This is completed downloadable of Essentials of Business Analytics 2nd Edition Camm Solutions Manual

Product Details:

ISBN-10 ‏ : ‎ 1305627733
ISBN-13 ‏ : ‎ 978-1305627734
Author: Jeffrey D. Camm (Author), James J. Cochran (Author), Michael J. Fry (Author), Jeffrey W. Ohlmann (Author), David R. Anderson (Author)

ESSENTIALS OF BUSINESS ANALYTICS, 2e provides students the opportunity to build valuable skills that are in high demand by today’s businesses. Excellent examples and visuals help illustrate data and results for each topic. Step-by-step instructions for various software programs help you perform the analyses discussed. You will find practical, relevant problems at a variety of difficulty levels to help you learn and succeed in your course.

Table of Content:

Chapter 1: Introduction
1.1 Decision Making
1.2 Business Analytics Defined
1.3 A Categorization of Analytical Methods and Models
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
1.4 Big Data
Volume
Velocity
Variety
Veracity
1.5 Business Analytics in Practice
Financial Analytics
Human Resource (HR) Analytics
Marketing Analytics
Health Care Analytics
Supply-Chain Analytics
Analytics for Government and Nonprofits
Sports Analytics
Web Analytics
Summary
Glossary
Chapter 2: Descriptive Statistics
Analytics in Action: U.S. Census Bureau
2.1 Overview of Using Data: Definitions and Goals
2.2 Types of Data
Population and Sample Data
Quantitative and Categorical Data
Cross-Sectional and Time Series Data
Sources of Data
2.3 Modifying Data in Excel
Sorting and Filtering Data in Excel
Conditional Formatting of Data in Excel
2.4 Creating Distributions from Data
Frequency Distributions for Categorical Data
Relative Frequency and Percent Frequency Distributions
Frequency Distributions for Quantitative Data
Histograms
Cumulative Distributions
2.5 Measures of Location
Mean (Arithmetic Mean)
Median
Mode
Geometric Mean
2.6 Measures of Variability
Range
Variance
Standard Deviation
Coefficient of Variation
2.7 Analyzing Distributions
Percentiles
Quartiles
z-Scores
Empirical Rule
Identifying Outliers
Box Plots
2.8 Measures of Association Between Two Variables
Scatter Charts
Covariance
Correlation Coefficient
Summary
Glossary
Problems
Case Problem: Heavenly Chocolates Web Site Transactions
Appendix 2.1 Creating Box Plots with XLMiner
Chapter 3: Data Visualization
Analytics in Action: Cincinnati Zoo & Botanical Garden
3.1 Overview of Data Visualization
Effective Design Techniques
3.2 Tables
Table Design Principles
Crosstabulation
PivotTables in Excel
Recommended PivotTables in Excel
3.3 Charts
Scatter Charts
Recommended Charts in Excel
Line Charts
Bar Charts and Column Charts
A Note on Pie Charts and Three-Dimensional Charts
Bubble Charts
Heat Maps
Additional Charts for Multiple Variables
PivotCharts in Excel
3.4 Advanced Data Visualization
Advanced Charts
Geographic Information Systems Charts
3.5 Data Dashboards
Principles of Effective Data Dashboards
Applications of Data Dashboards
Summary
Glossary
Problems
Case Problem: All-Time Movie Box-Office Data
Appendix 3.1 Creating a Scatter-Chart Matrix and a Parallel-Coordinates Plot with XLMiner
Chapter 4: Descriptive Data Mining
Analytics in Action: Advice from a Machine
4.1 Data Preparation
Treatment of Missing Data
Identification of Outliers and Erroneous Data
Variable Representation
4.2 Cluster Analysis
Measuring Similarity Between Observations
Hierarchical Clustering
k-Means Clustering
Hierarchical Clustering Versus k-Means Clustering
4.3 Association Rules
Evaluating Association Rules
Summary
Glossary
Problems
Case Problem: Know Thy Customer
Appendix 4.1 Hierarchical Clustering with XLMiner
Appendix 4.2 k-Means Clustering with XLMiner
Appendix 4.3 Association Rules with XLMiner
Chapter 5: Probability: An Introduction to Modeling Uncertainty
Analytics in Action: National Aeronautics and Space Administration
5.1 Events and Probabilities
5.2 Some Basic Relationships of Probability
Complement of an Event
Addition Law
5.3 Conditional Probability
Independent Events
Multiplication Law
Bayes’ Theorem
5.4 Random Variables
Discrete Random Variables
Continuous Random Variables
5.5 Discrete Probability Distributions
Custom Discrete Probability Distribution
Expected Value and Variance
Discrete Uniform Probability Distribution
Binomial Probability Distribution
Poisson Probability Distribution
5.6 Continuous Probability Distributions
Uniform Probability Distribution
Triangular Probability Distribution
Normal Probability Distribution
Exponential Probability Distribution
Summary
Glossary
Problems
Case Problem: Hamilton County Judges
Chapter 6: Statistical Inference
Analytics in Action: John Morrell & Company
6.1 Selecting a Sample
Sampling from a Finite Population
Sampling from an Infinite Population
6.2 Point Estimation
Practical Advice
6.3 Sampling Distributions
Sampling Distribution of x
Sampling Distribution of p
6.4 Interval Estimation
Interval Estimation of the Population Mean
Interval Estimation of the Population Proportion
6.5 Hypothesis Tests
Developing Null and Alternative Hypotheses
Type I and Type II Errors
Hypothesis Test of the Population Mean
Hypothesis Test of the Population Proportion
Big Data, Statistical Inference, and Practical Significance
Summary
Glossary
Problems
Case Problem 1: Young Professional Magazine
Case Problem 2: Quality Associates, Inc
Chapter 7: Linear Regression
Analytics in Action: Alliance Data Systems
7.1 Simple Linear Regression Model
Regression Model
Estimated Regression Equation
7.2 Least Squares Method
Least Squares Estimates of the Regression Parameters
Using Excel’s Chart Tools to Compute the Estimated Regression Equation
7.3 Assessing the Fit of the Simple Linear Regression Model
The Sums of Squares
The Coefficient of Determination
Using Excel’s Chart Tools to Compute the Coefficient of Determination
7.4 The Multiple Regression Model
Regression Model
Estimated Multiple Regression Equation
Least Squares Method and Multiple Regression
Butler Trucking Company and Multiple Regression
Using Excel’s Regression Tool to Develop the Estimated Multiple Regression Equation
7.5 Inference and Regression
Conditions Necessary for Valid Inference in the Least Squares Regression Model
Testing Individual Regression Parameters
Addressing Nonsignificant Independent Variables
Multicollinearity
Inference and Very Large Samples
7.6 Categorical Independent Variables
Butler Trucking Company and Rush Hour
Interpreting the Parameters
More Complex Categorical Variables
7.7 Modeling Nonlinear Relationships
Quadratic Regression Models
Piecewise Linear Regression Models
Interaction Between Independent Variables
7.8 Model Fitting
Variable Selection Procedures
Overfitting
Summary
Glossary
Problems
Case Problem: Alumni Giving
Appendix 7.1 Regression with XLMiner
Chapter 8: Time Series Analysis and Forecasting
Analytics in Action: ACCO Brands
8.1 Time Series Patterns
Horizontal Pattern
Trend Pattern
Seasonal Pattern
Trend and Seasonal Pattern
Cyclical Pattern
Identifying Time Series Patterns
8.2 Forecast Accuracy
8.3 Moving Averages and Exponential Smoothing
Moving Averages
Forecast Accuracy
Exponential Smoothing
Forecast Accuracy
8.4 Using Regression Analysis for Forecasting
Linear Trend Projection
Seasonality
Seasonality Without Trend
Seasonality with Trend
Using Regression Analysis as a Causal Forecasting Method
Combining Causal Variables with Trend and Seasonality Effects
Considerations in Using Regression in Forecasting
8.5 Determining the Best Forecasting Model to Use
Summary
Glossary
Problems
Case Problem: Forecasting Food and Beverage Sales
Appendix 8.1 Using Excel Forecast Sheet
Appendix 8.2 Forecasting with XLMiner
Chapter 9: Predictive Data Mining
Analytics in Action: Orbitz
9.1 Data Sampling
9.2 Data Partitioning
9.3 Accuracy Measures
Evaluating the Classification of Categorical Outcomes
Evaluating the Estimation of Continuous Outcomes
9.4 Logistic Regression
9.5 k-Nearest Neighbors
Classifying Categorical Outcomes with k-Nearest Neighbors
Estimating Continuous Outcomes with k-Nearest Neighbors
9.6 Classification and Regression Trees
Classifying Categorical Outcomes with a Classification Tree
Estimating Continuous Outcomes with a Regression Tree
Ensemble Methods
Summary
Glossary
Problems
Case Problem: Grey Code Corporation
Appendix 9.1 Data Partitioning with XLMiner
Appendix 9.2 Logistic Regression Classification with XLMiner
Appendix 9.3 k-Nearest Neighbor Classification and Estimation with XLMiner
Appendix 9.4 Single Classification and Regression Trees with XLMiner
Appendix 9.5 Random Forests of Classification or Regression Trees with XLMiner
Chapter 10: Spreadsheet Models
Analytics in Action: Procter & Gamble
10.1 Building Good Spreadsheet Models
Influence Diagrams
Building a Mathematical Model
Spreadsheet Design and Implementing the Model in a Spreadsheet
10.2 What-If Analysis
Data Tables
Goal Seek
10.3 Some Useful Excel Functions for Modeling
SUM and SUMPRODUCT
IF and COUNTIF
VLOOKUP
10.4 Auditing Spreadsheet Models
Trace Precedents and Dependents
Show Formulas
Evaluate Formulas
Error Checking
Watch Window
Summary
Glossary
Problems
Case Problem: Retirement Plan
Chapter 11: Linear Optimization Models
Analytics in Action: MeadWestvaco Corporation
11.1 A Simple Maximization Problem
Problem Formulation
Mathematical Model for the Par, Inc. Problem
11.2 Solving the Par, Inc. Problem
The Geometry of the Par, Inc. Problem
Solving Linear Programs with Excel Solver
11.3 A Simple Minimization Problem
Problem Formulation
Solution for the M&D Chemicals Problem
11.4 Special Cases of Linear Program Outcomes
Alternative Optimal Solutions
Infeasibility
Unbounded
11.5 Sensitivity Analysis
Interpreting Excel Solver Sensitivity Report
11.6 General Linear Programming Notation and More Examples
Investment Portfolio Selection
Transportation Planning
Advertising Campaign Planning
11.7 Generating an Alternative Optimal Solution for a Linear Program
Summary
Glossary
Problems
Case Problem: Investment Strategy
Appendix 11.1 Solving Linear Optimization Models Using Analytic Solver Platform
Chapter 12: Integer Linear Optimization Models
Analytics in Action: Petrobras
12.1 Types of Integer Linear Optimization Models
12.2 Eastborne Realty, An Example of Integer Optimization
The Geometry of Linear All-Integer Optimization
12.3 Solving Integer Optimization Problems with Excel Solver
A Cautionary Note About Sensitivity Analysis
12.4 Applications Involving Binary Variables
Capital Budgeting
Fixed Cost
Bank Location
Product Design and Market Share Optimization
12.5 Modeling Flexibility Provided by Binary Variables
Multiple-Choice and Mutually Exclusive Constraints
k Out of n Alternatives Constraint
Conditional and Corequisite Constraints
12.6 Generating Alternatives in Binary Optimization
Summary
Glossary
Problems
Case Problem: Applecore Children’s Clothing
Appendix 12.1 Solving Integer Linear Optimization Problems Using Analytic Solver Platform
Chapter 13 Nonlinear Optimization Models
Analytics in Action: Intercontinental Hotels
13.1 A Production Application: Par, Inc. Revisited
An Unconstrained Problem
A Constrained Problem
Solving Nonlinear Optimization Models Using Excel Solver
Sensitivity Analysis and Shadow Prices in Nonlinear Models
13.2 Local and Global Optima
Overcoming Local Optima with Excel Solver
13.3 A Location Problem
13.4 Markowitz Portfolio Model
13.5 Forecasting Adoption of a New Product
Summary
Glossary
Problems
Case Problem: Portfolio Optimization with Transaction Costs
Appendix 13.1 Solving Nonlinear Optimization Problems with Analytic Solver Platform
Chapter 14: Monte Carlo Simulation
Analytics in Action: Cook County Hospital ICU
14.1 Risk Analysis for Sanotronics LLC
Base-Case Scenario
Worst-Case Scenario
Best-Case Scenario
Sanotronics Spreadsheet Model
Use of Probability Distributions to Represent Random Variables
Generating Values for Random Variables with Excel
Executing Simulation Trials with Excel
Measuring and Analyzing Simulation Output
14.2 Simulation Modeling for Land Shark Inc
Spreadsheet Model for Land Shark
Generating Values for Land Shark’s Random Variables
Executing Simulation Trials and Analyzing Output
14.3 Simulation Considerations
Verification and Validation
Advantages and Disadvantages of Using Simulation
Summary
Glossary
Problems
Case Problem: Four Corners
Appendix 14.1 Land Shark Inc. Simulation with Analytic Solver Platform
Appendix 14.2 Distribution Fitting with Analytic Solver Platform
Appendix 14.3 Simulation Optimization with Analytic Solver Platform
Appendix 14.4 Correlating Random Variables with Analytic Solver Platform
Appendix 14.5 Probability Distributions for Random Variables
Chapter 15: Decision Analysis
Analytics in Action: Phytopharm
15.1 Problem Formulation
Payoff Tables
Decision Trees
15.2 Decision Analysis Without Probabilities
Optimistic Approach
Conservative Approach
Minimax Regret Approach
15.3 Decision Analysis with Probabilities
Expected Value Approach
Risk Analysis
Sensitivity Analysis
15.4 Decision Analysis with Sample Information
Expected Value of Sample Information
Expected Value of Perfect Information
15.5 Computing Branch Probabilities with Bayes’ Theorem
15.6 Utility Theory
Utility and Decision Analysis
Utility Functions
Exponential Utility Function
Summary
Glossary
Problems
Case Problem: Property Purchase Strategy
Appendix 15.1 Using Analytic Solver Platform to Create Decision Trees
APPENDIX A: Basics of Excel
APPENDIX B: Database Basics with Microsoft Access
References
Index