Regression models are widely used as statistical technique for prediction the outcome based on observed data.
Linear regressions allows describe how dependent variable (outcome) changes relatively to independent variable(s) (feature, predictor).
When there is one independent variable and one dependent, it is called simple linear regression (SLR).
When there is more than one independent variable and one dependent, it is called multiple linear regression (MLR).
A simple linear regression equation looks like:
y = a + bx
x — the independent (explanatory) variable,
y — the dependent (responce) variable,
a — intercept,
b — slope of the line (coefficient).
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