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  2. Peirce's criterion - Wikipedia

    en.wikipedia.org/wiki/Peirce's_criterion

    K. Thomsen identified that three parameters were needed to perform the calculation: the number of observation pairs (N), the number of outliers to be removed (n), and the number of regression parameters (e.g., coefficients) used in the curve-fitting to get the residuals (m).

  3. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    However, at 95% confidence, Q = 0.455 < 0.466 = Q table 0.167 is not considered an outlier. McBane [1] notes: Dixon provided related tests intended to search for more than one outlier, but they are much less frequently used than the r 10 or Q version that is intended to eliminate a single outlier.

  4. Extraneous and missing solutions - Wikipedia

    en.wikipedia.org/wiki/Extraneous_and_missing...

    This counterintuitive result occurs because in the case where =, multiplying both sides by multiplies both sides by zero, and so necessarily produces a true equation just as in the first example. In general, whenever we multiply both sides of an equation by an expression involving variables, we introduce extraneous solutions wherever that ...

  5. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  6. Grubbs's test - Wikipedia

    en.wikipedia.org/wiki/Grubbs's_test

    However, multiple iterations change the probabilities of detection, and the test should not be used for sample sizes of six or fewer since it frequently tags most of the points as outliers. [3] Grubbs's test is defined for the following hypotheses: H 0: There are no outliers in the data set H a: There is exactly one outlier in the data set

  7. Overdetermined system - Wikipedia

    en.wikipedia.org/wiki/Overdetermined_system

    The only cases where the overdetermined system does in fact have a solution are demonstrated in Diagrams #4, 5, and 6. These exceptions can occur only when the overdetermined system contains enough linearly dependent equations that the number of independent equations does not exceed the number of unknowns. Linear dependence means that some ...

  8. Sequoia’s Alfred Lin on the art and math of spotting outliers

    www.aol.com/finance/sequoia-alfred-lin-art-math...

    Alfred Lin, who just celebrated his 14th year at Sequoia Capital, talks about the frameworks he uses to identify outlier startup founders.

  9. Consistent and inconsistent equations - Wikipedia

    en.wikipedia.org/wiki/Consistent_and...

    The system + =, + = has exactly one solution: x = 1, y = 2 The nonlinear system + =, + = has the two solutions (x, y) = (1, 0) and (x, y) = (0, 1), while + + =, + + =, + + = has an infinite number of solutions because the third equation is the first equation plus twice the second one and hence contains no independent information; thus any value of z can be chosen and values of x and y can be ...