Applied regression modeling / Iain Pardoe, Thompson Rivers University, The Pennsylvania State University.

By: Pardoe, Iain, 1970- [author.]
Language: English Publisher: Hoboken, NJ : John Wiley & Sons, Inc., 2021Copyright date: ©2021Edition: Third editionDescription: 1 online resource (xxi, 310 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781119615866; 9781119615903; 1119615909; 9781119615880; 1119615887; 9781119615941; 1119615941; 1119615860; 9781119615866Subject(s): Regression analysis | StatisticsGenre/Form: Electronic books.Additional physical formats: Print version:: Applied regression modelingDDC classification: 519.5/36 LOC classification: QA278.2 | .P363 2021Online resources: Full text is available at Wiley Online Library. Click here to view.
Contents:
Foundations -- Simple linear regression -- Multiple linear regression -- Regression model building I -- Regression model building II -- Notation and formulas -- Bibliography -- Glossary -- Index.
Summary: "This book provides a concise treatment of statistical regression modelling together with three detailed case studies for undergraduate students and professional master students with limited background in calculus. All sections have been retained from the second edition, with the author clarifying sections and reworking the more challenging topics. The author has added more end-of-chapter exercises and has added coverage of several new topics. All methods are clearly illustrated using datasets that are available in a variety of formats on the book's supplemental website. Detailed instructions for applying the methods are provided for many software packages, including SPSS, Minitab, DataDesk, SAS, JMP, R, Eviews, Stata, Statistica, and Excel.The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting.The writing is clear and careful, avoiding overly technical language. There is extensive cross-referencing, an exhaustive index, an appendix with all notation and formulas collected together, a mathematics refresher, a list of references, and a glossary of terms. Illustrations, graphs, and computer software output appears throughout. Detailed exercises collected together at the end of each chapter are carefully chosen to illustrate concepts and aid understanding. Brief solutions for half of the exercises are provided in an appendix while complete solutions to all the exercises are available in an Instructor's Manual. Supplemental exercises are available on the book's website. In addition, Instructor and student resources are available on the book's website, including datasets, software information, presentation slides, supplemental exercises, and videos covering particular topics and software demonstrations"-- Provided by publisher.
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519.536 P2143 2021 (Browse shelf) Available (In Process)
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Includes bibliographical references and index.

Foundations -- Simple linear regression -- Multiple linear regression -- Regression model building I -- Regression model building II -- Notation and formulas -- Bibliography -- Glossary -- Index.

"This book provides a concise treatment of statistical regression modelling together with three detailed case studies for undergraduate students and professional master students with limited background in calculus. All sections have been retained from the second edition, with the author clarifying sections and reworking the more challenging topics. The author has added more end-of-chapter exercises and has added coverage of several new topics. All methods are clearly illustrated using datasets that are available in a variety of formats on the book's supplemental website. Detailed instructions for applying the methods are provided for many software packages, including SPSS, Minitab, DataDesk, SAS, JMP, R, Eviews, Stata, Statistica, and Excel.The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting.The writing is clear and careful, avoiding overly technical language. There is extensive cross-referencing, an exhaustive index, an appendix with all notation and formulas collected together, a mathematics refresher, a list of references, and a glossary of terms. Illustrations, graphs, and computer software output appears throughout. Detailed exercises collected together at the end of each chapter are carefully chosen to illustrate concepts and aid understanding. Brief solutions for half of the exercises are provided in an appendix while complete solutions to all the exercises are available in an Instructor's Manual. Supplemental exercises are available on the book's website. In addition, Instructor and student resources are available on the book's website, including datasets, software information, presentation slides, supplemental exercises, and videos covering particular topics and software demonstrations"-- Provided by publisher.

Description based on online resource; title from digital title page (viewed on March 01, 2021).

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