An outcome that is caused by multiple variables, wherein the relationship between those variables is not clear, is said to be ______.

Question

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If an outcome is caused by multiple variables, but the relationship between those variables is not clear, it is said to be overdetermined. This means that there are too many possible explanations for the outcome, and it is difficult to determine which variables are most important. Oversampling refers to collecting more data than is necessary, while overfitting refers to creating a model that is too complex and fits the data too closely. Overcompensation refers to a situation where someone or something tries too hard to make up for a perceived or real deficiency. None of these concepts accurately describe an outcome caused by multiple variables with an unclear relationship.

An Outcome That Is Caused by Multiple Variables, Wherein the Relationship