{"product_id":"time-series","title":"Time Series","description":"\u003cp\u003eThe goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.\u003c\/p\u003e\u003cp\u003eNumerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester\/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software.\u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003eRobert H. Shumway\u003c\/p\u003e is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the \u003ci\u003eJournal of Forecasting\u003c\/i\u003e and the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003cp\u003e. \u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003eDavid S. Stoffer\u003c\/p\u003e is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of \u003ci\u003eForecasting\u003c\/i\u003e, the \u003ci\u003eAnnals of Statistical Mathematics\u003c\/i\u003e, and the \u003ci\u003eJournal of Time Series Analysis\u003c\/i\u003e. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e and the \u003ci\u003eJournal of Business \u0026amp; Economic Statistics\u003c\/i\u003e\u003cp\u003e.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57310455791998,"sku":"NW9780367221096","price":68.4,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9780367221096.jpg?v=1778582604","url":"https:\/\/mediaplace.com\/products\/time-series","provider":"MediaPlace","version":"1.0","type":"link"}