# Tag Info

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This is not a problem of the t-test, but of any test in which the power of the test depends on the sample size. This is called "overpowering". And yes, changing the test to Mann-Whitney will not help. Therefore, apart from asking whether the results are statistically significant, you need to ask yourself whether the observed effect size is significant in ...

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There is nothing you can do without more data than just a list of pairs of numbers with some missing. It might be useful to really consider what getting all of these missing values might mean though. I'm doubtful you will gain much at all in going through the process of trying to make the imputation. Consider the mathematical impact of this on your standard ...

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All single-tree methods involve a staggering number of multiple comparisons that bring great instability to the result. That is why to achieve satisfactory predictive discrimination some form of tree averaging (bagging, boosting, random forests) is necessary (except that you lose the advantage of trees - interpretability). The simplicity of single trees is ...

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The skewness of the outcome variable (treated unconditionally on the other variables) will depend on the arrangement of the independent variables -- it might validly be anything. You shouldn't be trying to make the distribution of the outcome look like any particular thing. It's the error term the normal assumption is needed for. Normality of residuals ...

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If you concatenate all thirteen variables as 1-byte strings (or do the equivalent numerically with powers of 2), you can just run FREQUENCIES on this composite variable and sort the table in descending order. Recent versions of Statistics allow you to sort a table in the Viewer. For older versions or to create the table as a dataset, use OMS. Here is an ...

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First, perhaps automating regression analysis isn't the best of all ideas. As one author put it: "some procedures require alert human participation". Second, use Python instead of a macro. It's a million times more efficient. A tutorial that demonstrates regression over many dependent variables is found here. You may need to tweak it a bit, though.

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