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  2. PSPP - Wikipedia

    en.wikipedia.org/wiki/PSPP

    PSPP is a free software application for analysis of sampled data, intended as a free alternative for IBM SPSS Statistics. It has a graphical user interface [2] and conventional command-line interface. It is written in C and uses GNU Scientific Library for its mathematical routines. The name has "no official acronymic expansion". [3]

  3. Omnibus test - Wikipedia

    en.wikipedia.org/wiki/Omnibus_test

    The F-test in ANOVA is an example of an omnibus test, which tests the overall significance of the model. A significant F test means that among the tested means, at least two of the means are significantly different, but this result doesn't specify exactly which means are different one from the other.

  4. Dickey–Fuller test - Wikipedia

    en.wikipedia.org/wiki/Dickey–Fuller_test

    Which of the three main versions of the test should be used is not a minor issue. The decision is important for the size of the unit root test (the probability of rejecting the null hypothesis of a unit root when there is one) and the power of the unit root test (the probability of rejecting the null hypothesis of a unit root when there is not one).

  5. SPSS Modeler - Wikipedia

    en.wikipedia.org/wiki/SPSS_Modeler

    IBM SPSS Modeler is a data mining and text analytics software application from IBM.It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.

  6. Analysis of covariance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_covariance

    Variables in the model that are derived from the observed data are (the grand mean) and ¯ (the global mean for covariate ). The variables to be fitted are τ i {\displaystyle \tau _{i}} (the effect of the i th level of the categorical IV), B {\displaystyle B} (the slope of the line) and ϵ i j {\displaystyle \epsilon _{ij}} (the associated ...

  7. Phi coefficient - Wikipedia

    en.wikipedia.org/wiki/Phi_coefficient

    In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.

  8. Test statistic - Wikipedia

    en.wikipedia.org/wiki/Test_statistic

    The null hypothesis is that the curve fit is adequate. It is common to determine curve shapes to minimize the mean square error, so it is appropriate that the goodness-of-fit calculation sums the squared errors. F-tests (analysis of variance, ANOVA) are commonly used when deciding whether groupings of data by category are meaningful. If the ...

  9. Wilcoxon signed-rank test - Wikipedia

    en.wikipedia.org/wiki/Wilcoxon_signed-rank_test

    This requires that the data be on an ordered metric scale, a type of scale that carries more information than an ordinal scale but may have less than an interval scale. [6] The data for a one-sample test is a sample in which each observation is a real number: , …,. Assume for simplicity that the observations in the sample have distinct ...