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  2. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    The formulas given in the previous section allow one to calculate the point estimates of α and β — that is, the coefficients of the regression line for the given set of data. However, those formulas do not tell us how precise the estimates are, i.e., how much the estimators α ^ {\displaystyle {\widehat {\alpha }}} and β ^ {\displaystyle ...

  3. Arrhenius plot - Wikipedia

    en.wikipedia.org/wiki/Arrhenius_plot

    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 .

  4. Log–log plot - Wikipedia

    en.wikipedia.org/wiki/Log–log_plot

    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.

  5. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Many statistical inference procedures for linear models require an intercept to be present, so it is often included even if theoretical considerations suggest that its value should be zero. Sometimes one of the regressors can be a non-linear function of another regressor or of the data values, as in polynomial regression and segmented regression.

  6. Theil–Sen estimator - Wikipedia

    en.wikipedia.org/wiki/Theil–Sen_estimator

    The fit line is then the line y = mx + b with coefficients m and b in slopeintercept 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 ...

  7. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  8. Security characteristic line - Wikipedia

    en.wikipedia.org/wiki/Security_characteristic_line

    1 Formula. 2 See also. 3 References. ... The slope of the SCL is the security's beta, and the intercept is its alpha. [2] Formula

  9. 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.