Regression Analysis

After a relationship between paired data, which is referred to as bivariate
data has been discovered, one can model the relationship with an equation. One method of determining a linear relationship for bivariate data is called linear regression.
In linear regression, we assume that a change in 𝑥 (independent variable)
will lead directly to a change in 𝑦 (dependent variable). Sometimes, we are
interested in predicting the value of 𝑦 from the value of 𝑥. Generally, it is not logical to believe that 𝑦 caused 𝑥. By convention, we plot the independent variable along the horizontal axis, or 𝑥-axis, and the dependent variable along the vertical axis, or 𝑦-axis.
Furthermore, simple linear regression is similar to correlation in that the
The purpose is to measure the extent to which two variables have a linear relationship. In particular, linear regression aims to "predict" the value of the dependent variable based on the values of one or more independent variables. The relationship is summarized by a regression equation consisting of a slope and an intercept. The slope represents the amount the dependent variable increases or decreases with unit increase or decrease in the independent variable, and the intercept indicates the value of the dependent variable when the independent variable takes the value zero.