The parameter (the regression coefficient) indicates the degree to which the line slopes upwards or downwards by indicating the amount by which a change in x must be multiplied by the corresponding average change in y, or the amount by which y changes for each unit increase in x.
Understanding Linear Regression Coefficients, A positive coefficient indicates that the mean of the dependent variable tends to rise in tandem with an increase in the independent variable's value. A negative coefficient indicates that the dependent variable tends to decrease as the independent variable rises.
In a regression model, the statistical measure known as R-Squared (also known as R2 or the coefficient of determination) is used to figure out how much of the variance in the dependent variable can be explained by the independent variable. In other words, the goodness of fit, or r-squared, measurement indicates how well the data fit the regression model.
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