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The Sage dictionary of quantitative management research
Authors: ---
ISBN: 9781412935203 9781412935296 Year: 2010 Publisher: Thousand oaks, CA : Sage Publications,


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Statistical modeling for management
Authors: ---
ISBN: 1446220567 0761970126 9786612020612 0761970118 1282020617 1849202486 9781849202480 9781446220566 9780761970118 9780761970125 9781282020610 Year: 2008 Publisher: London : SAGE,

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Abstract

Bringing to life the most widely used quantitative measurements and statistical techniques in marketing, this text is packed with user-friendly descriptions, examples and study applications.

The multivariate social scientist : introductory statistics using generalized linear models
Authors: ---
ISBN: 0761952012 0761952004 9786612559761 1857021908 0857028073 1282559761 0857021907 9780857021908 9780857028075 9780761952015 9780761952008 Year: 1999 Publisher: London : SAGE,

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Abstract

The Multivariate Social Scientist is the first accessible introduction to multivariate analysis and techniques for students, researchers and social scientists who need to know how to use multivariate methods.


Book
The Sage dictionary of quantitative management research
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ISBN: 144625111X 1280299878 9786613554789 1446209601 Year: 2011 Publisher: Los Angeles, [Calif.] ; London : SAGE,

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A must-have reference resource for quantitative management researchers, this dictionary contains over 100 entries covering the fundamentals of qualitative methodologies; covering both analysis and implementation.

Advances in doctoral research in management
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ISBN: 9812560440 9786610837502 1280837500 9812707247 9789812707246 Year: 2006 Publisher: Singapore ; Hackensack, NJ : World Scientific,

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Abstract

"Difference Equations or Discrete Dynamical Systems is a diverse field which impacts almost every branch of pure and applied mathematics. Not surprisingly, the techniques that are developed vary just as broadly. No more so is this variety reflected than at the prestigious annual International Conference on Difference Equations and Applications. Organized under the auspices of the International Society of Difference Equations, the Conferences have an international attendance and a wide coverage of topics. The contributions from the conference collected in this volume invite the mathematical community to see a variety of problems and applications with one ingredient in common, the Discrete Dynamical System. Readers may also keep abreast of the many novel techniques and developments in the field. The special emphasis of the meeting was on mathematical biology and accordingly about half of the articles are in the related areas of mathematical ecology and mathematical medicine."


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Principal components analysis
Authors: --- --- --- --- --- et al.
ISBN: 1529749336 Year: 2020 Publisher: London : SAGE Publications Ltd.,

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Abstract

Principal component analysis (PCA) is a technique that essentially converts observed correlated variables into unobserved uncorrelated components. This enables a data set containing many individual variables to be described using a small number of components that capture much of the variation in the data set. PCA has a long history in statistics and has been applied in many disciplines including biology, astronomy, geography, social sciences, meteorology and management. In addition to reducing the number of variables required to describe a data set, PCA can also identify underlying mechanisms that may have played a role in determining the structure in the data (i.e., the underlying "Bcauses"). The reduction of a large number of variables to a relatively small number of components also enables a data set to be more easily analysed and described using other techniques. In particular, as the components identified by PCA are uncorrelated, many of the problems associated with multicollinearity are alleviated, enabling regression models to be more easily interpreted. This entry provides a relatively nontechnical and practical introduction to the application of PCA using a readily available data set and open-source software.

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