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  2. Studentized residual - Wikipedia

    en.wikipedia.org/wiki/Studentized_residual

    On the other hand, the internally studentized residuals are in the range , where ν = n − m is the number of residual degrees of freedom. If t i represents the internally studentized residual, and again assuming that the errors are independent identically distributed Gaussian variables, then: [2]

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The difference between the height of each man in the sample and the observable sample mean is a residual. Note that, because of the definition of the sample mean, the sum of the residuals within a random sample is necessarily zero, and thus the residuals are necessarily not independent.

  4. Studentization - Wikipedia

    en.wikipedia.org/wiki/Studentization

    A simple example is the process of dividing a sample mean by the sample standard deviation when data arise from a location-scale family. The consequence of "Studentization" is that the complication of treating the probability distribution of the mean, which depends on both the location and scale parameters, has been reduced to considering a ...

  5. DFFITS - Wikipedia

    en.wikipedia.org/wiki/DFFITS

    Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots.

  6. Studentized range distribution - Wikipedia

    en.wikipedia.org/wiki/Studentized_range_distribution

    The studentized range distribution function arises from re-scaling the sample range R by the sample standard deviation s, since the studentized range is customarily tabulated in units of standard deviations, with the variable q = R ⁄ s. The derivation begins with a perfectly general form of the distribution function of the sample range, which ...

  7. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  8. Newman–Keuls method - Wikipedia

    en.wikipedia.org/wiki/Newman–Keuls_method

    To determine if there is a significant difference between two means with equal sample sizes, the Newman–Keuls method uses a formula that is identical to the one used in Tukey's range test, which calculates the q value by taking the difference between two sample means and dividing it by the standard error:

  9. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

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