Microsoft Excel 2019 Data Analysis and Business Modeling, 6th edition

Published by Microsoft Press (March 28, 2019) © 2019

  • Wayne Winston
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Transform data into bottom-line results

Master business modeling and analysis techniques with Microsoft Excel 2019, 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.

  • 1 Basic worksheet modeling
  • 2 Range names
  • 3 Lookup functions
  • 4 The INDEX function
  • 5 The MATCH function
  • 6 Text functions and Flash Fill
  • 7 Dates and date functions
  • 8 Evaluating investments by using net present value criteria
  • 9 IRR, XIRR, and MIRR functions
  • 10 More Excel financial functions
  • 11 Circular references
  • 12 IF, IFERROR, IFS, CHOOSE, and SWITCH functions
  • 13 Time and time functions
  • 14 The Paste Special command
  • 15 Three-dimensional formulas and hyperlinks
  • 16 The auditing tool and the Inquire add-in
  • 17 Sensitivity analysis with data tables
  • 18 The Goal Seek command
  • 19 Using the Scenario Manager for sensitivity analysis
  • 20 The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functions
  • 21 The SUMIF, AVERAGEIF, SUMIFS, AVERAGEIFS, MAXIFS, and MINIFS functions
  • 22 The OFFSET function
  • 23 The INDIRECT function
  • 24 Conditional formatting
  • 25 Sorting in Excel
  • 26 Excel tables and table slicers
  • 27 Spin buttons, scrollbars, option buttons, check boxes, combo boxes, and group list boxes
  • 28 The analytics revolution
  • 29 An introduction to optimization with Excel Solver
  • 30 Using Solver to determine the optimal product mix
  • 31 Using Solver to schedule your workforce
  • 32 Using Solver to solve transportation or distribution problems
  • 33 Using Solver for capital budgeting
  • 34 Using Solver for financial planning
  • 35 Using Solver to rate sports teams
  • 36 Warehouse location and the GRG Multistart and Evolutionary Solver engines
  • 37 Penalties and the Evolutionary Solver
  • 38 The traveling salesperson problem
  • 39 Importing data from a text file or document
  • 40 Get & Transform (or s/b Get & Transform?)
  • 41 Geography and Stock data types
  • 42 Validating data
  • 43 Summarizing data by using histograms and Pareto charts
  • 44 Summarizing data by using descriptive statistics
  • 45 Using pivot tables and slicers to describe data
  • 46 The Data Model
  • 47 Power Pivot
  • 48 Filled and 3D Power Maps
  • 49 Sparklines
  • 50 Summarizing data with database statistical functions
  • 51 Filtering data and removing duplicates
  • 52 Consolidating data
  • 53 Creating subtotals
  • 54 Charting tricks
  • 55 Estimating straight-line relationships
  • 56 Modeling exponential growth
  • 57 The power curve
  • 58 Using correlations to summarize relationships
  • 59 Introduction to multiple regression
  • 60 Incorporating qualitative factors into multiple regression
  • 61 Modeling nonlinearities and interactions
  • 62 Analysis of variance: One-way ANOVA
  • 63 Randomized blocks and two-way ANOVA
  • 64 Using moving averages to understand time series
  • 65 Winters method and the Forecast Sheet
  • 66 Ratio-to-moving-average forecast method
  • 67 Forecasting in the presence of special events
  • 68 Introduction to Probability
  • 69 An introduction to random variables
  • 70 The binomial, hypergeometric, and negative binomial random variables
  • 71 The Poisson and exponential random variables
  • 72 The normal random variable and Z-scores
  • 73 Weibull and beta distributions: Modeling machine life and project duration
  • 74 Making probability statements from forecasts
  • 75 Using the lognormal random variable to model stock prices
  • 76 Importing Historical stock data into Excel
  • 77 Introduction to Monte Carlo simulation
  • 78 Calculating an optimal bid
  • 79 Simulating stock prices and asset-allocation modeling
  • 80 Fun and games: Simulating gambling and sporting event probabilities
  • 81 Using resampling to analyze data
  •  82 Pricing stock options
  • 83 Determining customer value
  • 84 The economic order quantity inventory model
  • 85 Inventory modeling with uncertain demand
  • 86 Queueing theory: The mathematics of waiting in line
  • 87 Estimating a demand curve
  • 88 Pricing products by using tie-ins
  • 89 Pricing products by using subjectively determined demand
  • 90 Nonlinear pricing
  • 91 Array formulas and functions
  • 92 Recording macros
  • 93 Advanced Sensitivity Analysis

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