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Microsoft Excel 2013: Data Analysis and Business Modelingby Wayne L Winston
Synopses & Reviews
Master business modeling and analysis techniques with Microsoft Excel 2013, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide shows you how to use the latest Excel tools to integrate data from multiple tables—and how to effectively build a relational data source inside an Excel workbook.
Solve real business problems with Excel—and sharpen your edge
Master the business modeling and analytic techniques with Microsoft Excel 2013, and help transform your data into bottom-line results. With this hands-on, scenario-focused guide from Excel expert and bestselling author Wayne Winston, youll learn how to use the new Data Model and Relationships features for integrating data from multiple tables, effectively building a relational data source inside an Excel workbook.
Gain the skills to solve real-world business problems with Excel 2013
About the Author
Wayne L. Winston is a professor of Decision Sciences at Indiana University's Kelley School of Business and has earned numerous MBA teaching awards. For 20+ years, he has taught clients at Fortune 500 companies how to use Excel to make smarter business decisions. Wayne and his business partner Jeff Sagarin developed the player-statistics tracking and rating system used by the Dallas Mavericks professional basketball team. He is also a two time Jeopardy! Champion.
Table of Contents
IntroductionChapter 1: Range namesChapter 2: Lookup functionsChapter 3: INDEX functionChapter 4: MATCH functionChapter 5: Text functionsChapter 6: Dates and date functionsChapter 7: Evaluating investments by using net present value criteriaChapter 8: Internal rate of returnChapter 9: More Excel financial functionsChapter 10: Circular referencesChapter 11: IF statementsChapter 12: Time and time functionsChapter 13: The Paste Special commandChapter 14: Three-dimensional formulasChapter 15: The Auditing tool and Inquire add-inChapter 16: Sensitivity analysis with data tablesChapter 17: The Goal Seek commandChapter 18: Using the Scenario Manager for sensitivity analysisChapter 19: The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functionsChapter 20: The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS functionsChapter 21: The OFFSET functionChapter 22: The INDIRECT functionChapter 23: Conditional formattingChapter 24: Sorting in ExcelChapter 25: TablesChapter 26: Spinner buttons, scroll bars, option buttons, check boxes, combo boxes, and group list boxesChapter 27: The analytics revolutionChapter 28: Introducing optimization with Excel SolverChapter 29: Using Solver to determine the optimal product mixChapter 30: Using Solver to schedule your workforceChapter 31: Using Solver to solve transportation or distribution problemsChapter 32: Using Solver for capital budgetingChapter 33: Using Solver for financial planningChapter 34: Using Solver to rate sports teamsChapter 35: Warehouse location and the GRG Multistart and Evolutionary Solver enginesChapter 36: Penalties and the Evolutionary SolverChapter 37: The traveling salesperson problemChapter 38: Importing data from a text file or documentChapter 39: Importing data from the InternetChapter 40: Validating dataChapter 41: Summarizing data by using histogramsChapter 42: Summarizing data by using descriptive statisticsChapter 43: Using PivotTables and slicers to describe dataChapter 44: The Data ModelChapter 45: PowerPivotChapter 46: Power ViewChapter 47: SparklinesChapter 48: Summarizing data with database statistical functionsChapter 49: Filtering data and removing duplicatesChapter 50: Consolidating dataChapter 51: Creating subtotalsChapter 52: Charting tricksChapter 53: Estimating straight-line relationshipsChapter 54: Modeling exponential growthChapter 55: The power curveChapter 56: Using correlations to summarize relationshipsChapter 57: Introduction to multiple regressionChapter 58: Incorporating qualitative factors into multiple regressionChapter 59: Modeling nonlinearities and interactionsChapter 60: Analysis of variance: one-way ANOVAChapter 61: Randomized blocks and two-way ANOVAChapter 62: Using moving averages to understand time seriesChapter 63: Winterss methodChapter 64: Ratio-to-moving-average forecast methodChapter 65: Forecasting in the presence of special eventsChapter 66: An introduction to random variablesChapter 67: The binomial, hypergeometric, and negative binomial random variablesChapter 68: The Poisson and exponential random variableChapter 69: The normal random variableChapter 70: Weibull and beta distributions: modeling machine life and duration of a projectChapter 71: Making probability statements from forecastsChapter 72: Using the lognormal random variable to model stock pricesChapter 73: Introduction to Monte Carlo simulationChapter 74: Calculating an optimal bidChapter 75: Simulating stock prices and asset allocation modelingChapter 76: Fun and games: simulating gambling and sporting event probabilitiesChapter 77: Using resampling to analyze dataChapter 78: Pricing stock optionsChapter 79: Determining customer valueChapter 80: The economic order quantity inventory modelChapter 81: Inventory modeling with uncertain demandChapter 82: Queuing theory: the mathematics of waiting in lineChapter 83: Estimating a demand curveChapter 84: Pricing products by using tie-insChapter 85: Pricing products by using subjectively determined demandChapter 86: Nonlinear pricingChapter 87: Array formulas and functionsAbout the Author
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