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  2. Log–log plot - Wikipedia

    en.wikipedia.org/wiki/Log–log_plot

    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.

  3. Slope - Wikipedia

    en.wikipedia.org/wiki/Slope

    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.

  4. Direct linear plot - Wikipedia

    en.wikipedia.org/wiki/Direct_linear_plot

    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.

  5. Semi-log plot - Wikipedia

    en.wikipedia.org/wiki/Semi-log_plot

    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:

  6. Line (geometry) - Wikipedia

    en.wikipedia.org/wiki/Line_(geometry)

    In two dimensions, the equation for non-vertical lines is often given in the slopeintercept form: = + where: m is the slope or gradient of the line. b is the y-intercept of the line. x is the independent variable of the function y = f(x).

  7. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    where = / and is known as the intercept (it is the vertical intercept or y-intercept of the line = +), and = / (inverse scale parameter or rate parameter): these are the y-intercept and slope of the log-odds as a function of x.

  8. Linear interpolation - Wikipedia

    en.wikipedia.org/wiki/Linear_interpolation

    Linear interpolation on a data set (red points) consists of pieces of linear interpolants (blue lines). Linear interpolation on a set of data points (x 0, y 0), (x 1, y 1), ..., (x n, y n) is defined as piecewise linear, resulting from the concatenation of linear segment interpolants between each pair of data points.

  9. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    In this case, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these variables. The intercept of the fitted line is such that the line passes through the center of mass ( x , y ) of the data points.

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