Essentials of business analytics
Jeffrey D. Camm
Bok · Engelsk · 2017
| Omfang | xxv, 870 sider : illustrasjoner
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| Utgave | 2nd ed.
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| Opplysninger | Machine generated contents note: ch. 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 -- ch. 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 -- ch. 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 -- ch. 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 -- ch. 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 -- ch. 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. -- ch. 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 -- ch. 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 -- ch. 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 -- ch. 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 -- ch. 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 --. - Note continued: 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 -- ch. 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 -- ch. 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 -- ch. 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 -- ch. 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.
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| Emner | |
| Dewey | |
| ISBN | 9781305627734
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