Microsoft Excel 2019 Data Analysis and Business Modeling eBook (6e) : 9781509306084

Microsoft Excel 2019 Data Analysis and Business Modeling eBook (6e)

Published by
Pearson Higher Ed USA
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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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Table of contents
  • 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
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