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Business Analytics Data Analysis and Decision Making 6th Edition Albright Solutions Manual

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ISBN-10 ‏: ‎1305947541
ISBN-13 ‏: ‎978-1305947542
Author: S. Christian Albright

Become a master of data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 6E! This popular quantitative methods text helps you maximize your success with its proven teach-by-example approach, student-friendly writing style, and complete Excel 2016 integration. (It is also compatible with Excel 2013, 2010, and 2007.) The text devotes three online chapters to advanced statistical analysis. Chapters on data mining and importing data into Excel emphasize tools commonly used under the Business Analytics umbrella — including Microsoft Excel’s “Power BI” suite. Up-to-date problem sets and cases demonstrate how chapter concepts relate to real-world practice.

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Table Of Content:

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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Business Analytics Data Analysis and Decision Making 6th Edition Albright Solutions Manual

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