{"title":"Jeff Gill","description":"\u003cp\u003eJeff Gill's works delve into the rigorous application of statistical methods within the realm of social science, offering readers a deep understanding of Bayesian approaches and their practical implications. His texts provide a clear exploration of complex quantitative techniques, making them essential for scholars and practitioners interested in advanced data analysis.\u003c\/p\u003e\n\n\u003cp\u003eRooted firmly in the category of \u003cem\u003eScience \u0026amp; Nature\u003c\/em\u003e, Gill's publications blend theoretical insights with real-world examples, inviting readers to engage critically with the statistical foundations that support social research. His writing is both precise and accessible, appealing to those seeking to deepen their methodological expertise.\u003c\/p\u003e","products":[{"product_id":"bayesian-social-science-statistics-volume-2-by-jeff-gill-9781009598446","title":"Bayesian Social Science Statistics: Volume 2","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003eThis Element introduces the basics of Bayesian regression modeling using modern computational tools. \u003cem\u003eBayesian Social Science Statistics: Volume 2\u003c\/em\u003e assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024).\u003c\/p\u003e\n\n\u003cp\u003eSome matrix algebra knowledge is assumed, but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process, readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions.\u003c\/p\u003e\n\n\u003cp\u003eThe sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":47933891182828,"sku":"9781009598446","price":213.0,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/9781009598446-bayesian-social-science-statistics-volume-2.jpg?v=1783881734"},{"product_id":"bayesian-social-science-statistics-volume-2-by-jeff-gill-9781009340977","title":"Bayesian Social Science Statistics: Volume 2","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003eThis Element introduces the basics of Bayesian regression modeling using modern computational tools. \u003cem\u003eBayesian Social Science Statistics: Volume 2\u003c\/em\u003e only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024).\u003c\/p\u003e\n\n\u003cp\u003eSome matrix algebra knowledge is assumed, but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process, readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions.\u003c\/p\u003e\n\n\u003cp\u003eThe sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":47933891215596,"sku":"9781009340977","price":76.0,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/9781009340977-bayesian-social-science-statistics-volume-2.jpg?v=1783881752"}],"url":"https:\/\/bookhero.pro\/collections\/jeff-gill.oembed","provider":"Book Hero","version":"1.0","type":"link"}