Simple linear regression is a sort of regression analysis in which there are only one independent variable and the independent(x) and dependent(y) variables have a linear relationship. In the graph above, the red line is referred to as the best fit straight line.
The most significant benefit of linear regression models is their linearity: It simplifies the estimating process and, more crucially, these linear equations have an easy-to-understand modular interpretation (i.e. the weights).
A straight line is used in linear regression models, while a curved line is used in logistic and nonlinear regression models. You can use regression to predict how a dependent variable will change as the independent variable(s) change. The link between two quantitative variables is estimated using simple linear regression.
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