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Viser: Microsoft Excel 2013: Data Analysis and Business Modeling
Microsoft Excel 2013: Data Analysis and Business Modeling
Wayne L. Winston
(2014)
Sprog: Engelsk
Detaljer om varen
- Paperback: 896 sider
- Udgiver: Microsoft Press (Januar 2014)
- ISBN: 9780735669130
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
- 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
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 About the Author