Order the following steps in evaluating the sensitivity of a predictor from the initial data assessment to the final interpretation of results:

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Seekh · Accepted Answer

First, look at the data to see what it contains, its size, and any missing values; this tells you whether the predictor can be used reliably. Next, choose the predictor you want to test and fit a model that uses it, so you have a baseline to compare against. Then, systematically change the predictor’s value or its weight to see how the model’s predictions change, which is the core of sensitivity analysis. After you run those changes, record how much the output shifts; large shifts mean the predictor is very sensitive, while small shifts mean it is robust. Finally, explain what the changes mean for the real world—whether the predictor is trustworthy enough for decisions or if you need to adjust the model.

Order the following steps in evaluating the sensitivity of a predictor from the initial data assessment to the final interpretation of results:

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