The standard error of the regression signifies the typical dimensions of the residuals.
On graphs, analysts spot independent variables on the horizontal X-axis. These variables are also known as predictor variables, input variables, and are frequently denoted using Xs.
In statistics, they differentiate between a simple and multiple linear regression. Usually, R Squared of 95% or more is considered a good fit. In other words, 91% of the dependent variables (y-values) are explained by the independent variables (x-values). It means that 91% of our values fit the regression analysis model. In our example, R2 is 0.91, which is fairy good.