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Verlag: Chapman and Hall/CRC 2020-12-18, 2020
ISBN 10: 1439885389 ISBN 13: 9781439885383
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.A solutions manual for all exercises is available to qualified instructors at the books website at , and data sets and Rmd files for all case studies and exercises are available at the authors GitHub repo For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbPaperback. Zustand: new. Paperback. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.A solutions manual for all exercises is available to qualified instructors at the books website at , and data sets and Rmd files for all case studies and exercises are available at the authors GitHub repo For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbZustand: New. AuthorsPaul Roback is the Kenneth O. Bjork Distinguished Professor of Statistics and Data Science and Julie Legler is Professor Emeritus of Statistics at St. Olaf College in Northfield, MN. Both are Fellows.
Verlag: Chapman and Hall/CRC 2020-12-29, 2020
ISBN 10: 1439885389 ISBN 13: 9781439885383
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.A solutions manual for all exercises is available to qualified instructors at the books website at , and data sets and Rmd files for all case studies and exercises are available at the authors GitHub repo For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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In den WarenkorbZustand: New. 2020. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.