книга Microsoft Excel 2013: Data Analysis and Business Modeling

А Б В Г Д Е Ж З И К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Э Ю Я
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Microsoft Excel 2013: Data Analysis and Business Modeling

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Название: Microsoft Excel 2013: Data Analysis and Business Modeling
Автор: Winston W.L.
Страниц: 889
Формат: PDF
Размер: 29 Мб
Качество: Нормальное
Язык: Английский
Год издания: 2014


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.

Table of Contents:

Range names.
Lookup functions.
INDEX function.
MATCH function.
Text functions.
Dates and date functions.
Evaluating investments by using net present value criteria.
Internal rate of return.
More Excel financial functions.
Circular references.
IF statements.
Time and time functions.
The Paste Special command.
Three-dimensional formulas.
The Auditing tool and Inquire add-in.
Sensitivity analysis with data tables.
The Goal Seek command.
Using the Scenario Manager for sensitivity analysis.
The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functions.
The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS functions.
The OFFSET function.
The INDIRECT function.
Conditional formatting.
Sorting in Excel.
Tables.
Spinner buttons, scroll bars, option buttons, check boxes, combo boxes, and group list boxes.
The analytics revolution.
Introducing optimization with Excel Solver.
Using Solver to determine the optimal product mix.
Using Solver to schedule your workforce.
Using Solver to solve transportation or distribution problems.
Using Solver for capital budgeting.
Using Solver for financial planning.
Using Solver to rate sports teams.
Warehouse location and the GRG Multistart and Evolutionary Solver engines.
Penalties and the Evolutionary Solver.
The traveling salesperson problem.
Importing data from a text file or document.
Importing data from the Internet.
Validating data.
Summarizing data by using histograms.
Summarizing data by using descriptive statistics.
Using PivotTables and slicers to describe data.
The Data Model.
PowerPivot.
Power View.
Sparklines.
Summarizing data with database statistical functions.
Filtering data and removing duplicates.
Consolidating data.
Creating subtotals.
Charting tricks.
Estimating straight-line relationships.
Modeling exponential growth.
The power curve.
Using correlations to summarize relationships.
Introduction to multiple regression.
Incorporating qualitative factors into multiple regression.
Modeling nonlinearities and interactions.
Analysis of variance: one-way ANOVA.
Randomized blocks and two-way ANOVA.
Using moving averages to understand time series.
Winters’s method.
Ratio-to-moving-average forecast method.
Forecasting in the presence of special events.
An introduction to random variables.
The binomial, hypergeometric, and negative binomial random variables.
The Poisson and exponential random variable.
The normal random variable.
Weibull and beta distributions: modeling machine life and duration of a project.
Making probability statements from forecasts.
Using the lognormal random variable to model stock prices.
Introduction to Monte Carlo simulation.
Calculating an optimal bid.
Simulating stock prices and asset allocation modeling.
Fun and games: simulating gambling and sporting event probabilities.
Using resampling to analyze data.
Pricing stock options.
Determining customer value.
The economic order quantity inventory model.
Inventory modeling with uncertain demand.
Queuing theory: the mathematics of waiting in line.
Estimating a demand curve.
Pricing products by using tie-ins.
Pricing products by using subjectively determined demand.
Nonlinear pricing.
Array formulas and functions.




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