Introduction to Linear Regression Analysis
Introduction to Linear Regression Analysis, written by Douglas C. Montgomery, is a widely-used textbook that provides a comprehensive introduction to the theory and practice of linear regression analysis. The book is designed for students and researchers in a variety of fields, including engineering, business, economics, and the social sciences, who want to understand and apply regression analysis in their work.
In the book's introduction, Montgomery begins by explaining the fundamental concepts of regression analysis, such as the relationship between a dependent variable and one or more independent variables. He also introduces the concept of the least squares method, which is used to estimate the parameters of a regression model. Montgomery then goes on to discuss the assumptions of linear regression, such as the normality of the errors and the linearity of the relationship between the variables.
The book covers a wide range of topics, including simple and multiple linear regression, model building and selection, hypothesis testing, confidence intervals, and prediction. The text is supplemented with numerous examples, exercises, and case studies that illustrate the concepts and techniques of regression analysis in real-world settings.
Overall, Introduction to Linear Regression Analysis is an excellent resource for anyone who wants to learn about regression analysis, from beginners to advanced practitioners. The book is written in a clear and accessible style, with an emphasis on practical applications and real-world examples. It is an essential reference for students, researchers, and practitioners in a wide range of fields.
Author: Sanford Weisberg
Link to buy: https://www.amazon.com/Applied-Linear-Regression-Sanford-Weisberg/dp/1118386086/
Ratings: 4.5 out of 5 stars (from 33 reviews)
Best Sellers Rank: #249,236 in Books
#65 in Statistics (Books)
#355 in Probability & Statistics (Books)
