We expand readers' knowledge of linear regression with detailed explanations and applications of key models used in time series analysis.
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Suzanna Linn is Distinguished Professor of Political Science in the Department of Political Science at Penn State University. She is a Fellow of the Society for Political Methodology and its immediate past president. She serves as associate editor of the Society's journal Political Analysis. Linn has served on the APSA and Midwest Political Science Association Councils. Her innovative research in political methodology and behavior appears in leading journals. Her book The Decline of the Death Penalty (Cambridge, 2008) won the Kammerer Award for best book on US national policy.
Matthew J. Lebo is Professor of Political Science and Director of the Centre for Computational and Quantitative Social Science at Western University. His expertise is in political methodology and American politics. He has been a post-doctoral fellow at Harvard, Academic Visitor at Oxford, and a Visiting Professor at the University of Toronto and McGill University.
Clayton Webb is an associate professor of Political Science at the University of Kansas. He received his Ph.D. from Texas A&M University. His areas of expertise are Political Methodology and International Relations. His work has been featured in Political Analysis, the American Journal of Political Science, and International Studies Quarterly.
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Paperback. Condición: new. Paperback. Understanding change over time is a critical component of social science. However, data measured over time time series requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statistical analyses to a broad audience. Our applied guide is for students analysing data over time. For example, approval ratings, economic measures, and international conflicts. Time series data requires its own set of advanced statistical tools which we outline as simply as possible. Detailed examples help readers navigate the complicated process of learning from temporal data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781108407519
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Paperback. Condición: New. Understanding change over time is a critical component of social science. However, data measured over time - time series - requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statistical analyses to a broad audience. Nº de ref. del artículo: LU-9781108407519
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Paperback. Condición: new. Paperback. Understanding change over time is a critical component of social science. However, data measured over time time series requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statistical analyses to a broad audience. Our applied guide is for students analysing data over time. For example, approval ratings, economic measures, and international conflicts. Time series data requires its own set of advanced statistical tools which we outline as simply as possible. Detailed examples help readers navigate the complicated process of learning from temporal data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781108407519
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