Introduction to Simple Linear Regression Models

This chapter describes the case of simple linear regression model (SLRM), which allows people to establish a linear relationship between two quantitative variables. In the simple regression model, one of the variables is identified as the response or dependent variable (X), while the second is called the predictor, explanatory, or independent variable (Y). The first step in establishing a possible linear relationship between two variables is through constructing a scatterplot. The trend line of the association between X and Y through the linear regression model can be displayed as a linear equation, in the simplest relationship. The Pearson correlation coefficient is an index indicating the degree of linear association between two continuous random variables. The chapter aims to interpret the results of a SLRM generated by the statistical software STATA and apply a simple linear regression model to study a problem in public health.