In a regression, we attempt to fit a line through the points that best fits the . In its simplest form, this is accomplished by finding a line that minimizes the sum of the squared deviations of the points from the line. Consequently, it is called ordinary least squares (OLS) regression. When such a line is fit, two parameters emerge - one is the point at which the line cuts through the Y-axis, called the intercept of the regression, and the other is the slope of the regression line.

The slope (b) of the regression measures both the direction and the magnitude of the relationship between the dependent variable (Y) and the independent variable (X). When the two variables are positively correlated, the slope will also be positive, whereas when the two variables are negatively correlated, the slope will be negative. The magnitude of the slope of the regression can be read as follows - for every unit increase in the dependent variable (X), the independent variable will change by b (slope).

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