Data Science for Business and Decision Making
- Length: 1244 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2019-05-06
- ISBN-10: 0128112166
- ISBN-13: 0000128112166
- Sales Rank: #2745053 (See Top 100 Books)
Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software(R), and IBM SPSS Statistics Software(R).
Cover Data Science for Business and Decision Making Copyright Dedication Epigraph 1 Introduction to Data Analysis and Decision Making Introduction: Hierarchy Between Data, Information, and Knowledge Overview of the Book Final Remarks 2 Types of Variables and Measurement and Accuracy Scales Introduction Types of Variables Nonmetric or Qualitative Variables Metric or Quantitative Variables Types of Variables x Scales of Measurement Nonmetric Variables-Nominal Scale Nonmetric Variables-Ordinal Scale Quantitative Variable-Interval Scale Quantitative Variable-Ratio Scale Types of Variables x Number of Categories and Scales of Accuracy Dichotomous or Binary Variable (Dummy) Polychotomous Variable Discrete Quantitative Variable Continuous Quantitative Variable Final Remarks Exercises Part II: Descriptive Statistics 3 Univariate Descriptive Statistics Introduction Frequency Distribution Table Frequency Distribution Table for Qualitative Variables Frequency Distribution Table for Discrete Data Frequency Distribution Table for Continuous Data Grouped into Classes Graphical Representation of the Results Graphical Representation for Qualitative Variables Bar Chart Pie Chart Pareto Chart Graphical Representation for Quantitative Variables Line Graph Scatter Plot Histogram Stem-and-Leaf Plot Boxplot or Box-and-Whisker Diagram The Most Common Summary-Measures in Univariate Descriptive Statistics Measures of Position or Location Measures of Central Tendency Arithmetic Mean Case 1: Simple Arithmetic Mean of Ungrouped Discrete and Continuous Data Case 2: Weighted Arithmetic Mean of Ungrouped Discrete and Continuous Data Case 3: Arithmetic Mean of Grouped Discrete Data Case 4: Arithmetic Mean of Continuous Data Grouped into Classes Median Case 1: Median of Ungrouped Discrete and Continuous Data Case 2: Median of Grouped Discrete Data Case 3: Median of Continuous Data Grouped into Classes Mode Case 1: Mode of Ungrouped Data Case 2: Mode of Grouped Qualitative or Discrete Data Case 3: Mode of Continuous Data Grouped into Classes Quantiles Quartiles Deciles Percentiles Case 1: Quartiles, Deciles, and Percentiles of Ungrouped Discrete and Continuous Data Case 2: Quartiles, Deciles, and Percentiles of Grouped Discrete Data Case 3: Quartiles, Deciles, and Percentiles of Continuous Data Grouped into Classes Identifying the Existence of Univariate Outliers Measures of Dispersion or Variability Range Average Deviation Case 1: Average Deviation of Ungrouped Discrete and Continuous Data Case 2: Average Deviation of Grouped Discrete Data Case 3: Average Deviation of Continuous Data Grouped into Classes Variance Case 1: Variance of Ungrouped Discrete and Continuous Data Case 2: Variance of Grouped Discrete Data Case 3: Variance of Continuous Data Grouped into Classes Standard Deviation Standard Error Coefficient of Variation Measures of Shape Measures of Skewness Pearsons First Coefficient of Skewness Pearsons Second Coefficient of Skewness Bowleys Coefficient of Skewness Fishers Coefficient of Skewness Coefficient of Skewness on Stata Measures of Kurtosis Coefficient of Kurtosis Fishers Coefficient of Kurtosis Coefficient of Kurtosis on Stata A Practical Example in Excel A Practical Example on SPSS Frequencies Option Descriptives Option Explore Option A Practical Example on Stata Univariate Frequency Distribution Tables on Stata Summary of Univariate Descriptive Statistics on Stata Calculating Percentiles on Stata Charts on Stata: Histograms, Stem-and-Leaf, and Boxplots Histogram Stem-and-Leaf Boxplot Final Remarks Exercises 4 Bivariate Descriptive Statistics Introduction Association Between Two Qualitative Variables Joint Frequency Distribution Tables Measures of Association Chi-Square Statistic Other Measures of Association Based on Chi-Square Spearmans Coefficient Correlation Between Two Quantitative Variables Joint Frequency Distribution Tables Graphical Representation Through a Scatter Plot Measures of Correlation Covariance Pearsons Correlation Coefficient Final Remarks Exercises Part III: Probabilistic Statistics 5 Introduction to Probability Introduction Terminology and Concepts Random Experiment Sample Space Events Unions, Intersections, and Complements Independent Events Mutually Exclusive Events Definition of Probability Basic Probability Rules Probability Variation Field Probability of the Sample Space Probability of an Empty Set Probability Addition Rule Probability of a Complementary Event Probability Multiplication Rule for Independent Events Conditional Probability Probability Multiplication Rule Bayes´ Theorem Combinatorial Analysis Arrangements Combinations Permutations Final Remarks Exercises 6 Random Variables and Probability Distributions Introduction Random Variables Discrete Random Variable Expected Value of a Discrete Random Variable Variance of a Discrete Random Variable Cumulative Distribution Function of a Discrete Random Variable Continuous Random Variable Expected Value of a Continuous Random Variable Variance of a Continuous Random Variable Cumulative Distribution Function of a Continuous Random Variable Probability Distributions for Discrete Random Variables Discrete Uniform Distribution Bernoulli Distribution Binomial Distribution Relationship Between the Binomial and the Bernoulli Distributions Geometric Distribution Negative Binomial Distribution Relationship Between the Negative Binomial and the Binomial Distributions Relationship Between the Negative Binomial and the Geometric Distributions Hypergeometric Distribution Approximation of the Hypergeometric Distribution by the Binomial Poisson Distribution Approximation of the Binomial by the Poisson Distribution Probability Distributions for Continuous Random Variables Uniform Distribution Normal Distribution Approximation of the Binomial by the Normal Distribution Approximation of the Poisson by the Normal Distribution Exponential Distribution Relationship Between the Poisson and the Exponential Distribution Gamma Distribution Special Cases of the Gamma Distribution Relationship Between the Poisson and the Gamma Distribution Chi-Square Distribution Students t Distribution Snedecors F Distribution Relationship Between Students t and Snedecors F Distribution Final Remarks Exercises Part IV: Statistical Inference 7 Sampling Introduction Probability or Random Sampling Simple Random Sampling Simple Random Sampling Without Replacement Simple Random Sampling With Replacement Systematic Sampling Stratified Sampling Cluster Sampling Nonprobability or Nonrandom Sampling Convenience Sampling Judgmental or Purposive Sampling Quota Sampling Geometric Propagation or Snowball Sampling Sample Size Size of a Simple Random Sample Sample Size to Estimate the Mean of an Infinite Population Sample Size to Estimate the Mean of a Finite Population Sample Size to Estimate the Proportion of an Infinite Population Sample Size to Estimate the Proportion of a Finite Population Size of the Systematic Sample Size of the Stratified Sample Sample Size to Estimate the Mean of an Infinite Population Sample Size to Estimate the Mean of a Finite Population Sample Size to Estimate the Proportion of an Infinite Population Sample Size to Estimate the Proportion of a Finite Population Size of a Cluster Sample Size of a One-Stage Cluster Sample Sample Size to Estimate the Mean of an Infinite Population Sample Size to Estimate the Mean of a Finite Population Sample Size to Estimate the Proportion of an Infinite Population Sample Size to Estimate the Proportion of a Finite Population Size of a Two-Stage Cluster Sample Final Remarks Exercises 8 Estimation Introduction Point and Interval Estimation Point Estimation Interval Estimation Point Estimation Methods Method of Moments Ordinary Least Squares Maximum Likelihood Estimation Interval Estimation or Confidence Intervals Confidence Interval for the Population Mean (μ) Known Population Variance (σ2) Unknown Population Variance (σ2) Confidence Interval for Proportions Confidence Interval for the Population Variance Final Remarks Exercises 9 Hypotheses Tests Introduction Parametric Tests Univariate Tests for Normality Kolmogorov-Smirnov Test Shapiro-Wilk Test Shapiro-Francia Test Solving Tests for Normality by Using SPSS Software Solving Tests for Normality by Using Stata Kolmogorov-Smirnov Test on the Stata Software Shapiro-Wilk Test on the Stata Software Shapiro-Francia Test on the Stata Software Tests for the Homogeneity of Variances Bartletts χ2 Test Cochrans C Test Hartleys Fmax Test Levenes F-Test Solving Levenes Test by Using SPSS Software Solving Levenes Test by Using the Stata Software Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample Z Test When the Population Standard Deviation (σ) Is Known and the Distribution Is Normal Students t-Test When the Population Standard Deviation (σ) Is Not Known Solving Students t-Test for a Single Sample by Using SPSS Software Solving Students t-Test for a Single Sample by Using Stata Software Students t-Test to Compare Two Population Means From Two Independent Random Samples Case 1: σ12σ22 Case 2: σ12=σ22 Solving Students t-Test From Two Independent Samples by Using SPSS Software Solving Students t-Test From Two Independent Samples by Using Stata Software Students t-Test to Compare Two Population Means From Two Paired Random Samples Solving Students t-Test From Two Paired Samples by Using SPSS Software Solving Students t-Test From Two Paired Samples by Using Stata Software ANOVA to Compare the Means of More Than Two Populations One-Way ANOVA Solving the One-Way ANOVA Test by Using SPSS Software Solving the One-Way ANOVA Test by Using Stata Software Factorial ANOVA Two-Way ANOVA Solving the Two-Way ANOVA Test by Using SPSS Software Solving the Two-Way ANOVA Test by Using Stata Software ANOVA With More Than Two Factors Final Remarks Exercises 10 Nonparametric Tests Introduction Tests for One Sample Binomial Test Solving the Binomial Test Using SPSS Software Solving the Binomial Test Using Stata Software Chi-Square Test (χ2) for One Sample Solving the χ2 Test for One Sample Using SPSS Software Solving the χ2 Test for One Sample Using Stata Software Sign Test for One Sample Solving the Sign Test for One Sample Using SPSS Software Solving the Sign Test for One Sample Using Stata Software Tests for Two Paired Samples McNemar Test Solving the McNemar Test Using SPSS Software Solving the McNemar Test Using Stata Software Sign Test for Two Paired Samples Solving the Sign Test for Two Paired Samples Using SPSS Software Solving the Sign Test for Two Paired Samples Using Stata Software Wilcoxon Test Solving the Wilcoxon Test Using SPSS Software Solving the Wilcoxon Test Using Stata Software Tests for Two Independent Samples Chi-Square Test (χ2) for Two Independent Samples Solving the χ2 Statistic Using SPSS Software Solving the χ2 Statistic by Using Stata Software Mann-Whitney U Test Solving the Mann-Whitney Test Using SPSS Software Solving the Mann-Whitney Test Using Stata Software Tests for k Paired Samples Cochrans Q Test Solving Cochrans Q Test by Using SPSS Software Solution of Cochrans Q Test on Stata Software Friedmans Test Solving Friedmans Test by Using SPSS Software Solving Friedmans Test by Using Stata Software Tests for k Independent Samples The χ2 Test for k Independent Samples Solving the χ2 Test for k Independent Samples on SPSS Solving the χ2 Test for k Independent Samples on Stata Kruskal-Wallis Test Solving the Kruskal-Wallis Test by Using SPSS Software Solving the Kruskal-Wallis Test by Using Stata Final Remarks Exercises Part V: Multivariate Exploratory Data Analysis 11 Cluster Analysis Introduction Cluster Analysis Defining Distance or Similarity Measures in Cluster Analysis Distance (Dissimilarity) Measures Between Observations for Metric Variables Similarity Measures Between Observations for Binary Variables Agglomeration Schedules in Cluster Analysis Hierarchical Agglomeration Schedules Notation A Practical Example of Cluster Analysis With Hierarchical Agglomeration Schedules Nearest-Neighbor or Single-Linkage Method Furthest-Neighbor or Complete-Linkage Method Between-Groups or Average-Linkage Method Nonhierarchical K-Means Agglomeration Schedule Notation A Practical Example of a Cluster Analysis With the Nonhierarchical K-Means Agglomeration Schedule Cluster Analysis with Hierarchical and Nonhierarchical Agglomeration Schedules in SPSS Elaborating Hierarchical Agglomeration Schedules in SPSS Elaborating Nonhierarchical K-Means Agglomeration Schedules in SPSS Cluster Analysis With Hierarchical and Nonhierarchical Agglomeration Schedules in Stata Elaborating Hierarchical Agglomeration Schedules in Stata Elaborating Nonhierarchical K-Means Agglomeration Schedules in Stata Final Remarks Exercises Appendix Detecting Multivariate Outliers 12 Principal Component Factor Analysis Introduction Principal Component Factor Analysis Pearsons Linear Correlation and the Concept of Factor Overall Adequacy of the Factor Analysis: Kaiser-Meyer-Olkin Statistic and Bartletts Test of Sphericity Defining the Principal Component Factors: Determining the Eigenvalues and Eigenvectors of Correlation Matrix ρ and Calcula ... Factor Loadings and Communalities Factor Rotation A Practical Example of the Principal Component Factor Analysis Principal Component Factor Analysis in SPSS Principal Component Factor Analysis in Stata Final Remarks Exercises Appendix: Cronbachs Alpha Brief Presentation Determining Cronbachs Alpha Algebraically Determining Cronbachs Alpha in SPSS Determining Cronbachs Alpha in Stata Part VI: Generalized Linear Models 13 Simple and Multiple Regression Models Introduction Linear Regression Models Estimation of the Linear Regression Model by Ordinary Least Squares Explanatory Power of the Regression Model: Coefficient of Determination R2 General Statistical Significance of the Regression Model and Each of Its Parameters Construction of the Confidence Intervals of the Model Parameters and Elaboration of Predictions Estimation of Multiple Linear Regression Models Dummy Variables in Regression Models Presuppositions of Regression Models Estimated by OLS Normality of Residuals The Multicollinearity Problem Causes of Multicollinearity Consequences of Multicollinearity Application of Multicollinearity Examples in Excel Multicollinearity Diagnostics Possible Solutions for the Multicollinearity Problem The Problem of Heteroskedasticity Causes of Heteroskedasticity Consequences of Heteroskedasticity Heteroskedasticity Diagnostics: Breusch-Pagan/Cook-Weisberg Test Weighted Least Squares Method: A Possible Solution Huber-White Method for Robust Standard Errors The Autocorrelation of Residuals Problem Causes of the Autocorrelation of Residuals Consequences of the Autocorrelation of Residuals Autocorrelation of Residuals Diagnostic: The Durbin-Watson Test Autocorrelation of Residuals Diagnostic: The Breusch-Godfrey Test Possible Solutions for the Autocorrelation of Residuals Problem Detection of Specification Problems: Linktest and RESET Test Nonlinear Regression Models The Box-Cox Transformation: The General Regression Model Estimation of Regression Models in Stata Estimation of Regression Models in SPSS Final Remarks Exercises Appendix: Quantile Regression Models A Brief Introduction Example: Quantile Regression Model in Stata 14 Binary and Multinomial Logistic Regression Models Introduction The Binary Logistic Regression Model Estimation of the Binary Logistic Regression Model by Maximum Likelihood General Statistical Significance of the Binary Logistic Regression Model and Each of Its Parameters Construction of the Confidence Intervals of the Parameters for the Binary Logistic Regression Model Cutoff, Sensitivity Analysis, Overall Model Efficiency, Sensitivity, and Specificity The Multinomial Logistic Regression Model Estimation of the Multinomial Logistic Regression Model by Maximum Likelihood General Statistical Significance of the Multinomial Logistic Regression Model and Each of Its Parameters Construction of the Confidence Intervals of the Parameters for the Multinomial Logistic Regression Model Estimation of Binary and Multinomial Logistic Regression Models in Stata Binary Logistic Regression in Stata Multinomial Logistic Regression in Stata Estimation of Binary and Multinomial Logistic Regression Models in SPSS Binary Logistic Regression in SPSS Multinomial Logistic Regression in SPSS Final Remarks Exercises Appendix: Probit Regression Models A Brief Introduction Example: Probit Regression Model in Stata 15 Regression Models for Count Data: Poisson and Negative Binomial Introduction The Poisson Regression Model Estimation of the Poisson Regression Model by Maximum Likelihood General Statistical Significance of the Poisson Regression Model and Each of Its Parameters Construction of the Confidence Intervals of the Parameters for the Poisson Regression Model Test to Verify Overdispersion in Poisson Regression Models The Negative Binomial Regression Model Estimation of the Negative Binomial Regression Model by Maximum Likelihood General Statistical Significance of the Negative Binomial Regression Model and Each of Its Parameters Construction of the Confidence Intervals of the Parameters for the Negative Binomial Regression Model Estimating Regression Models for Count Data in Stata Poisson Regression Model in Stata Negative Binomial Regression Model in Stata Regression Model Estimation for Count Data in SPSS Poisson Regression Model in SPSS Negative Binomial Regression Model in SPSS Final Remarks Exercises Appendix: Zero-Inflated Regression Models Brief Introduction Example: Zero-Inflated Poisson Regression Model in Stata Example: Zero-Inflated Negative Binomial Regression Model in Stata Part VII: Optimization Models and Simulation 16 Introduction to Optimization Models: General Formulations and Business Modeling Introduction to Optimization Models Introduction to Linear Programming Models Mathematical Formulation of a General Linear Programming Model Linear Programming Model in the Standard and Canonical Forms Linear Programming Model in the Standard Form Linear Programming Model in the Canonical Form Transformations Into the Standard or Canonical Form Assumptions of the Linear Programming Model Proportionality Additivity Divisibility and Non-negativity Certainty Modeling Business Problems Using Linear Programming Production Mix Problem Blending or Mixing Problem Diet Problem Capital Budget Problems Portfolio Selection Problem Model 1: Maximization of an Investment Portfolios Expected Return Model 2: Investment Portfolio Risk Minimization Production and Inventory Problem Aggregated Planning Problem Final Remarks Exercises 17 Solution of Linear Programming Problems Introduction Graphical Solution of a Linear Programming Problem Linear Programming Maximization Problem with a Single Optimal Solution Linear Programming Minimization Problem With a Single Optimal Solution Special Cases Multiple Optimal Solutions Unlimited Objective Function z There Is No Optimal Solution Degenerate Optimal Solution Analytical Solution of a Linear Programming Problem in Which m n The Simplex Method Logic of the Simplex Method Analytical Solution of the Simplex method for Maximization Problems Tabular Form of the Simplex Method for Maximization Problems The Simplex Method for Minimization Problems Special Cases of the Simplex Method Multiple Optimal Solutions Unlimited Objective Function z There Is No Optimal Solution Degenerate Optimal Solution Solution by Using a Computer Solver in Excel Solution of the Examples found in Section 16.6 of Chapter 16 using Solver in Excel Solution of Example 16.3 of Chapter 16 (Production Mix Problem at the Venix Toys) Solution of Example 16.4 of Chapter 16 (Production Mix Problem at Naturelat Dairy) Solution of Example 16.5 of Chapter 16 (Mix Problem of Oil-South Refinery) Solution of Example 16.6 of Chapter 16 (Diet Problem) Solution of Example 16.7 of Chapter 16 (Farmers Problem) Solution of Example 16.8 of Chapter 16 (Portfolio Selection-Maximization of the Expected Return) Solution of Example 16.9 of Chapter 16 (Portfolio Selection-Minimization of the Portfolios Mean Absolute Deviation) Solution of Example 16.10 of Chapter 16 (Production and Inventory Problem of FenixandFurniture) Solution of Example 16.11 of Chapter 16 (Problem of Lifestyle Natural Juices Manufacturer) Solver Error Messages for Unlimited and Infeasible Solutions Unlimited Objective Function z There Is No Optimal Solution Result Analysis by Using the Solver Answer and Limits Reports Answer Report Limits Report Sensitivity Analysis Alteration in one of the Objective Function Coefficients (Graphical Solution) Alteration in One of the Constants on the Right-Hand Side of the Constraint and Concept of Shadow Price (Graphica ... Reduced Cost Sensitivity Analysis With Solver in Excel Special Case: Multiple Optimal Solutions Special Case: Degenerate Optimal Solution Exercises 18 Network Programming Introduction Terminology of Graphs and Networks Classic Transportation Problem Mathematical Formulation of the Classic Transportation Problem Balancing the Transportation Problem When the Total Supply Capacity Is Not Equal to the Total Demand Consumed Case 1: Total Supply Is Greater than Total Demand Case 2: Total Supply Capacity Is Lower than Total Demand Consumed Solution of the Classic Transportation Problem The Transportation Algorithm Solution of the Transportation Problem Using Excel Solver Transhipment Problem Mathematical Formulation of the Transhipment Problem Solution of the Transhipment Problem Using Excel Solver Job Assignment Problem Mathematical Formulation of the Job Assignment Problem Solution of the Job Assignment Problem Using Excel Solver Shortest Path Problem Mathematical Formulation of the Shortest Path Problem Solution of the Shortest Path Problem Using Excel Solver Maximum Flow Problem Mathematical Formulation of the Maximum Flow Problem Solution of the Maximum Flow Problem Using Excel Solver Exercises 19 Integer Programming Introduction Mathematical Formulation of a General Model for Integer Programming and/or Binary and Linear Relaxation The Knapsack Problem Modeling of the Knapsack Problem Solution of the Knapsack Problem Using Excel Solver The Capital Budgeting Problem as a Model of Binary Programming Solution of the Capital Budgeting Problem as a Model of Binary Programming Using Excel Solver The Traveling Salesman Problem Modeling of the Traveling Salesman Problem Solution of the Traveling Salesman Problem Using Excel Solver The Facility Location Problem Modeling of the Facility Location Problem Solution of the Facility Location Problem Using Excel Solver The Staff Scheduling Problem Solution of the Staff Scheduling Problem Using Excel Solver Exercises 20 Simulation and Risk Analysis Introduction to Simulation The Monte Carlo Method Monte Carlo Simulation in Excel Generation of Random Numbers and Probability Distributions in Excel Practical Examples Case 1: Consumption of Red Wine Case 2: Profit x Loss Forecast Final Remarks Exercises Part VIII: Other Topics 21 Design and Analysis of Experiments Introduction Steps in the Design of Experiments The Four Principles of Experimental Design Types of Experimental Design Completely Randomized Design (CRD) Randomized Block Design (RBD) Factorial Design (FD) One-Way Analysis of Variance Factorial ANOVA Final Remarks Exercises 22 Statistical Process Control Introduction Estimating the Process Mean and Variability Control Charts for Variables Control Charts for X and R Control Charts for X Control Charts for R Control Charts for X and S Control Charts for Attributes P Chart (Defective Fraction) np Chart (Number of Defective Products) C Chart (Total Number of Defects per Unit) U Chart (Average Number of Defects per Unit) Process Capability Cp Index Cpk Index Cpm and Cpmk Indexes Final Remarks Exercises 23 Data Mining and Multilevel Modeling Introduction to Data Mining Multilevel Modeling Nested Data Structures Hierarchical Linear Models Two-Level Hierarchical Linear Models With Clustered Data (HLM2) Three-Level Hierarchical Linear Models With Repeated Measures (HLM3) Estimation of Hierarchical Linear Models in Stata Estimation of a Two-Level Hierarchical Linear Model With Clustered Data in Stata Estimation of a Three-Level Hierarchical Linear Model With Repeated Measures in Stata Estimation of Hierarchical Linear Models in SPSS Estimation of a Two-Level Hierarchical Linear Model With Clustered Data in SPSS Estimation of a Three-Level Hierarchical Linear Model With Repeated Measures in SPSS Final Remarks Exercises Appendix Hierarchical Nonlinear Models Answers Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Answer Keys: Exercises: Chapter Appendices References Index A B C D E F G H I J K L M N O P Q R S T U V W Y Z
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