Produktbild: Microsoft Excel 2019 Data Analysis and Business Modeling

Microsoft Excel 2019 Data Analysis and Business Modeling

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.04.2019

Verlag

Pearson Education Limited

Seitenzahl

880

Maße (L/B/H)

23.5/19.5/4.7 cm

Gewicht

1641 g

Auflage

6

Sprache

Englisch

ISBN

978-1-5093-0588-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.04.2019

Verlag

Pearson Education Limited

Seitenzahl

880

Maße (L/B/H)

23.5/19.5/4.7 cm

Gewicht

1641 g

Auflage

6

Sprache

Englisch

ISBN

978-1-5093-0588-9

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Microsoft Excel 2019 Data Analysis and Business Modeling

    • 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 20 The 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
    • Chap