It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible.
The adjective simple refers to the fact that the outcome variable is related to a single predictor. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. In statistics, simple linear regression is a linear regression model with a single explanatory variable. Here the dependent variable (GDP growth) is presumed to be in a linear relationship with the changes in the unemployment rate. Okun's law in macroeconomics is an example of the simple linear regression.
Calculator f simple linear regression series#
Linear regression model with a single explanatory variable Part of a series on
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