FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title Linear Regression Using R: An Introduction to Data Modeling
 Author(s) David J. Lilja
 Publisher: University of Minnesota Libraries Publishing (2022)
 License(s): CC BYNC 4.0
 Paperback: N/A
 eBook: PDF
 Language: English
 ISBN10: N/A
 ISBN13: 9781946135001
 Share This:
Book Description
This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed stepbystep process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
About the AuthorsN/A
 The R Programming Language
 Statistics, Mathematical Statistics, and SAS Programming
 Data Analysis and Data Mining
 Geographic Information System (GIS) and Web Mapping
 Linear Regression Using R: An Introduction to Data Modeling (David J. Lilja)
 The Mirror Site (1)  HTML and PDF
 The Mirror Site (2)  HTML

Beyond Multiple Linear Regression: Linear and Multilevel Models
This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes nonnormal responses and correlated structure.

Statistical Inference: Algorithms, Evidence, and Data Science
A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twentyfirst century. Every aspiring data scientist should carefully study this book, use it as a reference.

Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

Statistical Inference for Everyone (Brian S Blais)
Approaching an introductory statistical inference textbook in a novel way, this book walks through a simple introduction to probability, and then applies those principles to all problems of inference.

R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

Tidyverse Skills for Data Science (Carrie Wright, et al)
This book is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages, covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

Tidy Modeling with R: A Framework for Modeling in the Tidyverse
This book shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work. It demonstrate ways to create models by focusing on an R dialect called the Tidyverse.

R Programming for Data Science (Roger D. Peng)
This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

An Introduction to R (Alex Douglas, et al.)
The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.

R Packages: Organize, Test, Document, and Share Your Code
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

Advanced R Programming (Hadley Wickham)
This book presents useful tools and techniques for attacking many types of R programming problems. It not only helps current R users become R programmers but also shows existing programmers what's special about R.

Exploratory Data Analysis with R (Roger D. Peng)
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

Regression Models for Data Science in R (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

Advanced Data Analysis using R (Cosma R. Shalizi)
This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. All examples implemented using R.

Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

Data Engineering Cookbook: The Plumbing of Data Science
This is a practical and comprehensive guide. You will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.

Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

Data Science: Theories, Models, Algorithms, and Analytics
It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

The Data Science Handbook: Advice and Insights
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

Python Data Science Handbook: Essential Tools (Jake VanderPlas)
Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all  IPython, NumPy, Pandas, Matplotlib, ScikitLearn, and other related tools.
:






















