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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 above procedure now is reversed to find the form of the function F(x) using its (assumed) known log–log plot. To find the function F, pick some fixed point (x 0, F 0), where F 0 is shorthand for F(x 0), somewhere on the straight line in the above graph, and further some other arbitrary point (x 1, F 1) on the same graph.
We can see that the slope (tangent of angle) of the regression line is the weighted average of (¯) (¯) that is the slope (tangent of angle) of the line that connects the i-th point to the average of all points, weighted by (¯) because the further the point is the more "important" it is, since small errors in its position will affect the ...
The best known plots of the Michaelis–Menten equation, including the double-reciprocal plot of / against /, [2] the Hanes plot of / against , [3] and the Eadie–Hofstee plot [4] [5] of against / are all plots in observation space, with each observation represented by a point, and the parameters determined from the slope and intercepts of the lines that result.
Slope illustrated for y = (3/2)x − 1.Click on to enlarge Slope of a line in coordinates system, from f(x) = −12x + 2 to f(x) = 12x + 2. The slope of a line in the plane containing the x and y axes is generally represented by the letter m, [5] and is defined as the change in the y coordinate divided by the corresponding change in the x coordinate, between two distinct points on the line.
The fit line is then the line y = mx + b with coefficients m and b in slope–intercept form. [12] As Sen observed, this choice of slope makes the Kendall tau rank correlation coefficient become approximately zero, when it is used to compare the values x i with their associated residuals y i − mx i − b. Intuitively, this suggests that how ...
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).
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