Guide to Multiple Regression
Autor Samia Challalen Limba Engleză Hardback – 30 dec 2025
Basics in inferential statistics and basics in a programming language are necessary prerequisites to engage well with the content. We believe that using a computer is the only practical means of performing a linear or multilinear regression on a data set of even moderate size. Therefore, our purpose is to make these connections between theory, technology, and applications, as clear as possible.
Features
- Focused summaries of the main statistical methods, followed by solved questions
- Integration of R as a calculator, and as a programming language in solving each question with ease and accuracy
- Use of simple coding that illustrates the connection to the theory
- Suggest progressively alternative codes
- Visualization and interpretation of the R outputs
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Specificații
ISBN-13: 9781041018919
ISBN-10: 1041018916
Pagini: 240
Ilustrații: 72
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.6 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
ISBN-10: 1041018916
Pagini: 240
Ilustrații: 72
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.6 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Public țintă
Postgraduate and Undergraduate AdvancedCuprins
List of Figures List of Tables Preface 1 Simple linear probabilistic model 2 Multi-linear regression 3 Non-Basic Regression Appendix: Q-Q plot Appendix: Residual Plots Bibliography Index
Notă biografică
Samia Challal is an associate professor at Glendon College; the bilingual campus of York University. She teaches courses in Analysis, Algebra, and Statistics. Her research draws on partial differential equations applied to fluid dynamics, optimization, homogenization problems, and mathematics education. She received her PhD from the University of Metz, France.
Descriere
This book is intended to equip the reader with concept knowledge, on multilinear regression methods, and develop technical skills, while solving problems with the R programming language. It is rather an incentive to do research on advanced models in data analysis once confidence is gained with the practice of some methods.