1. Often the response variable may not be continuous but rather discrete. 2. One complication is how to best deal with the response variable . 3. However, these assumptions are inappropriate for some types of response variables . 4. Measurements of patient deaths and harm are often used as response variables . 5. Is the " i " th predicted value of the response variable . 6. The form of the distribution assumed for the response variable y, is very general. 7. Such implementations also allow use of truncated distributions and censored ( or interval ) response variables . 8. However, it is not equivariant under affine transformations of both the predictor and response variables . 9. This requirement then implies that one must first specify the distribution of the response variables observed. 10. The logarithm of the expected value of the response variable is a linear combination of the explanatory variables.
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