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[center]![[Obrazek: 2598b73d0866506f83523d6b8c6215d6.jpg]](https://i126.fastpic.org/big/2025/1225/d6/2598b73d0866506f83523d6b8c6215d6.jpg)
English | 2025 | ISBN: 1041018916 | 241 pages | True PDF,EPUB | 12.24 MB[/center]
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
English | 2025 | ISBN: 1041018916 | 241 pages | True PDF,EPUB | 12.24 MB[/center]
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
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