000			02797nam a22001817a 4500
020			_a978-1461473800
082			_a658.4033 FRA
100			_aFraser, Cynthia
245			_a Business statistics for competitive advantage with Excel 2013 : basics, model building simulation and cases
250			_a3rd Ed.,
260			_aUSA _bSpringer _c2013
300			_axiv,449p. _bPbk
500			_aAlpha/2423/Rs.6015/-
520			_aExceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios. To create these advantages, managers need to understand how to use statistics to provide information on performance response under alternative scenarios. This updated edition ofthe populartext 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 2013 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 emerging markets in Latin America, China and India. Results are linked to implications for decision making with sensitivity analyses to illustrate how alternate scenarios can be compared. Chapters include screenshots to make it easy to conduct analyses in Excel 2013 with time-saving shortcuts expected in the business world. PivotTables and PivotCharts, used frequently in businesses, are introduced from the start. The Third Edition features Monte Carlo simulation in three chapters, 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.
650			_aMathematical statistics _aCommercial statistics _aDecision making--Mathematical models _aMicrosoft Excel (Computer file)
856			_uhttp://www.springer.com/in/book/9781461473800
942			_cBK
999			_c103235 _d103235