http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
XIII, 242 p. 38 illus., 19 illus. in color. online resource.
Political Analysis Using R can serve as a textbook for undergraduate or graduate students as well as a manual for independent researchers. It is unique among competitor books in its usage of 21 example datasets that are all drawn from political research. All of the data and example code is available from the Springer website, as well as from Dataverse (http://dx.doi.org/10.7910/DVN/ARKOTI). 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.
Obtaining R and Downloading Packages -- Loading and Manipulating Data -- Visualizing Data -- Descriptive Statistics -- Basic Inferences -- Linear Models and Regression -- Diagnostics -- Generalized Linear Models -- Using Libraries to Apply Advanced Models -- Time Series Analysis -- Linear Algebra with Programming Applications -- Additional Programming Tools.
Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law