An R companion for the third edition of the fundamentals of political science research
by Kellstedt Paul M. ; Whitten , Guy D.
Published by : Cambridge University Press (Cambridge) Physical details: xiv, 75p ill., includes index 28cm ISBN:9781108446037. Year: 2021| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
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Book (Long Loan)
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GU Main Campus General Stacks | Reference | JA 86 .K45 2021 (Browse shelf) | Available | 25011513 | |
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Book (Long Loan)
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GU Main Campus General Stacks | Reference | JA 86 .K45 2021 (Browse shelf) | Available | 25011514 | |
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Book (Long Loan)
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GU Main Campus General Stacks | Reference | JA 86 .K45 2021 (Browse shelf) | Available | 25011515 |
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| JA71.P48 2005 Insitutional Theory in Political Science | JA 86 .K45 2021 An R companion for the third edition of the fundamentals of political science research | JA 86 .K45 2021 An R companion for the third edition of the fundamentals of political science research | JA 86 .K45 2021 An R companion for the third edition of the fundamentals of political science research | JC319 .G595 2019 Global politics : | JC573.2.U6 H624 2016 Strangers in their own land : | JC585 .M74 1998 On liberty and other essays / |
An R Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of R using real political data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using R. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of R step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in R to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses
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