| 1. | Bivariate analysis can be helpful in testing simple hypotheses of association.
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| 2. | Like univariate analysis, bivariate analysis can be descriptive or inferential.
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| 3. | In other words, all constraints are bivariate or conditional bivariate.
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| 4. | In other words, all constraints are bivariate or conditional bivariate.
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| 5. | Bivariate mapping shows the geographical distribution of two distinct sets of data.
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| 6. | Not all such bivariate distributions show regression towards the mean under this definition.
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| 7. | This distribution has been extended to the bivariate case.
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| 8. | The problem I am doing is in connection with the bivariate normal distribution.
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| 9. | Bivariate mapping is a comparatively recent graphical method.
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| 10. | Note that \ Phi is the cumulative distribution function of the bivariate normal distribution.
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