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Business Analytics: Data Analysis & Decision Making 7th edition [Hardback]

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(Indiana University, School of Business (Emeritus)), (Indiana University, Kelley School of Business (Emeritus))
  • Formāts: Hardback, 984 pages, height x width x depth: 38x220x281 mm, weight: 2131 g
  • Izdošanas datums: 02-Apr-2019
  • Izdevniecība: South-Western College Publishing
  • ISBN-10: 0357109953
  • ISBN-13: 9780357109953
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Business Analytics: Data Analysis & Decision Making 7th editionPalielināt
  • Formāts: Hardback, 984 pages, height x width x depth: 38x220x281 mm, weight: 2131 g
  • Izdošanas datums: 02-Apr-2019
  • Izdevniecība: South-Western College Publishing
  • ISBN-10: 0357109953
  • ISBN-13: 9780357109953
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780357109953.html
Keywords:
Master data analysis, modeling and the effective use of spreadsheets with the popular BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 7E. The quantitative methods approach in this edition helps you maximize your success with a proven teach-by-example presentation, inviting writing style and complete integration of the latest version of Excel. The approach is also compatible with earlier versions of Excel for your convenience. This edition is more data-oriented than ever before with a new chapter on the two main Power BI tools in Excel -- Power Query and Power Pivot -- and a new section of data visualization with Tableau Public. Current problems and cases demonstrate the importance of the concepts you are learning. In addition, a useful Companion Website provides data and solutions files, SolverTable for optimization sensitivity analysis and Palisade DecisionTools Suite. MindTap online resources are also available.
Preface xvi
1 Introduction to Business Analytics
1(36)
1-1 Introduction
3(1)
1-2 Overview of the Book
4(1)
1-2a The Methods
4(2)
1-2b The Software
6(2)
1-3 Introduction to Spreadsheet Modeling
8(1)
1-3a Basic Spreadsheet Modeling: Concepts and Best Practices
9(3)
l-3b Cost Projections
12(3)
1-3c Breakeven Analysis
15(5)
1-3d Ordering with Quantity Discounts and Demand Uncertainty
20(4)
1-3e Estimating the Relationship between Price and Demand
24(5)
1-3f Decisions Involving the Time Value of Money
29(4)
1-4 Conclusion
33(4)
PART 1 Data Analysis
37(1)
2 Describing the Distribution of a Variable
38(1)
2-1 Introduction
39(2)
2-2 Basic Concepts
41(4)
2-2a Populations and Samples
41(1)
2-2b Data Sets, Variables, and Observations
41(1)
2-2c Data Types
42(3)
2-3 Summarizing Categorical Variables
45(4)
2-4 Summarizing Numeric Variables
49(13)
2-4a Numeric Summary Measures
49(8)
2-4b Charts for Numeric Variables
57(5)
2-5 Time Series Data
62(7)
2-6 Outliers and Missing Values
69(2)
2-7 Excel Tables for Filtering, Sorting, and Summarizing
71(6)
2-8 Conclusion
77(7)
Appendix: Introduction to StatTools
83(1)
3 Finding Relationships among Variables
84(1)
3-1 Introduction
85(1)
3-2 Relationships among Categorical Variables
86(3)
3-3 Relationships among Categorical Variables and a Numeric Variable
89(7)
3-4 Relationships among Numeric Variables
96(10)
3-4a Scatterplots
96(5)
3-4b Correlation and Covariance
101(5)
3-5 Pivot Tables
106(20)
3-6 Conclusion
126(6)
Appendix: Using StatTools to Find Relationships
131(1)
4 Business Intelligence (Bl) Tools for Data Analysis
132(1)
4-1 Introduction
133(1)
4-2 Importing Data into Excel with Power Query
134(18)
4-2a Introduction to Relational Databases
134(5)
4-2b Excel's Data Model
139(7)
4-2c Creating and Editing Queries
146(6)
4-3 Data Analysis with Power Pivot
152(10)
4-3a Basing Pivot Tables on a Data Model
154(1)
4-3b Calculated Columns, Measures, and the DAX Language
154(8)
4-4 Data Visualization with Tableau Public
162(10)
4-5 Data Cleansing
172(6)
4-6 Conclusion
178(5)
PART 2 Probability and Decision Making under Uncertainty
183(1)
5 Probability and Probability Distributions
184(1)
5-1 Introduction
185(1)
5-2 Probability Essentials
186(8)
5-2a Rule of Complements
187(1)
5-2b Addition Rule
187(1)
5-2c Conditional Probability and the Multiplication Rule
188(2)
5-2d Probabilistic Independence
190(1)
5-2e Equally Likely Events
191(1)
5-2f Subjective Versus Objective Probabilities
192(2)
5-3 Probability Distribution of a Random Variable
194(6)
5-3a Summary Measures of a Probability Distribution
195(3)
5-3b Conditional Mean and Variance
198(2)
5-4 The Normal Distribution
200(14)
5-4a Continuous Distributions and Density Functions
200(1)
5-4b The Normal Density Function
201(1)
5-4c Standardizing: Z-Values
202(2)
5-4d Normal Tables and Z-Values
204(1)
5-4e Normal Calculations in Excel
205(3)
5-4f Empirical Rules Revisited
208(1)
5-4g Weighted Sums of Normal Random Variables
208(1)
5-4h Normal Distribution Examples
209(5)
5-5 The Binomial Distribution
214(12)
5-5a Mean and Standard Deviation of the Binomial Distribution
217(1)
5-5b The Binomial Distribution in the Context of Sampling
217(1)
5-5c The Normal Approximation to the Binomial
218(1)
5-5d Binomial Distribution Examples
219(7)
5-6 The Poisson and Exponential Distributions
226(5)
5-6a The Poisson Distribution
227(2)
5-6b The Exponential Distribution
229(2)
5-7 Conclusion
231(11)
6 Decision Making under Uncertainty
242(1)
6-1 Introduction
243(1)
6-2 Elements of Decision Analysis
244(3)
6-3 EMV and Decision Trees
247(4)
6-4 One-Stage Decision Problems
251(3)
6-5 The PrecisionTree Add-In
254(3)
6-6 Multistage Decision Problems
257(5)
6.6a Bayes' Rule
262(12)
6-6b The Value of Information
267(3)
6-6c Sensitivity Analysis
270(4)
6-7 The Role of Risk Aversion
274(6)
6-7a Utility Functions
275(1)
6-7b Exponential Utility
275(3)
6-7c Certainty Equivalents
278(1)
6-7d Is Expected Utility Maximization Used?
279(1)
6-8 Conclusion
280(13)
PART 3 Statistical Inference
293(118)
7 Sampling and Sampling Distributions
294(1)
7-1 Introduction
295(1)
7-2 Sampling Terminology
295(2)
7-3 Methods for Selecting Random Samples
297(8)
7-3a Simple Random Sampling
297(4)
7-3b Systematic Sampling
301(1)
7-3c Stratified Sampling
301(2)
7-3d Cluster Sampling
303(1)
7-3e Multistage Sampling
303(2)
7-4 Introduction to Estimation
305(15)
7-4a Sources of Estimation Error
305(1)
7-4b Key Terms in Sampling
306(1)
7-4c Sampling Distribution of the Sample Mean
307(5)
7-4d The Central Limit Theorem
312(5)
7-4e Sample Size Selection
317(1)
7-4f Summary of Key Ideas in Simple Random Sampling
318(2)
7-5 Conclusion
320(3)
8 Confidence Interval Estimation
323(1)
8-1 Introduction
323(2)
8-2 Sampling Distributions
325(1)
8-2a The t Distribution
326(1)
8-2b Other Sampling Distributions
327(1)
8-3 Confidence Interval for a Mean
328(5)
8-4 Confidence Interval for a Total
333(3)
8-5 Confidence Interval for a Proportion
336(4)
8-6 Confidence Interval for a Standard Deviation
340(3)
8-7 Confidence Interval for the Difference between Means
343(5)
8-7a Independent Samples
344(2)
8-7b Paired Samples
346(2)
8-8 Confidence Interval for the Difference between Proportions
348(3)
8-9 Sample Size Selection
351(7)
8-10 Conclusion
358(10)
9 Hypothesis Testing
368(1)
9-1 Introduction
369(1)
9-2 Concepts in Hypothesis Testing
370(6)
9-2a Null and Alternative Hypotheses
370(1)
9-2b One-Tailed Versus Two-Tailed Tests
371(1)
9-2c Types of Errors
372(1)
9-2d Significance Level and Rejection Region
372(1)
9-2e Significance from p-values
373(2)
9-2f Type II Errors and Power
375(1)
9-2g Hypothesis Tests and Confidence Intervals
375(1)
9-2h Practical Versus Statistical Significance
375(1)
9-3 Hypothesis Tests for a Population Mean
376(4)
9-4 Hypothesis Tests for Other Parameters
380(15)
9-4a Hypothesis Test for a Population Proportion
380(2)
9-4b Hypothesis Tests for Difference between Population Means
382(6)
9-4c Hypothesis Test for Equal Population Variances
388(1)
9-4d Hypothesis Test for Difference between Population Proportions
388(7)
9-5 Tests for Normality
395(6)
9-6 Chi-Square Test for Independence
401(3)
9-7 Conclusion
404(7)
PART 4 Regression Analysis and Time Series Forecasting
411(164)
10 Regression Analysis: Estimating Relationships
412(1)
10-1 Introduction
413(2)
10-2 Scatterplots: Graphing Relationships
415(7)
10-3 Correlations: Indicators of Linear Relationships
422(2)
10-4 Simple Linear Regression
424(11)
10-4a Least Squares Estimation
424(7)
10-4b Standard Error of Estimate
431(1)
10-4c R-Square
432(3)
10-5 Multiple Regression
435(7)
10-5a Interpretation of Regression Coefficients
436(3)
10-5b Interpretation of Standard Error of Estimate and R-Square
439(3)
10-6 Modeling Possibilities
442(19)
10-6a Dummy Variables
442(6)
10-6b Interaction Variables
448(4)
10-6c Nonlinear Transformations
452(9)
10-7 Validation of the Fit
461(2)
10-8 Conclusion
463(9)
11 Regression Analysis: Statistical Inference
472(1)
11-1 Introduction
473(1)
11-2 The Statistical Model
474(3)
11-3 Inferences About the Regression Coefficients
477(8)
11-3a Sampling Distribution of the Regression Coefficients
478(2)
11-3b Hypothesis Tests for the Regression Coefficients and p-Values
480(1)
11-3c A Test for the Overall Fit: The ANOVA Table
481(4)
11-4 Multicollinearity
485(4)
11-5 Include/Exclude Decisions
489(5)
11-6 Stepwise Regression
494(5)
11-7 Outliers
499(5)
11-8 Violations of Regression Assumptions
504(3)
11-8a Nonconstant Error Variance
504(1)
11-8b Nonnormality of Residuals
504(1)
11-8c Autocorrelated Residuals
505(2)
11-9 Prediction
507(5)
11-10 Conclusion
512(11)
12 Time Series Analysis and Forecasting
523(1)
12-1 Introduction
524(1)
12-2 Forecasting Methods: An Overview
525(6)
12-2a Extrapolation Models
525(1)
12-2b Econometric Models
526(1)
12-2c Combining Forecasts
526(1)
12-2d Components of Time Series Data
527(2)
12-2e Measures of Accuracy
529(2)
12-3 Testing for Randomness
531(8)
12-3a The Runs Test
534(1)
12-3b Autocorrelation
535(4)
12-4 Regression-Based Trend Models
539(5)
12-4a Linear Trend
539(2)
12-4b Exponential Trend
541(3)
12-5 The Random Walk Model
544(3)
12-6 Moving Averages Forecasts
547(4)
12-7 Exponential Smoothing Forecasts
551(9)
12-7a Simple Exponential Smoothing
552(4)
12-7b Holt's Model for Trend
556(4)
12-8 Seasonal Models
560(9)
12-8a Winters' Exponential Smoothing Model
561(3)
12-8b Deseasonalizing: The Ratio-to-Moving-Averages Method
564(1)
12-8c Estimating Seasonality with Regression
565(4)
12-9 Conclusion
569(6)
PART 5 Optimization and Simulation Modeling
575(1)
13 Introduction to Optimization Modeling
576(1)
13-1 Introduction
577(1)
13-2 Introduction to Optimization
577(2)
13-3 A Two-Variable Product Mix Model
579(11)
13-4 Sensitivity Analysis
590(1)
13-4a Solver's Sensitivity Report
590(3)
13-4b SolverTable Add-In
593(6)
13-4c A Comparison of Solver's Sensitivity Report and SolverTable
599(1)
13-5 Properties of Linear Models
600(2)
13-6 Infeasibility and Unboundedness
602(2)
13-7 A Larger Product Mix Model
604(8)
13-8 A Multiperiod Production Model
612(7)
13-9 A Comparison of Algebraic and Spreadsheet Models
619(1)
13-10 A Decision Support System
620(2)
13-11 Conclusion
622(8)
14 Optimization Models
630(1)
14-1 Introduction
631(1)
14-2 Employee Scheduling Models
632(6)
14-3 Blending Models
638(6)
14-4 Logistics Models
644(1)
14-4a Transportation Models
644(7)
14-4b More General Logistics Models
651(8)
14-5 Aggregate Planning Models
659(8)
14-6 Financial Models
667(10)
14-7 Integer Optimization Models
677(18)
14-7a Capital Budgeting Models
678(4)
14-7b Fixed-Cost Models
682(7)
14-7c Set-Covering Models
689(6)
14-8 Nonlinear Optimization Models
695(13)
14-8a Difficult Issues in Nonlinear Optimization
695(1)
14-8b Managerial Economics Models
696(4)
14-8c Portfolio Optimization Models
700(8)
14-9 Conclusion
708(9)
15 Introduction to Simulation Modeling
717(1)
15-1 Introduction
718(2)
15-2 Probability Distributions for Input Variables
720(1)
15-2a Types of Probability Distributions
721(3)
15-2b Common Probability Distributions
724(4)
15-2c Using @RISK to Explore Probability Distributions
728(8)
15-3 Simulation and the Flaw of Averages
736(2)
15-4 Simulation with Built-in Excel Tools
738(9)
15-5 Simulation with @RISK
747(16)
15-5a @RISK Feature
748(1)
15-5b Loading CHVRISK
748(1)
15-5c @RISK Models with a Single Random Input
749(9)
15-5d Some Limitations of @RISK
758(1)
15-5e @RISK Models with Several Random Inputs
758(5)
15-6 The Effects of Input Distributions on Results
763(8)
15-6a Effect of the Shape of the Input Distribution(s)
763(3)
15-6b Effect of Correlated Inputs
766(5)
15-7 Conclusion
771(8)
16 Simulation Models
779(1)
16-1 Introduction
780(1)
16-2 Operations Models
780(1)
16-2a Bidding for Contracts
780(4)
16-2b Warranty Costs
784(5)
16-2c Drug Production with Uncertain Yield
789(5)
16-3 Financial Models
794(16)
16-3a Financial Planning Models
795(4)
16-3b Cash Balance Models
799(4)
16-3c Investment Models
803(7)
16-4 Marketing Models
810(13)
16-4a Customer Loyalty Models
810(7)
16-4b Marketing and Sales Models
817(6)
16-5 Simulating Games of Chance
823(5)
16-5a Simulating the Game of Craps
823(2)
16-5b Simulating the NCAA Basketball Tournament
825(3)
16-6 Conclusion
828(9)
PART 6 Advanced Data Analysis
837(1)
17 Data Mining
838(1)
17-1 Introduction
839(1)
17-2 Classification Methods
840(1)
17-2a Logistic Regression
841(5)
17-2b Neural Networks
846(5)
17-2c Naive Bayes
851(3)
17-2d Classification Trees
854(1)
17-2e Measures of Classification Accuracy
855(2)
17-2f Classification with Rare Events
857(3)
17-3 Clustering Methods
860(10)
17-4 Conclusion
870
18 Analysis of Variance and Experimental Design (MindTap Reader only)
18-1 Introduction
2(3)
18-2 One-Way ANOVA
5(1)
18-2a The Equal-Means Test
5(2)
18-2b Confidence Intervals for Differences Between Means
7(4)
18-2c Using a Logarithmic Transformation
11(4)
18-3 Using Regression to Perform ANOVA
15(3)
18-4 The Multiple Comparison Problem
18(4)
18-5 Two-Way ANOVA
22(6)
18-5a Confidence Intervals for Contrasts
28(2)
18-5b Assumptions of Two-Way ANOVA
30(2)
18-6 More About Experimental Design
32(1)
18-6a Randomization
32(3)
18-6b Blocking
35(3)
18-6c Incomplete Designs
38(2)
18-7 Conclusion
40
19 Statistical Process Control (MindTap Reader only)
19-1 Introduction
2(1)
19-2 Deming's 14 Points
3(3)
19-3 Introduction to Control Charts
6(2)
19-4 Control Charts for Variables
8(18)
19-4a Control Charts and Hypothesis Testing
13(2)
19-4b Other Out-of-Control Indications
15(1)
19-4c Rational Subsamples
16(2)
19-4d Deming's Funnel Experiment and Tampering
18(4)
19-4e Control Charts in the Service Industry
22(4)
19-5 Control Charts for Attributes
26(1)
19-5a P Charts
26(3)
19-5b Deming's Red Bead Experiment
29(4)
19-6 Process Capability
33(2)
19-6a Process Capability Indexes
35(5)
19-6b More on Motorola and 6-Sigma
40(3)
19-7 Conclusion
43
APPENDIX A Quantitative Reporting (MindTap Reader only)
A-1 Introduction
1(1)
A-2 Suggestions for Good Quantitative Reporting
2(4)
A-2a Planning
2(1)
A-2b Developing a Report
3(1)
A-2c Be Clear
4(1)
A-2d Be Concise
4(1)
A-2e Be Precise
5(1)
A-3 Examples of Quantitative Reports
6(10)
A-4 Conclusion
16(857)
References 873(2)
Index 875
S. Christian Albright received both his B.S. degree in mathematics and his Ph.D. in operations research from Stanford University. He then taught in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University until his retirement in 2011. He has taught courses in management science, computer simulation and statistics to all levels of business students, including undergraduate, M.B.A. and Ph.D. students. He has published more than 20 articles in leading operations research journals in applied probability. After retiring, he worked for several years for the Palisade Software Company and is the author of several successful textbooks. Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now Professor of Decision and Information Sciences at the Bauer College at the University of Houston. Dr. Winston has received more than 45 teaching awards and is a six-time recipient of the school-wide M.B.A. award. His current interest focuses on showing how to use spreadsheet models to solve business problems in all disciplines, particularly in finance, sports and marketing. In addition to publishing more than 20 articles in leading journals, Dr. Winston has written successful textbooks on topics including operations research, mathematical programming, simulation modeling, spreadsheet modeling, data analysis for managers, marketing analytics and more. Dr. Winston received his B.S. degree in mathematics from MIT and his Ph.D. in operations research from Yale.