 # Microsoft Excel 2013 Data Analysis and Business Modeling

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## Description

• Recorded Online Training
• ISBN-10: 0-7356-8105-8
• ISBN-13: 978-0-7356-8105-7

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.

• Summarize data with PivotTables and Descriptive Statistics
• Explore new trends in predictive and prescriptive analytics
• Use Excel Trend Curves, multiple regression, and exponential smoothing
• Master advanced Excel functions such as OFFSET and INDIRECT
• Delve into key financial, statistical, and time functions
• Make your charts more effective with the Power View tool
• Tame complex optimization problems with Excel Solver
• Run Monte Carlo simulations on stock prices and bidding models
• Apply important modeling tools such as the Inquire add-in

## Sample Content

• Introduction
• Chapter 1: Range names
• Chapter 2: Lookup functions
• Chapter 3: INDEX function
• Chapter 4: MATCH function
• Chapter 5: Text functions
• Chapter 6: Dates and date functions
• Chapter 7: Evaluating investments by using net present value criteria
• Chapter 8: Internal rate of return
• Chapter 9: More Excel financial functions
• Chapter 10: Circular references
• Chapter 11: IF statements
• Chapter 12: Time and time functions
• Chapter 13: The Paste Special command
• Chapter 14: Three-dimensional formulas
• Chapter 15: The Auditing tool and Inquire add-in
• Chapter 16: Sensitivity analysis with data tables
• Chapter 17: The Goal Seek command
• Chapter 18: Using the Scenario Manager for sensitivity analysis
• Chapter 19: The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functions
• Chapter 20: The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS functions
• Chapter 21: The OFFSET function
• Chapter 22: The INDIRECT function
• Chapter 23: Conditional formatting
• Chapter 24: Sorting in Excel
• Chapter 25: Tables
• Chapter 26: Spinner buttons, scroll bars, option buttons, check boxes, combo boxes, and group list boxes
• Chapter 27: The analytics revolution
• Chapter 28: Introducing optimization with Excel Solver
• Chapter 29: Using Solver to determine the optimal product mix
• Chapter 30: Using Solver to schedule your workforce
• Chapter 31: Using Solver to solve transportation or distribution problems
• Chapter 32: Using Solver for capital budgeting
• Chapter 33: Using Solver for financial planning
• Chapter 34: Using Solver to rate sports teams
• Chapter 35: Warehouse location and the GRG Multistart and Evolutionary Solver engines
• Chapter 36: Penalties and the Evolutionary Solver
• Chapter 37: The traveling salesperson problem
• Chapter 38: Importing data from a text file or document
• Chapter 39: Importing data from the Internet
• Chapter 40: Validating data
• Chapter 41: Summarizing data by using histograms
• Chapter 42: Summarizing data by using descriptive statistics
• Chapter 43: Using PivotTables and slicers to describe data
• Chapter 44: The Data Model
• Chapter 45: PowerPivot
• Chapter 46: Power View
• 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's method
• Chapter 64: Ratio-to-moving-average forecast method
• Chapter 65: Forecasting in the presence of special events
• Chapter 66: An introduction to random variables
• Chapter 67: The binomial, hypergeometric, and negative binomial random variables
• Chapter 68: The Poisson and exponential random variable
• Chapter 69: The normal random variable
• Chapter 70: Weibull and beta distributions: modeling machine life and duration of a project
• Chapter 71: Making probability statements from forecasts
• Chapter 72: Using the lognormal random variable to model stock prices
• Chapter 73: Introduction to Monte Carlo simulation
• Chapter 74: Calculating an optimal bid
• Chapter 75: Simulating stock prices and asset allocation modeling
• Chapter 76: Fun and games: simulating gambling and sporting event probabilities
• Chapter 77: Using resampling to analyze data
• Chapter 78: Pricing stock options
• Chapter 79: Determining customer value
• Chapter 80: The economic order quantity inventory model
• Chapter 81: Inventory modeling with uncertain demand
• Chapter 82: Queuing theory: the mathematics of waiting in line
• Chapter 83: Estimating a demand curve
• Chapter 84: Pricing products by using tie-ins
• Chapter 85: Pricing products by using subjectively determined demand
• Chapter 86: Nonlinear pricing
• Chapter 87: Array formulas and functions 