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Analysis of covariance (ANCOVA)
It is initially used for an expansion of the analysis of variance which permits to the possible effects of continuous concomitant variables (such as covariates) on the response variable, additionally to the effects of the factor or the treatment variables. Generally supposed that covariates are unaffected by the treatments and that their relationship to the response is linear in nature. If such a relationship exists then the inclusion of covariates in this means decreases the error mean square and thus increases the sensitivity of the F-tests used in assessing the treatment differences. The term now seems to also be more generally used for almost any of the analysis seeking to assess the relationship among response variable and a number of the explanatory variables
Agreement The degree to which different observers, raters or diagnostic the tests agree on the binary classification. Measures of agreement like that of the kappa coefficient qu
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X 110 120 130 120 140 135 155 160 165 155 Y 12 18 20 15 25 30 35 20 25 10
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