Political analysis using R / James E. Monogan III.
Material type: TextSeries: Use R!Publication details: Cham : Springer, 2015.Description: xiii, 242 pages : illustrations (some color) ; 24 cmISBN:- 9783319234458
- 000SB:320 23 M751
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Books | ISI Library, Kolkata | 000SB:320 M751 (Browse shelf(Opens below)) | Available | 137493 |
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000SB:320 D262 Political statistics | 000SB:320 K44 Primer of statistics for political scientists | 000SB:320 M123 Political game theory | 000SB:320 M751 Political analysis using R / | 000SB:320 T238 Mathematics and politics | 000SB:320 T238 Mathematics and politics | 000SB:324.601 L336 Voting and collective decision-making |
Includes bibliographical references and index.
1. Obtaining R and Downloading Packages --
2. Loading and Manipulating Data --
3. Visualizing Data --
4. Descriptive Statistics --
5. Basic Inferences and Bivariate Association --
6. Linear Models and Regression Diagnostics--
7. Generalized Linear Models --
8. Using Libraries to Apply Advanced Models --
9. Time Series Analysis --
10. Linear Algebra with Programming Applications --
11. Additional Programming Tools.
The book provides a narrative of how R can be useful for addressing problems common to the analysis of public administration, public policy, and political science data specifically, in addition to the social sciences more broadly. While the book uses data drawn from political science, public administration, and policy analyses, it is written so that students and researchers in other fields should find it accessible and useful as well. Political Analysis Using R is perfect for the first-time R user who has no prior knowledge about the program. By working through the first seven chapters of this book, an entry-level user should be well acquainted with how to use R as a traditional econometric software program. These chapters explain how to install R, open and clean data, draw graphs, compute descriptive statistics, conduct bivariate inferences, and estimate common models such as linear and logistic regression. This portion of the book is ideal for undergraduate students, graduate students, or professionals trying to learn R in their spare time. This book also can be useful for an intermediate R user wishing to develop additional skills within the program. The last four chapters of the book introduce the user to advanced techniques that R offers but many other programs do not make available. Topics in these l ast chapters include: using user-contributed packages, conducting time series analysis, conducting matrix algebra, and writing programs in R.
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