Calibration_regression

# Calibration_regression - Calibration Many Things Are Not...

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Calibration Many Things Are Not Absolute Calibration Graph line through center of distribution dN/N - normal distribution eqn. fraction of universe between x and (x + dx) is the probability that x will lie between x and (x + dx) Method of Least Squares It can be shown: best straight line through a series of experimental points: the line for which the sum of the squares of the deviations of the points from the line is a minimum quadratic summation allows for sign of deviation to be ignored

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Sum of Squares of Differences equation of line y = mx + b square of sum of differences, S is assumes no error in x - independent variable y l is the value on the line Minimize S by Differentiation Best straight line occurs when S goes through a minium Differentiate and set the derivatives of S wrt m and b equal to zero and solve for m and b The result is: where is the mean of all the values of x i and is the mean of all the values of y i x y Easier Form of Least Squares Equations n is the number of data points
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Calibration_regression - Calibration Many Things Are Not...

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