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  2. Standard normal table - Wikipedia

    en.wikipedia.org/wiki/Standard_normal_table

    In statistics, a standard normal table, also called the unit normal table or Z table, [1] is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution.

  3. Standard score - Wikipedia

    en.wikipedia.org/wiki/Standard_score

    Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.

  4. Normal score - Wikipedia

    en.wikipedia.org/wiki/Normal_score

    The term normal score is used with two different meanings in statistics. One of them relates to creating a single value which can be treated as if it had arisen from a standard normal distribution (zero mean, unit variance). The second one relates to assigning alternative values to data points within a dataset, with the broad intention of ...

  5. Z-test - Wikipedia

    en.wikipedia.org/wiki/Z-test

    Because of the central limit theorem, many test statistics are approximately normally distributed for large samples.Therefore, many statistical tests can be conveniently performed as approximate Z-tests if the sample size is large or the population variance is known.

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  7. Michelle Obama Skips Jimmy Carter's Funeral and Is 'Still in ...

    www.aol.com/michelle-obama-skips-jimmy-carters...

    The former first lady was notably absent from President Jimmy Carter's state funeral service, leading Barack Obama and Donald Trump to be seated next to one another

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  9. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).