### Test bank for Business Analytics Data Analysis and Decision Making – Standalone book 6th Edition by S. Christian Albright

#### Test bank for Business Analytics Data Analysis and Decision Making – Standalone book 6th Edition by S. Christian Albright

Preface

Chapter 1: Introduction to Business Analytics

1-1 Introduction

1-2 Overview Of The Book

1-2a The Methods

1-2b The Software

1-3 Modeling And Models

1-3a Graphical Models

1-3b Algebraic Models

1-3c Spreadsheet Models

1-3d A Seven-Step Modeling Process

1-4 Conclusion

Part 1: Exploring Data

Chapter 2: Describing the Distribution of a Single Variable

2-1 Introduction

2-2 Basic Concepts

2-2a Populations and Samples

2-2b Data Sets, Variables, and Observations

2-2c Types of Data

2-3 Descriptive Measures For Categorical Variables

2-4 Descriptive Measures For Numerical Variables

2-4a Numerical Summary Measures

2-4b Numerical Summary Measures with StatTools

2-4c Analysis ToolPak Add-In

2-4d Charts for Numerical Variables

2-5 Time Series Data

2-6 Outliers And Missing Values

2-6a Outliers

2-6b Missing Values

2-7 Excel Tables For Filtering, Sorting, And Summarizing

2-8 Conclusion

Summary of Key Terms

Chapter 3: Finding Relationships among Variables

3-1 Introduction

3-2 Relationships Among Categorical Variables

3-3 Relationships Among Categorical Variables And A Numerical Variable

3-3a Stacked and Unstacked Formats

3-4 Relationships Among Numerical Variables

3-4a Scatterplots

3-4b Correlation and Covariance

3-5 Pivot Tables

3-6 Conclusion

Part 2: Probability and Decision Making under Uncertainty

Chapter 4: Probability and Probability Distributions

4-1 Introduction

4-2 Probability Essentials

4-2a Rule of Complements

4-2b Addition Rule

4-2c Conditional Probability and the Multiplication Rule

4-2d Probabilistic Independence

4-2e Equally Likely Events

4-2f Subjective Versus Objective Probabilities

4-3 Probability Distribution Of A Single Random Variable

4-3a Summary Measures of a Probability Distribution

4-3b Conditional Mean and Variance

4-4 Introduction To Simulation

4-5 Conclusion

Chapter 5: Normal, Binomial, Poisson, and Exponential Distributions

5-1 Introduction

5-2 The Normal Distribution

5-2a Continuous Distributions and Density Functions

5-2b The Normal Density

5-2c Standardizing: Z-Values

5-2d Normal Tables and Z-Values

5-2e Normal Calculations in Excel

5-2f Empirical Rules Revisited

5-2g Weighted Sums of Normal Random Variables

5-3 Applications Of The Normal Distribution

5-4 The Binomial Distribution

5-4a Mean and Standard Deviation of the Binomial Distribution

5-4b The Binomial Distribution in the Context of Sampling

5-4c The Normal Approximation to the Binomial

5-5 Applications Of The Binomial Distribution

5-6 The Poisson And Exponential Distributions

5-6a The Poisson Distribution

5-6b The Exponential Distribution

5-7 Conclusion

Chapter 6: Decision Making Under Uncertainty

6-1 Introduction

6-2 Elements Of Decision Analysis

6-2a Identifying the Problem

6-2b Possible Decisions

6-2c Possible Outcomes

6-2d Probabilities of Outcomes

6-2e Payoffs and Costs

6-2f Decision Criterion

6-2g More about the EMV Criterion

6-2h Decision Trees

6-3 One-stage Decision Problems

6-4 The PrecisionTree Add-in

6-5 Multistage Decision Problems

6-6 The Role Of Risk Aversion

6-6a Utility Functions

6-6b Exponential Utility

6-6c Certainty Equivalents

6-6d Is Expected Utility Maximization Used?

6-7 Conclusion

Part 3: Statistical Inference

Chapter 7: Sampling and Sampling Distributions

7-1 Introduction

7-2 Sampling Terminology

7-3 Methods For Selecting Random Samples

7-3a Simple Random Sampling

7-3b Systematic Sampling

7-3c Stratified Sampling

7-3d Cluster Sampling

7-3e Multistage Sampling Schemes

7-4 Introduction To Estimation

7-4a Sources of Estimation Error

7-4b Key Terms in Sampling

7-4c Sampling Distribution of the Sample Mean

7-4d The Central Limit Theorem

7-4e Sample Size Selection

7-4f Summary of Key Ideas for Simple Random Sampling

7-5 Conclusion

Chapter 8: Confidence Interval Estimation

8-1 Introduction

8-2 Sampling Distributions

8-2a The t Distribution

8-2b Other Sampling Distributions

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

8-7a Independent Samples

8-7b Paired Samples

8-8 Confidence Interval For The Difference Between Proportions

8-9 Sample Size Selection

8-9a Sample Size Selection for Estimation of the Mean

8-9b Sample Size Selection for Estimation of Other Parameters

8-10 Conclusion

Summary of Key Terms

Chapter 9: Hypothesis Testing

9-1 Introduction

9-2 Concepts In Hypothesis Testing

9-2a Null and Alternative Hypotheses

9-2b One-Tailed Versus Two-Tailed Tests

9-2c Types of Errors

9-2d Significance Level and Rejection Region

9-2e Significance from p-values

9-2f Type II Errors and Power

9-2g Hypothesis Tests and Confidence Intervals

9-2h Practical versus Statistical Significance

9-3 Hypothesis Tests For A Population Mean

9-4 Hypothesis Tests For Other Parameters

9-4a Hypothesis Tests for a Population Proportion

9-4b Hypothesis Tests for Differences between Population Means

9-4c Hypothesis Test for Equal Population Variances

9-4d Hypothesis Tests for Differences between Population Proportions

9-5 Tests For Normality

9-6 Chi-square Test For Independence

9-7 Conclusion

Part 4: Regression Analysis and Time Series Forecasting

Chapter 10: Regression Analysis: Estimating Relationships

10-1 Introduction

10-2 Scatterplots: Graphing Relationships

10-2a Linear versus Nonlinear Relationships

10-2b Outliers

10-2c Unequal Variance

10-2d No Relationship

10-3 Correlations: Indicators Of Linear Relationships

10-4 Simple Linear Regression

10-4a Least Squares Estimation

10-4b Standard Error of Estimate

10-4c The Percentage of Variation Explained: R-Square

10-5 Multiple Regression

10-5a Interpretation of Regression Coefficients

10-5b Interpretation of Standard Error of Estimate and R-Square

10-6 Modeling Possibilities

10-6a Dummy Variables

10-6b Interaction Variables

10-6c Nonlinear Transformations

10-7 Validation of the Fit

10-8 Conclusion

Chapter 11: Regression Analysis: Statistical Inference

11-1 Introduction

11-2 The Statistical Model

11-3 Inferences About The Regression Coefficients

11-3a Sampling Distribution of the Regression Coefficients

11-3b Hypothesis Tests for the Regression Coefficients and p-Values

11-3c A Test for the Overall Fit: The ANOVA Table

11-4 Multicollinearity

11-5 Include/Exclude Decisions

11-6 Stepwise Regression

11-7 Outliers

11-8 Violations Of Regression Assumptions

11-8a Nonconstant Error Variance

11-8b Nonnormality of Residuals

11-8c Autocorrelated Residuals

11-9 Prediction

11-10 Conclusion

Chapter 12: Time Series Analysis and Forecasting

12-1 Introduction

12-2 Forecasting Methods: An Overview

12-2a Extrapolation Models

12-2b Econometric Models

12-2c Combining Forecasts

12-2d Components of Time Series Data

12-2e Measures of Accuracy

12-3 Testing For Randomness

12-3a The Runs Test

12-3b Autocorrelation

12-4 Regression-based Trend Models

12-4a Linear Trend

12-4b Exponential Trend

12-5 The Random Walk Model

12-6 Moving Averages Forecasts

12-7 Exponential Smoothing Forecasts

12-7a Simple Exponential Smoothing

12-7b Holt’s Model for Trend

12-8 Seasonal Models

12-8a Winters’ Exponential Smoothing Model

12-8b Deseasonalizing: The Ratio-to-Moving-Averages Method

12-8c Estimating Seasonality with Regression

12-9 Conclusion

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

13-4a Solver’s Sensitivity Report

13-4b SolverTable Add-In

13-4c Comparison of Solver’s Sensitivity Report and SolverTable

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

Summary of Key Terms (Continued )

Chapter 14: Optimization Models

14-1 Introduction

14-2 Employee Scheduling Models

14-3 Blending Models

14-4 Logistics Models

14-4a Transportation Models

14-4b Other Logistics Models

14-5 Aggregate Planning Models

14-6 Financial Models

14-7 Integer Optimization Models

14-7a Capital Budgeting Models

14-7b Fixed-Cost Models

14-7c Set-Covering Models

14-8 Nonlinear Optimization Models

14-8a Basic Ideas of Nonlinear Optimization

14-8b Managerial Economics Models

14-8c Portfolio Optimization Models

14-9 Conclusion

Chapter 15: Introduction to Simulation Modeling

15-1 Introduction

15-2 Probability Distributions For Input Variables

15-2a Types of Probability Distributions

15-2b Common Probability Distributions

15-2c Using @RISK to Explore Probability Distributions

15-3 Simulation And The Flaw Of Averages

15-4 Simulation With Built-in Excel Tools

15-5 Introduction To @RISK

15-5a @RISK Features

15-5b Loading @RISK

15-5c @RISK Models with a Single Random Input Variable

15-5d Some Limitations of @RISK

15-5e @RISK Models with Several Random Input Variables

15-6 The Effects Of Input Distributions On Results

15-6a Effect of the Shape of the Input Distribution(s)

15-6b Effect of Correlated Input Variables

15-7 Conclusion

Chapter 16: Simulation Models

16-1 Introduction

16-2 Operations Models

16-2a Bidding for Contracts

16-2b Warranty Costs

16-2c Drug Production with Uncertain Yield

16-3 Financial Models

16-3a Financial Planning Models

16-3b Cash Balance Models

16-3c Investment Models

16-4 Marketing Models

16-4a Models of Customer Loyalty

16-4b Marketing and Sales Models

16-5 Simulating Games Of Chance

16-5a Simulating the Game of Craps

16-5b Simulating the NCAA Basketball Tournament

16-6 Conclusion

Part 6: Advanced Data Analysis

Chapter 17: Data Mining

17-1 Introduction

17-2 Data Exploration And Visualization

17-2a Introduction to Relational Databases

17-2b Online Analytical Processing (OLAP)

17-2c Power Pivot and Self-Service BI Tools in Excel

17-2d Visualization Software

17-3 Classification Methods

17-3a Logistic Regression

17-3b Neural Networks

17-3c Naïve Bayes

17-3d Classification Trees

17-3e Measures of Classification Accuracy

17-3f Classification with Rare Events

17-4 Clustering

17-5 Conclusion

Part 7: Bonus Online Material

Chapter 18: Importing Data into Excel

18-1 Introduction

18-2 Rearranging Excel Data

18-3 Importing Text Data

18-4 Importing Data Into Excel

18-4a Importing from Access with Old Tools

18-4b Importing from Access with Power Query

18.4c Using Microsoft Query

18.4d SQL Statements and M

18-4e Web Queries

18.5 Cleansing Data

18.6 Conclusion

Chapter 19: Analysis of Variance and Experimental Design

19-1 Introduction

19-2 One-way ANOVA

19-2a The Equal-Means Test

19-2b Confidence Intervals for Differences between Means

19-2c Using a Logarithmic Transformation

19-3 Using Regression To Perform ANOVA

19-4 The Multiple Comparison Problem

19-5 Two-way ANOVA

19-5a Confidence Intervals for Contrasts

19-5b Assumptions of Two-Way ANOVA

19-6 More About Experimental Design

19-6a Randomization

19-6b Blocking

19-6c Incomplete Designs

19-7 Conclusion

Chapter 20: Statistical Process Control

20-1 Introduction

20-2 Deming’s 14 Points

20-3 Introduction To Control Charts

20-4 Control Charts For Variables

20-4a Control Charts and Hypothesis Testing

20-4b Other Out-of-Control Indications

20-4c Rational Subsamples

20-4d Deming’s Funnel Experiment and Tampering

20-4e Control Charts in the Service Industry

20-5 Control Charts For Attributes

20-5a The p Chart

20-5b The Red Bead Experiment

20-6 Process Capability

20-6a Process Capability Indexes

20-6b More on Motorola and 6-sigma

20-7 Conclusion

Appendix A: Statistical Reporting

A-1 Introduction

A-2 Suggestions For Good Statistical Reporting

A-2a Planning

A-2b Developing a Report

A-2c Be Clear

A-2d Be Concise

A-2e Be Precise

A-3 Examples Of Statistical Reports

A-4 Conclusion

References

Index

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