Verlag: Cambridge University Press, 2014
ISBN 10: 1107003148 ISBN 13: 9781107003149
Sprache: Englisch
Anbieter: Phatpocket Limited, Waltham Abbey, HERTS, Vereinigtes Königreich
EUR 27,63
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In den WarenkorbZustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Shows some signs of wear but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
Verlag: Cambridge University Press, 2017
ISBN 10: 1316622223 ISBN 13: 9781316622223
Sprache: Englisch
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 32,96
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In den WarenkorbZustand: New. pp. 392.
Verlag: Cambridge University Press, 2014
ISBN 10: 1107003148 ISBN 13: 9781107003149
Sprache: Englisch
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Verlag: Cambridge University Press, 2014
ISBN 10: 1107003148 ISBN 13: 9781107003149
Sprache: Englisch
Anbieter: GridFreed, North Las Vegas, NV, USA
hardcover. Zustand: New. In shrink wrap.
Verlag: Cambridge University Press, 2014
ISBN 10: 1107003148 ISBN 13: 9781107003149
Sprache: Englisch
Anbieter: Labyrinth Books, Princeton, NJ, USA
Zustand: Very Good.
Verlag: Cambridge University Press, 2017
ISBN 10: 1316622223 ISBN 13: 9781316622223
Sprache: Englisch
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 66,08
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In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 391 pages. 8.82x5.98x1.02 inches. In Stock.
Verlag: Cambridge University Press, 2017
ISBN 10: 1316622223 ISBN 13: 9781316622223
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 142,16
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 373 pages. 9.00x6.25x1.00 inches. In Stock.
Verlag: Cambridge University Press, 2014
ISBN 10: 1107003148 ISBN 13: 9781107003149
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.