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- Full title: Business Analytics: Data Analysis & Decision Making, 7th Edition
- Edition: 7th
- Copyright year: 2020
- Publisher: Cengage Learning
- Author: S. Christian Albright; Wayne L. Winston
- ISBN: 9780357110065, 9780357110065
- Format: PDF
Description of Business Analytics: Data Analysis & Decision Making, 7th Edition:
Master data analysis, modeling and the effective use of spreadsheets with Albright/Winston’s popular BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 7E. The quantitative methods approach in this edition helps you maximize your success with a proven teach-by-example presentation, inviting writing style and complete integration of the latest version of Excel. For your convenience, this edition is also compatible with earlier versions of Excel. You’ll find a stronger data-oriented approach than ever before with a new chapter on the two main Power BI tools in Excel — Power Query and Power Pivot — and a new section of data visualization with Tableau Public. Current problems, cases and examples demonstrate the importance and application of the concepts and skills that you are learning for business today.Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Table of Contents of Business Analytics: Data Analysis & Decision Making, 7th Edition PDF ebook:
About the AuthorsBrief ContentsContentsPrefaceChapter 1: Introduction to Business Analytics1-1 Introduction1-2 Overview of the Book1-3 Introduction to Spreadsheet Modeling1-4 ConclusionSummary of Key TermsProblemsPart 1: Data AnalysisChapter 2: Describing the Distribution of a Variable2-1 Introduction2-2 Basic Concepts2-3 Summarizing Categorical Variables2-4 Summarizing Numeric Variables2-5 Time Series Data2-6 Outliers and Missing Values2-7 Excel Tables for Filtering, Sorting, and Summarizing2-8 ConclusionSummary of Key TermsProblemsCase 2.1 Correct Interpretation of MeansCase 2.2 The Dow Jones Industrial AverageCase 2.3 Home and Condo PricesAppendix: Introduction to StatToolsChapter 3: Finding Relationships among Variables3-1 Introduction3-2 Relationships among Categorical Variables3-3 Relationships among Categorical Variables and a Numeric Variable3-4 Relationships among Numeric Variables3-5 Pivot Tables3-6 ConclusionSummary of Key TermsProblemsCase 3.1 Customer Arrivals at Bank98Case 3.2 Saving, Spending, and Social ClimbingCase 3.3 Churn in the Cellular Phone MarketCase 3.4 Southwest Border Apprehensions and UnemploymentAppendix: Using StatTools to Find RelationshipsChapter 4: Business Intelligence (BI) Tools for Data Analysis4-1 Introduction4-2 Importing Data into Excel with Power Query4-3 Data Analysis with Power Pivot4-4 Data Visualization with Tableau Public4-5 Data Cleansing4-6 ConclusionSummary of Key TermsProblemsPart 2: Probability and Decision Making under UncertaintyChapter 5: Probability and Probability Distributions5-1 Introduction5-2 Probability Essentials5-3 Probability Distribution of a Random Variable5-4 The Normal Distribution5-5 The Binomial Distribution5-6 The Poisson and Exponential Distributions5-7 ConclusionSummary of Key TermsProblemsCase 5.1 Simpson’s ParadoxCase 5.2 EuroWatch CompanyCase 5.3 Cashing in on the LotteryChapter 6: Decision Making under Uncertainty6-1 Introduction6-2 Elements of Decision Analysis6-3 EMV and Decision Trees6-4 One-Stage Decision Problems6-5 The PrecisionTree Add-In6-6 Multistage Decision Problems6-7 The Role of Risk Aversion6-8 ConclusionSummary of Key TermsProblemsCase 6.1 Jogger Shoe CompanyCase 6.2 Westhouser Paper CompanyCase 6.3 Electronic Timing System for OlympicsCase 6.4 Developing a Helicopter Component for the ArmyAppendix: Decision Trees with DADM_ToolsPart 3: Statistical InferenceChapter 7: Sampling and Sampling Distributions7-1 Introduction7-2 Sampling Terminology7-3 Methods for Selecting Random Samples7-4 Introduction to Estimation7-5 ConclusionSummary of Key TermsProblemsChapter 8: Confidence Interval Estimation8-1 Introduction8-2 Sampling Distributions8-3 Confidence Interval for a Mean8-4 Confidence Interval for a Total8-5 Confidence Interval for a Proportion8-6 Confidence Interval for a Standard Deviation8-7 Confidence Interval for the Difference between Means8-8 Confidence Interval for the Difference between Proportions8-9 Sample Size Selection8-10 ConclusionSummary of Key TermsProblemsCase 8.1 Harrigan University AdmissionsCase 8.2 Employee Retention at D&YCase 8.3 Delivery Times at SnowPea RestaurantChapter 9: Hypothesis Testing9-1 Introduction9-2 Concepts in Hypothesis Testing9-3 Hypothesis Tests for a Population Mean9-4 Hypothesis Tests for Other Parameters9-5 Tests for Normality9-6 Chi-Square Test for Independence9-7 ConclusionSummary of Key TermsProblemsCase 9.1 Regression toward the MeanCase 9.2 Friday Effect in the Stock MarketCase 9.3 Removing Vioxx from the MarketPart 4: Regression Analysis and Time Series ForecastingChapter 10: Regression Analysis: Estimating Relationships10-1 Introduction10-2 Scatterplots: Graphing Relationships10-3 Correlations: Indicators of Linear Relationships10-4 Simple Linear Regression10-5 Multiple Regression10-6 Modeling Possibilities10-7 Validation of the Fit10-8 ConclusionSummary of Key TermsProblemsCase 10.1 Quantity Discounts at Firm Chair CompanyCase 10.2 Housing Price Structure in Mid CityCase 10.3 Demand for French Bread at Howie’s BakeryCase 10.4 Investing for RetirementChapter 11: Regression Analysis: Statistical Inference11-1 Introduction11-2 The Statistical Model11-3 Inferences about the Regression Coefficients11-4 Multicollinearity11-5 Include/Exclude Decisions11-6 Stepwise Regression11-7 Outliers11-8 Violations of Regression Assumptions11-9 Prediction11-10 ConclusionSummary of Key TermsProblemsCase 11.1 Heating Oil at Dupree FuelsCase 11.2 Developing a Flexible Budget at the Gunderson PlantCase 11.3 Forecasting Overhead at Wagner PrintersChapter 12: Time Series Analysis and Forecasting12-1 Introduction12-2 Forecasting Methods: An Overview12-3 Testing for Randomness12-4 Regression-Based Trend Models12-5 The Random Walk Model12-6 Moving Averages Forecasts12-7 Exponential Smoothing Forecasts12-8 Seasonal Models12-9 ConclusionSummary of Key TermsProblemsCase 12.1 Arrivals at the Credit UnionCase 12.2 Forecasting Weekly Sales at AmantaAppendix: Alternative Forecasting SoftwarePart 5: Optimization and Simulation ModelingChapter 13: Introduction to Optimization Modeling13-1 Introduction13-2 Introduction to Optimization13-3 A Two-Variable Product Mix Model13-4 Sensitivity Analysis13-5 Properties of Linear Models13-6 Infeasibility and Unboundedness13-7 A Larger Product Mix Model13-8 A Multiperiod Production Model13-9 A Comparison of Algebraic and Spreadsheet Models13-10 A Decision Support System13-11 ConclusionSummary of Key TermsProblemsCase 13.1 Shelby ShelvingChapter 14: Optimization Models14-1 Introduction14-2 Employee Scheduling Models14-3 Blending Models14-4 Logistics Models14-5 Aggregate Planning Models14-6 Financial Models14-7 Integer Optimization Models14-8 Nonlinear Optimization Models14-9 ConclusionSummary of Key TermsProblemsCase 14.1 Giant Motor CompanyCase 14.2 GMS Stock HedgingChapter 15: Introduction to Simulation Modeling15-1 Introduction15-2 Probability Distributions for Input Variables15-3 Simulation and the Flaw of Averages15-4 Simulation with Built-in Excel Tools15-5 Simulation with @RISK15-6 The Effects of Input Distributions on Results15-7 ConclusionSummary of Key TermsProblemsCase 15.1 Ski Jacket ProductionCase 15.2 Ebony Bath SoapAppendix: Simulation with DADM_ToolsChapter 16: Simulation Models16-1 Introduction16-2 Operations Models16-3 Financial Models16-4 Marketing Models16-5 Simulating Games of Chance16-6 ConclusionSummary of Key TermsProblemsCase 16.1 College Fund InvestmentCase 16.2 Bond Investment StrategyPart 6: Advanced Data AnalysisChapter 17: Data Mining17-1 Introduction17-2 Classification Methods17-3 Clustering Methods17-4 ConclusionSummary of Key TermsProblemsCase 17.1 Houston Area SurveyReferencesIndex