https://datatab.net/tutorial/linear-regression
Simple linear regression is used to predict the value of a dependent variable based on an independent variable. The greater the linear relationship between the independent variable and the dependent variable, the more accurate is the prediction.
Visually, the relationship between the variables can be shown in a scatter plot. The greater the linear relationship between the dependent and independent variables, the more the data points lie on a straight line.
Simple Linear Regression Equation:
a : point of intersection with the y axis
b : gradient of the straight line
ŷ : is the respective estimate of the y-value. For each x-value the corresponding y-value is estimated.
If all points (measured values) were exactly on one straight line, the estimate would be perfect. However, this is almost never the case and therefore, in most cases a straight line must be found, which is as close as possible to the individual data points.
The attempt is thus made to keep the error in the estimation as small as possible so that the distance between the estimated value and the true value is as small as possible. This distance or error is called the "residual", is abbreviated as "e" (error) and can be represented by the greek letter epsilon (ϵ).
