Understanding Regression Analysis: An Introductory Guide presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style. It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. The Second Edition features updated examples and new references to modern software output.
Multiple Linear Regression
In Chapter 1, variations in the dependent variable were attributed to changes in only a single independent variable. This is known as simple (or bi-variate) linear regression. Yet theories very frequently suggest that several factors simultaneously affect a dependent variable. Multiple linear regression analysis is a method for estimating the effects of several factors concurrently.
There are numerous occasions where the use of multiple regression analysis is appropriate. In economics it has been used to estimate how the demand for gasoline responds to changes in prices and income (Arzaghi and Squalli 2015). The likelihood that an unemployed individual will double up (share living arrangements) with family or friends depends on many factors, including the ...