Time Series Analysis in the Social Sciences
The Fundamentals
Nonfiction, Social & Cultural Studies, Social Science, Statistics, Crimes & Criminals, Criminology
| Author: | Youseop Shin | ISBN: | 9780520966383 |
| Publisher: | University of California Press | Publication: | January 31, 2017 |
| Imprint: | University of California Press | Language: | English |
| Author: | Youseop Shin |
| ISBN: | 9780520966383 |
| Publisher: | University of California Press |
| Publication: | January 31, 2017 |
| Imprint: | University of California Press |
| Language: | English |
Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and residuals, to the evaluation and prediction of estimated models. The book also explains smoothing, multiple time series analysis, and interrupted time series analysis. With a wealth of practical advice and supplemental data sets wherein students can apply their knowledge, this flexible and friendly primer is suitable for all students in the social sciences.
Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and residuals, to the evaluation and prediction of estimated models. The book also explains smoothing, multiple time series analysis, and interrupted time series analysis. With a wealth of practical advice and supplemental data sets wherein students can apply their knowledge, this flexible and friendly primer is suitable for all students in the social sciences.
