameter Analysis 555 * A Customer-Assignment Model for Supply Chain Optimization 556 Mixed-Integer Optimization Models 559 Plant Location and Distribution Models 559 ® Binary Variables, IF Functions, and Nonlinearities in Model Formulation 560 * Fixed-Cost Models 562 Key Terms Equipment 564 * Problems and Exercises 573 564 * Case: Performance Lawn Chapter 16: Decision Analysis 579 Learning Objectives 579 Formulating Decision Problems 581 Decision Strategies without Outcome Probabilities 582 Decision Strategies for a Minimize Objective 582 ® Decision Strategies for a Maximize Objective 583 * Decisions with Conflicting Objectives 584 Decision Strategies with Outcome Probabilities 586 Average Payoff Strategy 586 ® Expected Value Strategy 586 * Evaluating Risk 587 Decision Trees 588 Decision Trees and Monte Carlo Simulation 592 ® Decision Trees and Risk 592 * Sensitivity Analysis in Decision Trees 594 The Value of Information 595 Decisions with Sample Information 596 * Bayes’s Rule
596 Utility and Decision Making 598 Constructing a Utility Function 599 e Exponential Utility Functions 602 Key Tenns 604 · Problems and Exercises Equipment 608 604 * Case: Performance Lawn
Contents 15 Supplementary Chapter  (online) Nonlinear and Non-Smooth Optimization Supplementary Chapter В (online) Optimization Models with Uncertainty Online chapters are available for download at www.pearsonglobaleditions.com/Evans. Appendix A 611 Glossary 635 Index 643
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| spelling | Evans, James R. 1950- Verfasser (DE-588)170321924 aut Business analytics methods, models, and decisions James R. Evans, University of Cincinnati Global edition, second edition Boston [und 23 weitere] Pearson [2017] © 2017 652 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Always learning Datenmanagement (DE-588)4213132-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Betriebsdaten (DE-588)4145038-3 gnd rswk-swf Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Unternehmen (DE-588)4061963-1 s Betriebsdaten (DE-588)4145038-3 s Datenanalyse (DE-588)4123037-1 s DE-604 Betriebliches Informationssystem (DE-588)4069386-7 s Datenmanagement (DE-588)4213132-7 s Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029007126&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Evans, James R. 1950- Business analytics methods, models, and decisions Datenmanagement (DE-588)4213132-7 gnd Datenanalyse (DE-588)4123037-1 gnd Betriebsdaten (DE-588)4145038-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Unternehmen (DE-588)4061963-1 gnd |
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| title | Business analytics methods, models, and decisions |
| title_auth | Business analytics methods, models, and decisions |
| title_exact_search | Business analytics methods, models, and decisions |
| title_full | Business analytics methods, models, and decisions James R. Evans, University of Cincinnati |
| title_fullStr | Business analytics methods, models, and decisions James R. Evans, University of Cincinnati |
| title_full_unstemmed | Business analytics methods, models, and decisions James R. Evans, University of Cincinnati |
| title_short | Business analytics |
| title_sort | business analytics methods models and decisions |
| title_sub | methods, models, and decisions |
| topic | Datenmanagement (DE-588)4213132-7 gnd Datenanalyse (DE-588)4123037-1 gnd Betriebsdaten (DE-588)4145038-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Unternehmen (DE-588)4061963-1 gnd |
| topic_facet | Datenmanagement Datenanalyse Betriebsdaten Betriebliches Informationssystem Unternehmen Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029007126&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT evansjamesr businessanalyticsmethodsmodelsanddecisions |