Business Analytics: Data Analysis & Decision Making

Name: Business Analytics: Data Analysis & Decision Making
Price: 4990 ISK
Availability: InStock
Author: S. Christian Albright, Wayne L. Winston
ISBN: 9798214493497

Höfundar: S. Christian Albright, Wayne L. Winston (Útgáfa: 8)

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CIE BUSINESS ANALYTICS DATA AN ALYSIS/DECISION MAKING Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

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Útgefandi: Cengage Learning EMEA

ISBN: 9798214493497

Print ISBN: 9798214050270

Format: Page Fidelity (PDF)

Útgáfa: 8

Höfundar: S. Christian Albright, Wayne L. Winston

Tungumál: English

Útgefið: 06/15/2024

Prent takmörkun á líftíma: 100

Prent takmörkun: 2

Afritunar takmörkun: 2

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Kaflar

Dedication
About The Authors
Brief Contents
Contents
Preface
Overview of Applications in the Book by Discipline
Chapter 1: Introduction to Business Analytics
Learning Objectives
Business Analytics Provides Insights and Improves Performance
1-1 Introduction
1-2 Overview of the Book
1-3 Introduction to Spreadsheet Modeling
1-4 Conclusion
Summary of Key Terms
Problems
Part 1: Data Analysis
Chapter 2: Describing the Distribution of a Variable
Learning Objectives
Recent Presidential Elections
2-1 Introduction
2-2 Basic Concepts
2-3 Summarizing Categorical Variables
2-4 Summarizing Numeric Variables
2-5 Time Series Data
2-6 Outliers and Missing Values
2-7 Excel Tables for Filtering, Sorting, and Summarizing
2-8 Conclusion
Summary of Key Terms
Problems
Appendix: Introduction to StatTools
Appendix: Summary Stats with DADM_Tools
Chapter 3: Finding Relationships
Learning Objectives
Data Analytics at NYPD
3-1 Introduction
3-2 Relationships Among Categorical Variables
3-3 Relationships Among Categorical Variables and a Numeric Variable
3-4 Relationships Among Numeric Variables
3-5 Pivot Tables
3-6 Conclusion
Summary of Key Terms
Problems
Appendix: Using StatTools to Find Relationships
Appendix: Using DADM_Tools to Summarize Data
Chapter 4: Business Intelligence (BI) Tools
Learning Objectives
Power BI at Heathrow Airport
4-1 Introduction
4-2 Introduction to Relational Databases
4-3 Storing Data in a Data Model
4-4 Using Power Query in Excel
4-5 Using Power Query in Power BI Desktop
4-6 Conclusion
Summary of Key Terms
Problems
Chapter 5: Business Intelligence (BI) Tools for Data Analysis: Power Pivot
Learning Objectives
Power BI at T-Mobile
5-1 Introduction
5-2 Basing Pivot Tables on a Data Model
5-3 Using Power Pivot in Excel
5-4 Creating KPIs in Power Pivot
5-5 Creating Visualizations in Power BI Desktop
5-6 Conclusion
Summary of Key Terms
Problems
Appendix: Writing Complex DAX Formulas
Part 2: Probability and Decision Making under Uncertainty
Chapter 6: Probability and Probability Distributions
Learning Objectives
Judging Probabilities
6-1 Introduction
6-2 Probability Essentials
6-3 Probability Distribution of a Random Variable
6-4 The Normal Distribution
6-5 The Binomial Distribution
6-6 The Poisson and Exponential Distributions
6-7 Conclusion
Summary of Key Terms
Problems
Chapter 7: Decision Making Under Uncertainty
Learning Objectives
Cost-Effective Hepatitis B Interventions
7-1 Introduction
7-2 Elements of Decision Analysis
7-3 EMV and Decision Trees
7-4 One-Stage Decision Problems
7-5 The PrecisionTree Add-In
7-6 Multistage Decision Problems
7-7 The Role of Risk Aversion
7-8 Conclusion
Summary of Key Terms
Problems
Appendix: Decision Trees with DADM_Tools
Part 3: Statistical Inference, Regression Analysis, and Time Series Forecasting
Chapter 8: Statistical Inference
Learning Objectives
Sample Size Selection in a Legal Case
8-1 Introduction
8-2 Populations and Sampling
8-3 Sampling Distributions
8-4 Concepts in Hypothesis Testing and Confidence Interval Estimation
8-5 Overview of Statistical Inference Procedures
8-6 Inference About a Mean
8-7 Inference About the Difference Between Means
8-8 Inference About a Proportion
8-9 Inference About the Difference Between Proportions
8-10 Sample Size Determination
8-11 Conclusion
Summary of Key Terms
Problems
Chapter 9: Regression Analysis: Estimating Relationships
Learning Objectives
Hewlett Packard Delivering Profitable Growth for HPDirect.com
9-1 Introduction
9-2 Scatterplots: Graphing Relationships
9-3 Correlations: Indicators of Linear Relationships
9-4 Simple Linear Regression
9-5 Multiple Regression
9-6 Modeling Possibilities
9-7 Validation of the Fit
9-8 Conclusion
Summary of Key Terms
Problems
Appendix: Regression with DADM_Tools
Chapter 10: Regression Analysis: Statistical Inference
Learning Objectives
Forecasting in the Tire Industry
10-1 Introduction
10-2 The Statistical Model
10-3 Inferences About the Regression Coefficients
10-4 Multicollinearity
10-5 Include/Exclude Decisions
10-6 Stepwise Regression
10-7 Outliers
10-8 Violations of Regression Assumptions
10-9 Prediction
10-10 Conclusion
Summary of Key Terms
Problems
Chapter 11: Time Series Analysis and Forecasting
Learning Objectives
Revenue Managementat Harrah's Cherokee Casino & Hotel
11-1 Introduction
11-2 Forecasting Methods: An Overview
11-3 Testing for Randomness
11-4 Regression-Based Trend Models
11-5 The Random Walk Model
11-6 Moving Averages Forecasts
11-7 Exponential Smoothing Forecasts
11-8 Seasonal Models
11-9 Conclusion
Summary of Key Terms
Problems
Appendix: Alternative Forecasting Software
Part 4: Optimization and SimulationModeling
Chapter 12: Introduction to Optimization Modeling
Learning Objectives
Inventory Optimization at GM
12-1 Introduction
12-2 Introduction to Optimization
12-3 A Two-Variable Product Mix Model
12-4 Sensitivity Analysis
12-5 Properties of Linear Models
12-6 Infeasibility and Unboundedness
12-7 A Larger Product Mix Model
12-8 A Multiperiod Production Model
12-9 A Comparison of Algebraic and Spreadsheet Models
12-10 A Decision Support System
12-11 Conclusion
Summary of Key Terms
Problems
Appendix: Another Solver Add-In
Chapter 13: Optimization Models
Learning Objectives
Optimization of Work Center Locations at Verizon
13-1 Introduction
13-2 Employee Scheduling Models
13-3 Blending Models
13-4 Logistics Models
13-5 Aggregate Planning Models
13-6 Financial Models
13-7 Integer Optimization Models
13-8 Nonlinear Optimization Models
13-9 Conclusion
Summary of Key Terms
Problems
Chapter 14: Introduction to Simulation Modeling
Learning Objectives
Real Applications of Simulation with @RISK
14-1 Introduction
14-2 Probability Distributions for Input Variables
14-3 Simulation and the Flaw of Averages
14-4 Simulation with Built-in Excel Tools
14-5 Simulation with @RISK
14-6 The Effects of Input Distributions on Results
14-7 Conclusion
Summary of Key Terms
Problems
Appendix: Simulation with DADM_Tools
Chapter 15: Simulation Models
Learning Objectives
Effects of Merit Pay on Payroll Growth at DoD
15-1 Introduction
15-2 Operations Models
15-3 Financial Models
15-4 Marketing Models
15-5 Simulating Games of Chance
15-6 Conclusion
Summary of Key Terms
Problems
Part 5: Advanced Data Analysis
Chapter 16: Data Mining: Classification
Learning Objectives
Hottest New Jobs: Statistics and Mathematics
16-1 Introduction
16-2 Classification Methods
16-3 Measures of Classification Accuracy
16-4 Classification with Rare Events
16-5 Conclusion
Summary of Key Terms
Problems
Chapter 17: Data Mining: Clustering and Market Basket Analysis
Learning Objectives
Beer and Diapers: a Myth?
17-1 Introduction
17-2 Clustering
17-3 Market Basket Analysis
17-4 Conclusion
Summary of Key Terms
Problems
References
Index