Business statistics for competitive advantage with Excel 2013 : basics, model building, simulation and sases

Exceptional 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. Th...

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Gorde:
Xehetasun bibliografikoak
Egile nagusia: Fraser, Cynthia
Formatua: Livre numérique
Hizkuntza:Anglais
Argitaratua: New York, NY : Springer New York 2013.
Cham : Springer Nature
Sarrera elektronikoa:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Oharra: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Business Statistics for Competitive Advantage with Excel 2013, Basics, Model Building, Simulation and Cases, Cynthia Fraser., 3rd edition, New York, Springer Science+Business Media, 2013, 1 vol. (XIV; 450 p), 978-1-4614-7380-0
• Business Statistics for Competitive Advantage with Excel 2013, Texte imprimé, 9781461473824
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245 1 0 |a Business statistics for competitive advantage with Excel 2013 :  |b basics, model building, simulation and sases   |c by Cynthia Fraser. 
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505 0 |a Statistics for Decision Making and Competitive Advantage -- Describing Your Data Chapter -- Hypothesis Tests, Confidence Intervals, and Simulation to Infer Population Characteristics and Differences -- Quantifying the Influence of Performance Drivers and Forecasting: Regression -- Market Simulation and Segmentation with Descriptive Statistics, Inference, Hypothesis Tests, and Regression -- Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression -- Association between Two Categorical Variables: Contingency Analysis with Chi Square -- Building Multiple Regression Models -- Model Building and Forecasting with Multicollinear Time Series -- Indicator Variables -- Nonlinear Multiple Regression Models -- Indicator Interactions for Segment Differences or Changes in Response -- Logit Regression for Bounded Responses -- Index. 
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520 |a Exceptional 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 of the popular 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 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 inmarket 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. 
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