So, essentially, the linear correlation coefficient (Pearson’s r) is just the standardized slope of a simple linear regression line (fit).

Summary. The slope-intercept form of a line is: y=mx+b where m is the slope and b is the y-intercept. The y-intercept is always where the line intersects the y-axis, and will always appear as (0,b) in coordinate form.

Use the linear equation 6y = 6x + 5 as an example. Divide both sides by 6: y = x + (5/6), where the slope is 1 and the y-intercept is (5/6) or point (0,5/6). Convert a fractional y-intercept to decimal form to make it easier to graph. Divide the numerator by the denominator: 5 / 6 = 0.833… or 0.83 (rounded).

If the slope of the line is positive, then there is a positive linear relationship, i.e., as one increases, the other increases. If the slope is negative, then there is a negative linear relationship, i.e., as one increases the other variable decreases.

The slope is defined as the ratio of the vertical change between two points, the rise, to the horizontal change between the same two points, the run. The slope of a line is usually represented by the letter m. (x1, y1) represents the first point whereas (x2, y2) represents the second point.

Differences. The value of the correlation indicates the strength of the linear relationship. The value of the slope does not. The slope interpretation tells you the change in the response for a one-unit increase in the predictor.

The slope of a least squares regression can be calculated by m = r(SDy/SDx). In this case (where the line is given) you can find the slope by dividing delta y by delta x. So a score difference of 15 (dy) would be divided by a study time of 1 hour (dx), which gives a slope of 15/1 = 15.

A negative slope means that two variables are negatively related; that is, when x increases, y decreases, and when x decreases, y increases. Graphically, a negative slope means that as the line on the line graph moves from left to right, the line falls.