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The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. = = The following is Yates's corrected version of Pearson's chi-squared statistics:
Electronic questionnaires can also be created in the Form Designer module. Individual questions can be placed anywhere on a page and each form may contain multiple pages. The user is given a high degree of control over the form's appearance and function. The user defines both the question's prompt and the format of the data that is to be collected.
One-way Two-way MANOVA GLM Mixed model Post-hoc Latin squares; ADaMSoft: Yes Yes No No No No No Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes No No Yes Yes No BMDP: Yes Yes Yes Yes Yes Yes Epi Info: Yes Yes No No No No No EViews: Yes GAUSS: No No No No No GenStat: Yes Yes Yes Yes Yes Yes Yes GraphPad Prism: Yes Yes No Yes Yes Yes No gretl ...
Unlike spreadsheets, the data cells can only contain numbers or text, and formulas cannot be stored in these cells. The 'Variable View' displays the metadata dictionary, where each row represents a variable and shows the variable name, variable label, value label(s), print width, measurement type, and a variety of other characteristics.
In place of a named cell, an alternative approach is to use a cell (or grid) reference. Most cell references indicate another cell in the same spreadsheet, but a cell reference can also refer to a cell in a different sheet within the same spreadsheet, or (depending on the implementation) to a cell in another spreadsheet entirely, or a value ...
The basic technique of compact letter display is to label variables by one or more letters, so that variables are statistically indistinguishable if and only if they share at least one letter. The problem of doing so, using as few distinct letters as possible can be represented combinatorially as the problem of computing an edge clique cover of ...
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The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.