Business Analytics, Global Edition 3rd Edition
- Length: 705 pages
- Edition: 3
- Language: English
- Publisher: Pearson
- Publication Date: 2020-08-11
- ISBN-10: 1292339063
- ISBN-13: 9781292339061
- Sales Rank: #140479 (See Top 100 Books)
A balanced and holistic approach to business analytics
Business Analytics teaches the fundamental concepts of modern business analytics and provides vital tools in understanding how data analysis works in today’s organisations. Author James Evans takes a fair and comprehensive, approach, examining business analytics from both descriptive and predictive perspectives. Students learn how to apply basic principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. And included access to commercial grade analytics software gives students real-world experience and career-focused value. As such, the 3rd Edition has gone through an extensive revision and now relies solely on Excel, enhancing students’ skills in the program and basic understanding of fundamental concepts.
Cover Half Title Page Title Page Copyright Page Brief Contents Contents Preface About the Author Credits Part 1: Foundations of Business Analytics Chapter 1: Introduction to Business Analytics Learning Objectives What Is Business Analytics? Using Business Analytics Impacts and Challenges Evolution of Business Analytics Analytic Foundations Modern Business Analytics Software Support and Spreadsheet Technology Analytics in Practice: Social Media Analytics Descriptive, Predictive, and Prescriptive Analytics Analytics in Practice: Analytics in the Home Lending and Mortgage Industry Data for Business Analytics Big Data Data Reliability and Validity Models in Business Analytics Descriptive Models Predictive Models Prescriptive Models Model Assumptions Uncertainty and Risk Problem Solving with Analytics Recognizing a Problem Defining the Problem Structuring the Problem Analyzing the Problem Interpreting Results and Making a Decision Implementing the Solution Analytics in Practice: Developing Effective Analytical Tools at Hewlett‐Packard Key Terms Chapter 1 Technology Help Problems and Exercises Case: Performance Lawn Equipment Appendix A1: Basic Excel Skills Excel Formulas and Addressing Copying Formulas Useful Excel Tips Excel Functions Basic Excel Functions Functions for Specific Applications Insert Function Date and Time Functions Miscellaneous Excel Functions and Tools Range Names VALUE Function Paste Special Concatenation Error Values Problems and Exercises Chapter 2: Database Analytics Learning Objectives Data Sets and Databases Using Range Names in Databases Analytics in Practice: Using Big Data to Monitor Water Usage in Cary, North Carolina Data Queries: Tables, Sorting, and Filtering Sorting Data in Excel Pareto Analysis Filtering Data Database Functions Analytics in Practice: Discovering the Value of Database Analytics at Allders International Logical Functions Lookup Functions for Database Queries Excel Template Design Data Validation Tools Form Controls PivotTables PivotTable Customization Slicers Key Terms Chapter 2 Technology Help Problems and Exercises Case: People’s Choice Bank Case: Drout Advertising Research Project Part 2: Descriptive Analytics Chapter 3: Data Visualization Learning Objectives The Value of Data Visualization Tools and Software for Data Visualization Analytics in Practice: Data Visualization for the New York City Police Department’s Domain Awareness System Creating Charts in Microsoft Excel Column and Bar Charts Data Label and Data Table Chart Options Line Charts Pie Charts Area Charts Scatter Charts and Orbit Charts Bubble Charts Combination Charts Radar Charts Stock Charts Charts from PivotTables Geographic Data Other Excel Data Visualization Tools Data Bars Color Scales Icon Sets Sparklines Dashboards Analytics in Practice: Driving Business Transformation with IBM Business Analytics Key Terms Chapter 3 Technology Help Problems and Exercises Case: Performance Lawn Equipment Appendix A3: Additional Tools for Data Visualization Hierarchy Charts Waterfall Charts PivotCharts Tableau Problems and Exercises Chapter 4: Descriptive Statistics Learning Objectives Analytics in Practice: Applications of Statistics in Health care Metrics and Data Classification Frequency Distributions and Histograms Frequency Distributions for Categorical Data Relative Frequency Distributions Frequency Distributions for Numerical Data Grouped Frequency Distributions Cumulative Relative Frequency Distributions Constructing Frequency Distributions Using PivotTables Percentiles and Quartiles Cross‐Tabulations Descriptive Statistical Measures Populations and Samples Statistical Notation Measures of Location: Mean, Median, Mode, and Midrange Using Measures of Location in Business Decisions Measures of Dispersion: Range, Interquartile Range, Variance, and Standard Deviation Chebyshev’s Theorem and the Empirical Rules Standardized Values (Z‐Scores) Coefficient of Variation Measures of Shape Excel Descriptive Statistics Tool Computing Descriptive Statistics for Frequency Distributions Descriptive Statistics for Categorical Data: The Proportion Statistics in PivotTables Measures of Association Covariance Correlation Excel Correlation Tool Outliers Using Descriptive Statistics to Analyze Survey Data Statistical Thinking in Business Decisions Variability in Samples Analytics in Practice: Applying Statistical Thinking to Detecting Financial Problems Key Terms Chapter 4 Technology Help Problems and Exercises Case: Drout Advertising Research Project Case: Performance Lawn Equipment Appendix A4: Additional Charts for Descriptive Statistics in Excel for Windows Problems and Exercises Chapter 5: Probability Distributions and Data Modeling Learning Objectives Basic Concepts of Probability Experiments and Sample Spaces Combinations and Permutations Probability Definitions Probability Rules and Formulas Joint and Marginal Probability Conditional Probability Random Variables and Probability Distributions Discrete Probability Distributions Expected Value of a Discrete Random Variable Using Expected Value in Making Decisions Variance of a Discrete Random Variable Bernoulli Distribution Binomial Distribution Poisson Distribution Analytics in Practice: Using the Poisson Distribution for Modeling Bids on Priceline Continuous Probability Distributions Properties of Probability Density Functions Uniform Distribution Normal Distribution The NORM.INV Function Standard Normal Distribution Using Standard Normal Distribution Tables Exponential Distribution Triangular Distribution Data Modeling and Distribution Fitting Goodness of Fit: Testing for Normality of an Empirical Distribution Analytics in Practice: The value of Good Data Modeling in Advertising Key Terms Chapter 5 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 6: Sampling and Estimation Learning Objectives Statistical Sampling Sampling Methods Analytics in Practice: Using Sampling Techniques to Improve Distribution Estimating Population Parameters Unbiased Estimators Errors in Point Estimation Understanding Sampling Error Sampling Distributions Sampling Distribution of the Mean Applying the Sampling Distribution of the Mean Interval Estimates Confidence Intervals Confidence Interval for the Mean with Known Population Standard Deviation The t‐Distribution Confidence Interval for the Mean with Unknown Population Standard Deviation Confidence Interval for a Proportion Additional Types of Confidence Intervals Using Confidence Intervals for Decision Making Data Visualization for Confidence Interval Comparison Prediction Intervals Confidence Intervals and Sample Size Key Terms Chapter 6 Technology Help Problems and Exercises Case: Drout Advertising Research Project Case: Performance Lawn Equipment Chapter 7: Statistical Inference Learning Objectives Hypothesis Testing Hypothesis‐Testing Procedure One‐Sample Hypothesis Tests Understanding Potential Errors in Hypothesis Testing Selecting the Test Statistic Finding Critical Values and Drawing a Conclusion Two‐Tailed Test of Hypothesis for the Mean Summary of One‐Sample Hypothesis Tests for the Mean p‐Values One‐Sample Tests for Proportions Confidence Intervals and Hypothesis Tests An Excel Template for One‐Sample Hypothesis Tests Two‐Sample Hypothesis Tests Two‐Sample Tests for Differences in Means Two‐Sample Test for Means with Paired Samples Two‐Sample Test for Equality of Variances Analysis of Variance (ANOVA) Assumptions of ANOVA Chi‐Square Test for Independence Cautions in Using the Chi‐Square Test Chi‐Square Goodness of Fit Test Analytics in Practice: Using Hypothesis Tests and Business Analytics in a Help Desk Service Improvement Project Key Terms Chapter 7 Technology Help Problems and Exercises Case: Drout Advertising Research Project Case: Performance Lawn Equipment Part 3: Predictive Analytics Chapter 8: Trendlines and Regression Analysis Learning Objectives Modeling Relationships and Trends in Data Analytics in Practice: Using Predictive Trendline Models at Procter & Gamble Simple Linear Regression Finding the Best‐Fitting Regression Line Using Regression Models for Prediction Least‐Squares Regression Simple Linear Regression with Excel Regression as Analysis of Variance Testing Hypotheses for Regression Coefficients Confidence Intervals for Regression Coefficients Residual Analysis and Regression Assumptions Checking Assumptions Multiple Linear Regression Analytics in Practice: Using Linear Regression and Interactive Risk Simulators to Predict Performance at Aramark Building Good Regression Models Correlation and Multicollinearity Practical Issues in Trendline and Regression Modeling Regression with Categorical Independent Variables Categorical Variables with More Than Two Levels Regression Models with Nonlinear Terms Key Terms Chapter 8 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 9: Forecasting Techniques Learning Objectives Analytics in Practice: Forecasting Call‐Center Demand at L.L. Bean Qualitative and Judgmental Forecasting Historical Analogy The Delphi Method Indicators and Indexes Statistical Forecasting Models Forecasting Models for Stationary Time Series Moving Average Models Error Metrics and Forecast Accuracy Exponential Smoothing Models Forecasting Models for Time Series with a Linear Trend Double Exponential Smoothing Regression‐Based Forecasting for Time Series with a Linear Trend Forecasting Time Series with Seasonality Regression‐Based Seasonal Forecasting Models Holt‐Winters Models for Forecasting Time Series with Seasonality and No Trend Holt‐Winters Models for Forecasting Time Series with Seasonality and Trend Selecting Appropriate Time‐Series‐Based Forecasting Models Regression Forecasting with Causal Variables The Practice of Forecasting Analytics in Practice: Forecasting at NBCUniversal Key Terms Chapter 9 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 10: Introduction to Data Mining Learning Objectives The Scope of Data Mining Cluster Analysis Measuring Distance Between Objects Normalizing Distance Measures Clustering Methods Classification An Intuitive Explanation of Classification Measuring Classification Performance Classification Techniques Association Cause‐and‐Effect Modeling Analytics In Practice: Successful Business Applications of Data Mining Key Terms Chapter 10 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 11: Spreadsheet Modeling and Analysis Learning Objectives Analytics in Practice: Using Spreadsheet Modeling and Analysis at Nestlé Model‐Building Strategies Building Models Using Logic and Business Principles Building Models Using Influence Diagrams Building Models Using Historical Data Model Assumptions, Complexity, and Realism Implementing Models on Spreadsheets Spreadsheet Design Spreadsheet Quality Data Validation Analytics in Practice: Spreadsheet Engineering at Procter & Gamble Descriptive Spreadsheet Models Staffing Decisions Single‐Period Purchase Decisions Overbooking Decisions Analytics in Practice: Using an Overbooking Model at a Student Health Clinic Retail Markdown Decisions Predictive Spreadsheet Models New Product Development Model Cash Budgeting Retirement Planning Project Management Prescriptive Spreadsheet Models Portfolio Allocation Locating Central Facilities Job Sequencing Analyzing Uncertainty and Model Assumptions What‐If Analysis Data Tables Scenario Manager Goal Seek Key Terms Chapter 11 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 12: Simulation and Risk Analysis Learning Objectives Monte Carlo Simulation Random Sampling from Probability Distributions Generating Random Variates using Excel Functions Discrete Probability Distributions Uniform Distributions Exponential Distributions Normal Distributions Binomial Distributions Triangular Distributions Monte Carlo Simulation in Excel Profit Model Simulation New Product Development Retirement Planning Single‐Period Purchase Decisions Overbooking Decisions Project Management Analytics in Practice: Implementing Large‐Scale Monte Carlo Spreadsheet Models Dynamic Systems Simulation Simulating Waiting Lines Analytics in Practice: Using Systems Simulation for Agricultural Product Development Key Terms Chapter 12 Technology Help Problems and Exercises Case: Performance Lawn Equipment Part 4: Prescriptive Analytics Chapter 13: Linear Optimization Learning Objectives Optimization Models Analytics in Practice: Using Optimization Models for Sales Planning at NBC Developing Linear Optimization Models Identifying Decision Variables, the Objective, and Constraints Developing a Mathematical Model More About Constraints Implementing Linear Optimization Models on Spreadsheets Excel Functions to Avoid in Linear Optimization Solving Linear Optimization Models Solver Answer Report Graphical Interpretation of Linear Optimization with Two Variables How Solver Works How Solver Creates Names in Reports Solver Outcomes and Solution Messages Unique Optimal Solution Alternative (Multiple) Optimal Solutions Unbounded Solution Infeasibility Applications of Linear Optimization Blending Models Dealing with Infeasibility Portfolio Investment Models Scaling Issues in Using Solver Transportation Models Multiperiod Production Planning Models Multiperiod Financial Planning Models Analytics in Practice: Linear Optimization in Bank Financial Planning Key Terms Chapter 13 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 14: Integer and Nonlinear Optimization Learning Objectives Integer Linear Optimization Models Models with General Integer Variables Workforce‐Scheduling Models Alternative Optimal Solutions Models with Binary Variables Using Binary Variables to Model Logical Constraints Applications in Supply Chain Optimization Analytics in Practice: Supply Chain Optimization at Procter & Gamble Nonlinear Optimization Models A Nonlinear Pricing Decision Model Quadratic Optimization Practical Issues Using Solver for Nonlinear Optimization Analytics in Practice: Applying Nonlinear Optimization at Prudential Securities Non‐Smooth Optimization Evolutionary Solver Evolutionary Solver for Sequencing and Scheduling Models The Traveling Salesperson Problem Key Terms Chapter 14 Technology Help Problems and Exercises Case: Performance Lawn Equipment Chapter 15: Optimization Analytics Learning Objectives What‐If Analysis for Optimization Models Solver Sensitivity Report Using the Sensitivity Report Degeneracy Interpreting Solver Reports for Nonlinear Optimization Models Models with Bounded Variables Auxiliary Variables for Bound Constraints What‐If Analysis for Integer Optimization Models Visualization of Solver Reports Using Sensitivity Information Correctly Key Terms Chapter 15 Technology Help Problems and Exercises Case: Performance Lawn Equipment Part 5: Making Decisions Chapter 16: Decision Analysis Learning Objectives Formulating Decision Problems Decision Strategies Without Outcome Probabilities Decision Strategies for a Minimize Objective Decision Strategies for a Maximize Objective Decisions with Conflicting Objectives Decision Strategies with Outcome Probabilities Average Payoff Strategy Expected Value Strategy Evaluating Risk Decision Trees Decision Trees and Risk Sensitivity Analysis in Decision Trees The Value of Information Decisions with Sample Information Bayes’s Rule Utility and Decision Making Constructing a Utility Function Exponential Utility Functions Analytics in Practice: Using Decision Analysis in Drug Development Key Terms Chapter 16 Technology Help Problems and Exercises Case: Performance Lawn Equipment Appendix A Glossary Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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