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  2. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals .

  3. APA style - Wikipedia

    en.wikipedia.org/wiki/APA_style

    APA style (also known as APA format) is a writing style and format for academic documents such as scholarly journal articles and books. It is commonly used for citing sources within the field of behavioral and social sciences , including sociology, education, nursing, criminal justice, anthropology, and psychology.

  4. List of statistical tests - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_tests

    Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1]The choice of the test depends on many properties of the research question.

  5. Degrees of freedom (statistics) - Wikipedia

    en.wikipedia.org/wiki/Degrees_of_freedom...

    Here, the degrees of freedom arises from the residual sum-of-squares in the numerator, and in turn the n − 1 degrees of freedom of the underlying residual vector {¯}. In the application of these distributions to linear models, the degrees of freedom parameters can take only integer values. The underlying families of distributions allow ...

  6. 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]

  7. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

  8. Helen H. Hobbs, M.D. - Pay Pals - The Huffington Post

    data.huffingtonpost.com/paypals/helen-h-hobbs-m-d

    From December 2011 to December 2012, if you bought shares in companies when Helen H. Hobbs, M.D. joined the board, and sold them when she left, you would have a 23.0 percent return on your investment, compared to a 15.3 percent return from the S&P 500.

  9. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.

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