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

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