Section 10.2 Simple Linear Regression Vs. Multiple Linear Regression
Linear regression analysis is commonly used to examine the relationship between one continuous dependent variable and a set of independent variables. Simple linear regression involves examining the linear relationship between the dependent variable and a single independent variable. Conversely, multiple linear regression entails analyzing the impact of multiple independent variables on a dependent variable in the linear relationships.
Subsection 10.2.1 Ordinary Least Squares (OLS) Model
It is important to note that various types of linear regression models exist, but we will focus on the Ordinary Least Squares (OLS) regression model in this chapter because it is the most widely used. OLS is a statistical estimation technique for determining a regression equation that best represents the relationship between the dependent and independent variables. This method calculates the slope and intercept by minimizing the sum of squared differences between observed and predicted values. Other statistical estimation methods, such as maximum likelihood, are available for establishing a regression model.
