AUMELBAS281

# Microsoft Excel 2019 Data Analysis and Business Modeling eBook, 6th Edition

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Overview
Author
Edition
6th
ISBN
9781509306084
Published Date
01/04/2019
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.

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