Business Analytics: Communicating with Numbers
- Length: 688 pages
- Edition: 1
- Language: English
- Publisher: McGraw Hill
- Publication Date: 2020-01-13
- ISBN-10: 1260785009
- ISBN-13: 9781260785005
- Sales Rank: #379231 (See Top 100 Books)
Business Analytics: Communicating with Numbers was written from the ground up to prepare students to understand, manage, and visualize the data, apply the appropriate tools, and communicate the findings and their relevance. Unlike other texts that simply repackage statistics and traditional operations research topics, this text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. It provides a holistic analytics process, including dealing with real life data that are not necessarily ‘clean’ and/or ‘small’ and stresses the importance of effectively communicating findings by including features such as a synopsis (a short writing sample) and a sample report (a longer writing sample) in every chapter. These features help students develop skills in articulating the business value of analytics by communicating insights gained from a non-technical standpoint.
Cover Title Copyright Dedication About the Authors From the Authors Business Analytics: Communicating with Numbers Resources for instructors and Students Acknowledgments Brief Contents Contents Chapter 1: Introduction to Business Analytics 1.1 Overview of Business Analytics Important Business Analytics Applications 1.2 Types of Data Sample and Population Data Cross-Sectional and Time Series Data Structured and Unstructured Data Big Data 1.3 Variables and Scales of Measurement The Measurement Scales 1.4 Data Sources and File Formats Fixed-Width Format Delimited Format eXtensible Markup Language HyperText Markup Language JavaScript Object Notation 1.5 Writing With Big Data Chapter 2: Data Management and Wrangling 2.1 Data Management Data Modeling: The Entity-Relationship Diagram Data Retrieval in the Database Environment Data Warehouse and Data Mart 2.2 Data Inspection 2.3 Data Preparation Handling Missing Values Subsetting 2.4 Transforming Numerical Data Binning Mathematical Transformations 2.5 Transforming Categorical Data Category Reduction Dummy Variables Category Scores 2.6 Writing With Big Data Chapter 3: Data Visualization and Summary Measures 3.1 Methods to Visualize Categorical and Numerical Variables Methods to Visualize a Categorical Variable Methods to Visualize a Numerical Variable Cautionary Comments When Constructing or Interpreting Charts or Graphs 3.2 Methods to Visualize the Relationship Between Two Variables Methods to Visualize the Relationship between Two Categorical Variables A Method to Visualize the Relationship between Two Numerical Variables 3.3 Other Data Visualization Methods A Scatterplot with a Categorical Variable A Bubble Plot A Line Chart A Heat Map Options for Advanced Visualizations 3.4 Summary Measures Measures of Central Location Measures of Dispersion Measures of Shape Measures of Association 3.5 Detecting Outliers A Boxplot z-Scores 3.6 Writing With Big Data Chapter 4: Probability and Probability Distributions 4.1 Probability Concepts and Probability Rules Events Assigning Probabilities Rules of Probability 4.2 The Total Probability Rule and Bayes’ Theorem The Total Probability Rule and Bayes’ Theorem Extensions of the Total Probability Rule and Bayes’ Theorem 4.3 Random Variables and Discrete Probability Distributions The Discrete Probability Distribution Summary Measures of a Discrete Random Variable 4.4 The Binomial and the Poisson Distributions The Binomial Distribution The Poisson Distribution Using Excel and R to Obtain Binomial and Poisson Probabilities 4.5 The Normal Distribution The Standard Normal Distribution The Transformation of Normal Random Variables Using Excel and R for the Normal Distribution 4.6 Writing With Data Chapter 5: Statistical Inference 5.1 Sampling Distributions The Sampling Distribution of the Sample Mean The Sampling Distribution of the Sample Proportion 5.2 Estimation Confidence Interval for the Population Mean μ Using Excel and R to Construct a Confidence Interval for μ Confidence Interval for the Population Proportion p 5.3 Hypothesis Testing Hypothesis Test for the Population Mean μ Using Excel and R to Test μ Hypothesis Test for the Population Proportion p 5.4 Writing With Data Chapter 6: Regression Analysis 6.1 The Linear Regression Model The Components of the Linear Regression Model Estimating a Linear Regression Model with Excel or R Categorical Variables with Multiple Categories 6.2 Model Selection The Standard Error of the Estimate, se The Coefficient of Determination, R2 The Adjusted R2 One Last Note on Goodness-of-Fit Measures 6.3 Tests of Significance Test of Joint Significance Test of Individual Significance A Test for a Nonzero Slope Coefficient Reporting Regression Results 6.4 Model Assumptions and Common Violations Required Assumptions of Regression Analysis Common Violation 1: Nonlinear Patterns Common Violation 2: Multicollinearity Common Violation 3: Changing Variability Common Violation 4: Correlated Observations Common Violation 5: Excluded Variables Summary Using Excel and R to Construct Residual Plots 6.5 Writing With Big Data Chapter 7: Advanced Regression Analysis 7.1 Regression Models with Interaction Variables The Interaction of Two Dummy Variables The Interaction of a Dummy Variable and a Numerical Variable The Interaction of Two Numerical Variables 7.2 Regression Models for Nonlinear Relationships The Quadratic Regression Model Regression Models with Logarithms The Log-Log Regression Model The Logarithmic Regression Model The Exponential Regression Model Comparing Linear and Log-Transformed Regression Models Using Excel and R to Compare Linear and Log-Transformed Regression Models 7.3 Linear Probability and Logistic Regression Models The Linear Probability Model The Logistic Regression Model Accuracy of Binary Choice Models Using Analytic Solver and R to Estimate the Logistic Regression Model 7.4 Cross-Validation Methods The Holdout Method Using Analytic Solver and R for the Holdout Method for the Logistic Regression Model The k-Fold Cross-Validation Method 7.5 Writing With Big Data APPENDIX 7.1: The Caret Package in R for the k-fold Cross-Validation Method Chapter 8: Introduction to Data Mining 8.1 Data Mining Overview The Data Mining Process Supervised and Unsupervised Data Mining 8.2 Similarity Measures Similarity Measures for Numerical Data Similarity Measures for Categorical Data 8.3 Performance Evaluation Data Partitioning Oversampling Performance Evaluation in Supervised Data Mining Performance Evaluation for Classification Models Using Excel to Obtain the Confusion Matrix and Performance Measures Selecting Cut-off Values Performance Charts for Classification Using Excel to Obtain Performance Charts for Classification Performance Evaluation for Prediction Using Excel to Obtain Performance Measures for Prediction 8.4 Principal Component Analysis Using Analytic Solver and R to Perform Principal Component Analysis 8.5 Writing With Big Data Chapter 9: Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes 9.1 Introduction to Supervised Data Mining Comparison of Supervised Data Mining Techniques 9.2 The k-Nearest Neighbors Method 9.3 The Naïve Bayes Method Transforming Numerical into Categorical Values 9.4 Writing With Big Data Chapter 10: Supervised Data Mining: Decision Trees 10.1 Introduction to Classification and Regression Trees (CART) Classification and Regression Trees (CART) 10.2 Classification Trees Using Analytic Solver and R to Develop a Classification Tree 10.3 Regression Trees Using Analytic Solver and R to Develop a Prediction Tree 10.4 Ensemble Tree Models Using Analytic Solver and R to Develop Ensemble Classification Tree Models 10.5 Writing With Big Data Chapter 11: Unsupervised Data Mining 11.1 Hierarchical Cluster Analysis Hierarchical Cluster Analysis Agglomerative Clustering with Numerical or Categorical Variables Using Analytic Solver and R to Perform Agglomerative Clustering Agglomerative Clustering with Mixed Data 11.2 k-Means Cluster Analysis Using Analytic Solver and R to Perform k-Means Clustering 11.3 Association Rule Analysis Using Analytic Solver and R to Perform Association Rule Analysis 11.4 Writing With Big Data Chapter 12: Forecasting with Time Series Data 12.1 The Forecasting Process for Time Series Forecasting Methods Model Selection Criteria 12.2 Simple Smoothing Techniques The Moving Average Technique The Simple Exponential Smoothing Technique Using Excel for Moving Averages and Exponential Smoothing 12.3 Linear Regression Models for Trend and Seasonality The Linear Trend Model The Linear Trend Model with Seasonality A Note on Causal Models for Forecasting 12.4 Nonlinear Regression Models for Trend and Seasonality The Exponential Trend Model The Polynomial Trend Model The Nonlinear Trend Models with Seasonality 12.5 Data Partitioning and Model Selection Cross-validation of Regression Models with R 12.6 Advanced Exponential Smoothing Methods The Holt Exponential Smoothing Method The Holt-Winters Exponential Smoothing Method 12.7 Writing With Data Chapter 13: Introduction to Prescriptive Analytics 13.1 Overview of Prescriptive Analytics 13.2 Monte Carlo Simulation Modeling Risk and Uncertainty Using Excel and R to Generate Random Observations from a Discrete Probability Distribution Using Excel and R to Generate Random Observations from a Continuous Probability Distribution Formulating and Developing a Monte Carlo Simulation 13.3 Optimization with Linear Programming Formulating a Linear Programming Model Solving a Linear Programming Problem 13.4 Optimization with Integer Programming Capital Budgeting Transportation Problem 13.5 Writing With Data Appendix A Big Data Sets: Variable Description and Data Dictionary Appendix B Getting Started with Excel and Excel Add-Ins Appendix C Getting Started with R Appendix D Statistical Tables Appendix E Answers to Selected Exercises Index
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