Business Statistics for Competitive Advantage with Excel 2007: Basics, Model Building, and Cases

ذڑذ½ذ¸ذ³ذ° Business Statistics for Competitive Advantage with Excel 2007: Basics,.
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Business Statistics for Competitive Advantage with Excel 2007: Basics, Model Building and Cases ذڑذ½ذ¸ذ³ذ¸ Microsoft Office ذگذ²ر‚ذ¾ر€: Cynthia Fraser ذ“ذ¾ذ´ ذ¸ذ·ذ´ذ°ذ½ذ¸رڈ: 2009 ذ¤ذ¾ر€ذ¼ذ°ر‚: pdf ذکذ·ذ´ذ°ر‚.
:Springer Science ذ،ر‚ر€ذ°ذ½ذ¸ر†: 410 ذ ذ°ذ·ذ¼ذµر€: 21,5 ISBN: 978-0-387-74403-2 ذ¯ذ·ر‹ذ؛: ذگذ½ذ³ذ»ذ¸ذ¹رپذ؛ذ¸ذ¹0 (ذ³ذ¾ذ»ذ¾رپذ¾ذ²: 0) ذ‍ر†ذµذ½ذ؛ذ°:This text helps business students develop competitive advantages for use in their future careers as decision makers.
Students learn to build models using logic and experience, produce statistics using Excel 2007 with shortcuts, and translate results into implications for decision makers.
The author emphasizes communicating results effectively in plain English and with compelling graphics in the form of memos and PowerPoints.
Statistics, from basics to sophisticated models, are illustrated with examples using real data such as students will encounter in their roles as managers.
A number of examples focus on business in emerging global markets with particular emphasis on China and India.
Results are linked to implications for decision making with sensitivity analyses to illustrate how alternate scenarios can be compared.
Condensing and distilling results makes results more usable.
Chapters include screenshots to make it easy to conduct analyses in Excel 2007 with time-saving shortcuts expected in the business world.
PivotTables and PivotCharts, used frequently in businesses, are introduced from the start.
Monte Carlo simulation is introduced early, as a tool to illustrate the range of possible outcomes from decision makers’ assumptions and underlying uncertainties.
Model building with regression is presented as a process, adding levels of sophistication, with chapters on multicollinearity and remedies, forecasting and model validation, autocorrelation and remedies, indicator variables to represent segment differences, and seasonality, structural shifts or shocks in time series models.
Special applications in market segmentation and portfolio analysis are offered, and an introduction to conjoint analysis is included.
Nonlinear models are motivated with arguments of diminishing or increasing marginal response, and a chapter on logit regression models market share or proportions.
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