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> Statistical Modeling and Inference for Social Science

Statistical Modeling and Inference for Social Science

Authors

Sean Gailmard, University of California, Berkeley
Published 2014

Description

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…

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Key features

  • Integrates social science theory and statistical models
  • Focuses on statistical techniques as used by social scientists
  • Assumes no prior background in statistics
  • Provides extensive guidance to students on introductory reading in statistical literature

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