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When plotted in the manner described above, the value of the y-intercept (at = / =) will correspond to (), and the slope of the line will be equal to /. The values of y-intercept and slope can be determined from the experimental points using simple linear regression with a spreadsheet .
In other words, F is proportional to the logarithm of x times the slope of the straight line of its lin–log graph, plus a constant. Specifically, a straight line on a lin–log plot containing points ( F 0 , x 0 ) and ( F 1 , x 1 ) will have the function:
The slope of the SCL is the security's beta, and the intercept is its alpha. [2] ... Security characteristic line calculator;
Specifically, a straight line on a log–log plot containing points (x 0, F 0) and (x 1, F 1) will have the function: = (/) (/), Of course, the inverse is true too: any function of the form = will have a straight line as its log–log graph representation, where the slope of the line is m.
A 1 and A 2 are regression coefficients (indicating the slope of the line segments); K 1 and K 2 are regression constants (indicating the intercept at the y-axis). The data may show many types or trends, [2] see the figures. The method also yields two correlation coefficients (R):
The x and y coordinates of the point of intersection of two non-vertical lines can easily be found using the following substitutions and rearrangements. Suppose that two lines have the equations y = ax + c and y = bx + d where a and b are the slopes (gradients) of the lines and where c and d are the y-intercepts of the lines.
Figure 2 gives an example; in this example, the two x-intercepts differ by about 0.2 mL but this is a small discrepancy, given the large equivalence volume (0.5% error). Similar equations can be written for the titration of a weak base by strong acid (Gran, 1952; Harris, 1998).
Given the two red points, the blue line is the linear interpolant between the points, and the value y at x may be found by linear interpolation.. In mathematics, linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.