Название: Regression Models for Data Science in R: Statistical inference for Data ScienceАвтор: Yassine Mousaif
Издательство: Independently published
Год: 2022
Страниц: 132
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB
What's Special about this Book: The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in "Statistical inference for Data Science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.
Regression models are the workhorse of Data Science. They are the most well described, practical and theoretically understood models in statistics. A data scientist well versed in regression models will be able to solve and incredible array of problems. Perhaps the key insight for regression models is that they produce highly interpretable model fits. This is unlike Machine Learning algorithms, which often sacrifice interpretability for improved prediction performance or automation. These are, of course, valuable attributes in their own rights. However, the benefit of simplicity, parsimony and intrepretability offered by regression models (and their close generalizations) should make them a first tool of choice for any practical problem.
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