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College --Undergrad Upper Division

  • Nonparametric test for the significance of the difference among the distributions of k correlated samples (A, B, etc., each of size n) involving repeated measures or matched sets. As the page opens, you will be prompted to enter the value of n. The necessary rank- ordering of your raw data will be performed automatically.

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  • Nonparametric test for the significance of the difference among the distributions of k correlated samples (A, B, etc., each of size n) involving repeated measures or matched sets. As the page opens, you will be prompted to enter the value of n. The necessary rank- ordering of your raw data will be performed automatically.

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  • As the page opens, you will be prompted to enter the sizes of your several samples. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.

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  • As the page opens, you will be prompted to enter the sizes of your several samples. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.

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  • In the Latin Square computational pages on this site, the third IV, with levels designated as A, B, C, etc., is listed as the "treatment" variable. The analysis of variance within an orthogonal Latin Square results in three F-ratios: one for the row variable, one for the column variable, and one for the third IV whose j levels are distributed orthogonally among the cells of the rows x columns matrix.

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  • In the Latin Square computational pages on this site, the third IV, with levels designated as A, B, C, etc., is listed as the "treatment" variable. The analysis of variance within an orthogonal Latin Square results in three F-ratios: one for the row variable, one for the column variable, and one for the third IV whose j levels are distributed orthogonally among the cells of the rows x columns matrix.

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  • This page has two calculators. One will cacluate a simple logistic regression, while the other calculates the predicted probability and odds ratio. There is also a brief tutorial covering logistic regression using an example involving infant gestational age and breast feeding. Please note, however, that the logistic regression accomplished by this page is based on a simple, plain-vanilla empirical regression.

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  • This page will calculate the intercorrelations (r and r2) for up to five variables, designated as A, B, C, D, and E.

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  • This page will calculate the intercorrelations (r) for any number of variables (V1, V2, V3, etc.) and for any number of observations per variable.

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  • The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples. A two-sample t-test (Pooled and Satterthwaite) is performed for k = 2. An ANOVA test is performed for k > 2. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Data.html
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