Guide to Multiple Regression

Samia Challal

ISBN 10: 1041018916 / ISBN 13: 9781041018919

Published by Taylor and Francis Ltd, GB, 2025

About this Item

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. The goal is not to become an expert in both directions. It is rather an incentive to do research on advanced models in data analysis once confidence is gained with the practice of some methods.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.FeaturesFocused summaries of the main statistical methods, followed by solved questionsIntegration of R as a calculator, and as a programming language in solving each question with ease and accuracyUse of simple coding that illustrates the connection to the theorySuggest progressively alternative codesVisualization and interpretation of the R outputs.

Seller Inventory # LU-9781041018919

Bibliographic Details

• Title: Guide to Multiple Regression
• Publisher: Taylor and Francis Ltd, GB
• Publication Date: 2025
• Language: English
• ISBN 10: 1041018916
• ISBN 13: 9781041018919
• Binding: Hardback
• Condition: New
• Weight: 600 grams

About this title

Synopsis

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. The goal is not to become an expert in both directions. It is rather an incentive to do research on advanced models in data analysis once confidence is gained with the practice of some methods.

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

About the Author

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.