Regression Analysis for the Social Sciences (Hardcover, 2nd edition)


Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: *interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. *thorough integration of teaching statistical theory with teaching data processing and analysis. *teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

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Product Description

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: *interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. *thorough integration of teaching statistical theory with teaching data processing and analysis. *teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

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Product Details

General

Imprint

Routledge

Country of origin

United Kingdom

Release date

March 2015

Availability

Expected to ship within 12 - 17 working days

First published

2015

Authors

Dimensions

235 x 187 x 33mm (L x W x T)

Format

Hardcover - Cloth over boards

Pages

22

Edition

2nd edition

ISBN-13

978-1-138-81053-2

Barcode

9781138810532

Categories

LSN

1-138-81053-3



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