Business analytics : data analysis and decison making

Cover
About the Authors
Brief Contents
Contents
Preface
Chapter 1: Introduction to Business Analytics
	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
		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
		Case 2.1 Correct Interpretation of Means
		Case 2.2 The Dow Jones Industrial Average
		Case 2.3 Home and Condo Prices
		Appendix: Introduction to StatTools
	Chapter 3: Finding Relationships among Variables
		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
		Case 3.1 Customer Arrivals at Bank98
		Case 3.2 Saving, Spending, and Social Climbing
		Case 3.3 Churn in the Cellular Phone Market
		Case 3.4 Southwest Border Apprehensions and Unemployment
		Appendix: Using StatTools to Find Relationships
	Chapter 4: Business Intelligence (BI) Tools for Data Analysis
		4-1 Introduction
		4-2 Importing Data into Excel with Power Query
		4-3 Data Analysis with Power Pivot
		4-4 Data Visualization with Tableau Public
		4-5 Data Cleansing
		4-6 Conclusion
		Summary of Key Terms
		Problems
Part 2: Probability and Decision Making under Uncertainty
	Chapter 5: Probability and Probability Distributions
		5-1 Introduction
		5-2 Probability Essentials
		5-3 Probability Distribution of a Random Variable
		5-4 The Normal Distribution
		5-5 The Binomial Distribution
		5-6 The Poisson and Exponential Distributions
		5-7 Conclusion
		Summary of Key Terms
		Problems
		Case 5.1 Simpson's Paradox
		Case 5.2 EuroWatch Company
		Case 5.3 Cashing in on the Lottery
	Chapter 6: Decision Making under Uncertainty
		6-1 Introduction
		6-2 Elements of Decision Analysis
		6-3 EMV and Decision Trees
		6-4 One-Stage Decision Problems
		6-5 The PrecisionTree Add-In
		6-6 Multistage Decision Problems
		6-7 The Role of Risk Aversion
		6-8 Conclusion
		Summary of Key Terms
		Problems
		Case 6.1 Jogger Shoe Company
		Case 6.2 Westhouser Paper Company
		Case 6.3 Electronic Timing System for Olympics
		Case 6.4 Developing a Helicopter Component for the Army
		Appendix: Decision Trees with DADM_Tools
Part 3: Statistical Inference
	Chapter 7: Sampling and Sampling Distributions
		7-1 Introduction
		7-2 Sampling Terminology
		7-3 Methods for Selecting Random Samples
		7-4 Introduction to Estimation
		7-5 Conclusion
		Summary of Key Terms
		Problems
	Chapter 8: Confidence Interval Estimation
		8-1 Introduction
		8-2 Sampling Distributions
		8-3 Confidence Interval for a Mean
		8-4 Confidence Interval for a Total
		8-5 Confidence Interval for a Proportion
		8-6 Confidence Interval for a Standard Deviation
		8-7 Confidence Interval for the Difference between Means
		8-8 Confidence Interval for the Difference between Proportions
		8-9 Sample Size Selection
		8-10 Conclusion
		Summary of Key Terms
		Problems
		Case 8.1 Harrigan University Admissions
		Case 8.2 Employee Retention at D&Y
		Case 8.3 Delivery Times at SnowPea Restaurant
	Chapter 9: Hypothesis Testing
		9-1 Introduction
		9-2 Concepts in Hypothesis Testing
		9-3 Hypothesis Tests for a Population Mean
		9-4 Hypothesis Tests for Other Parameters
		9-5 Tests for Normality
		9-6 Chi-Square Test for Independence
		9-7 Conclusion
		Summary of Key Terms
		Problems
		Case 9.1 Regression toward the Mean
		Case 9.2 Friday Effect in the Stock Market
		Case 9.3 Removing Vioxx from the Market
Part 4: Regression Analysis and Time Series Forecasting
	Chapter 10: Regression Analysis: Estimating Relationships
		10-1 Introduction
		10-2 Scatterplots: Graphing Relationships
		10-3 Correlations: Indicators of Linear Relationships
		10-4 Simple Linear Regression
		10-5 Multiple Regression
		10-6 Modeling Possibilities
		10-7 Validation of the Fit
		10-8 Conclusion
		Summary of Key Terms
		Problems
		Case 10.1 Quantity Discounts at Firm Chair Company
		Case 10.2 Housing Price Structure in Mid City
		Case 10.3 Demand for French Bread at Howie's Bakery
		Case 10.4 Investing for Retirement
	Chapter 11: Regression Analysis: Statistical Inference
		11-1 Introduction
		11-2 The Statistical Model
		11-3 Inferences about the Regression Coefficients
		11-4 Multicollinearity
		11-5 Include/Exclude Decisions
		11-6 Stepwise Regression
		11-7 Outliers
		11-8 Violations of Regression Assumptions
		11-9 Prediction
		11-10 Conclusion
		Summary of Key Terms
		Problems
		Case 11.1 Heating Oil at Dupree Fuels
		Case 11.2 Developing a Flexible Budget at the Gunderson Plant
		Case 11.3 Forecasting Overhead at Wagner Printers
	Chapter 12: Time Series Analysis and Forecasting
		12-1 Introduction
		12-2 Forecasting Methods: An Overview
		12-3 Testing for Randomness
		12-4 Regression-Based Trend Models
		12-5 The Random Walk Model
		12-6 Moving Averages Forecasts
		12-7 Exponential Smoothing Forecasts
		12-8 Seasonal Models
		12-9 Conclusion
		Summary of Key Terms
		Problems
		Case 12.1 Arrivals at the Credit Union
		Case 12.2 Forecasting Weekly Sales at Amanta
		Appendix: Alternative Forecasting Software
Part 5: Optimization and Simulation Modeling
	Chapter 13: Introduction to Optimization Modeling
		13-1 Introduction
		13-2 Introduction to Optimization
		13-3 A Two-Variable Product Mix Model
		13-4 Sensitivity Analysis
		13-5 Properties of Linear Models
		13-6 Infeasibility and Unboundedness
		13-7 A Larger Product Mix Model
		13-8 A Multiperiod Production Model
		13-9 A Comparison of Algebraic and Spreadsheet Models
		13-10 A Decision Support System
		13-11 Conclusion
		Summary of Key Terms
		Problems
		Case 13.1 Shelby Shelving
	Chapter 14: Optimization Models
		14-1 Introduction
		14-2 Employee Scheduling Models
		14-3 Blending Models
		14-4 Logistics Models
		14-5 Aggregate Planning Models
		14-6 Financial Models
		14-7 Integer Optimization Models
		14-8 Nonlinear Optimization Models
		14-9 Conclusion
		Summary of Key Terms
		Problems
		Case 14.1 Giant Motor Company
		Case 14.2 GMS Stock Hedging
	Chapter 15: Introduction to Simulation Modeling
		15-1 Introduction
		15-2 Probability Distributions for Input Variables
		15-3 Simulation and the Flaw of Averages
		15-4 Simulation with Built-in Excel Tools
		15-5 Simulation with @RISK
		15-6 The Effects of Input Distributions on Results
		15-7 Conclusion
		Summary of Key Terms
		Problems
		Case 15.1 Ski Jacket Production
		Case 15.2 Ebony Bath Soap
		Appendix: Simulation with DADM_Tools
	Chapter 16: Simulation Models
		16-1 Introduction
		16-2 Operations Models
		16-3 Financial Models
		16-4 Marketing Models
		16-5 Simulating Games of Chance
		16-6 Conclusion
		Summary of Key Terms
		Problems
		Case 16.1 College Fund Investment
		Case 16.2 Bond Investment Strategy
Part 6: Advanced Data Analysis
	Chapter 17: Data Mining
		17-1 Introduction
		17-2 Classification Methods
		17-3 Clustering Methods
		17-4 Conclusion
		Summary of Key Terms
		Problems
		Case 17.1 Houston Area Survey
References
Index