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# Test bank for Business Analytics Data Analysis and Decision Making 5th Edition by S. Christian Albright

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### Test bank for Business Analytics Data Analysis and Decision Making 5th Edition by S. Christian Albright

#### Test bank for Business Analytics Data Analysis and Decision Making 5th Edition by S. Christian Albright

Become a master of data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 5E! This quantitative methods text provides users with the tools to succeed with a teach-by-example approach, student-friendly writing style, and complete Excel 2013 integration. It is also compatible with Excel 2010 and 2007. Problem sets and cases provide realistic examples to show the relevance of the material. The Companion Website includes: the Palisade DecisionTools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); SolverTable, which allows you to do sensitivity analysis; data and solutions files, PowerPoint slides, and tutorial videos.

Brief Contents
Contents
Preface
Ch 1: Introduction to Data Analysis and Decision Making
1-1: Introduction
1-2: Overview of the Book
1-3: Modeling and Models
1-4: Conclusion
Hottest New Jobs: Statistics and Mathematics
Part 1: Exploring Data
Ch 2: Describing the Distribution of a Single Variable
2-1: Introduction
2-2: Basic Concepts
2-3: Descriptive Measures for Categorical Variables
2-4: Descriptive Measures for Numerical Variables
2-5: Time Series Data
2-6: Outliers and Missing Values
2-7: Excel Tables for Filtering, Sorting, and Summarizing
2-8: Conclusion
Ch 3: Finding Relationships among Variables
3-1: Introduction
3-2: Relationships among Categorical Variables
3-3: Relationships among Categorical Variables and a Numerical Variable
3-4: Relationships among Numerical Variables
3-5: Pivot Tables
3-6: Conclusion
Part 2: Probability and Decision Making under Uncertainty
Ch 4: Probability and Probability Distributions
4-1: Introduction
4-2: Probability Essentials
4-3: Probability Distribution of a Single Random Variable
4-4: Introduction to Simulation
4-5: Conclusion
Ch 5: Normal, Binomial, Poisson, and Exponential Distributions
5-1: Introduction
5-2: The Normal Distribution
5-3: Applications of the Normal Distribution
5-4: The Binomial Distribution
5-5: Applications of the Binomial Distribution
5-6: The Poisson and Exponential Distributions
5-7: Conclusion
Ch 6: Decision Making under Uncertainty
6-1: Introduction
6-2: Elements of Decision Analysis
6-4: Bayes’ Rule
6-5: Multistage Decision Problems and the Value of Information
6-6: Risk Aversion and Expected Utility
6-7: Conclusion
Part 3: Statistical Inference
Ch 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
Ch 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
Ch 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
Part 4: Regression Analysis and Time Series Forecasting
Ch 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
Ch 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
Ch 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
Part 5: Optimization and Simulation Modeling
Ch 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
Ch 14: Optimization Models
14-1: Introduction
14-2: Worker 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
Ch 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: Introduction to the @RISK
15-6: The Effects of Input Distributions on Results
15-7: Conclusion
Ch 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: An Automated Template for @RISK Models
16-7: Conclusion
Ch 17: Data Mining
17-1: Introduction
17-2: Data Exploration and Visualization
17-3: Microsoft Data Mining Add-Ins for Excel
17-4: Classification Methods
17-5: Clustering
17-6: Conclusion
Part 7: Bonus Online Material
Ch 18: Importing Data into Excel
18-1: Introduction
18-2: Rearranging Excel Data
18-3: Importing Text Data
18-4: Importing Relational Database Data
18-5: Web Queries
18-6: Cleansing Data
18-7: Conclusion
Ch 19: Analysis of Variance and Experimental Design
19-1: Introduction
19-2: One-Way ANOVA
19-3: Using Regression to Perform ANOVA
19-4: The Multiple Comparison Problem
19-5: Two-Way ANOVA
19-7: Conclusion
Ch 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
20-5: Control Charts for Attributes
20-6: Process Capability
20-7: Conclusion
Appendix A: Statistical Reporting
A-1: Introduction
A-2: Suggestions for Good Statistical Reporting
A-3: Examples of Statistical Reports
A-4: Conclusion
References
Index

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