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Asked Feb 3, 2020
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The following assumptions are made to set up a linear model. Explain in plain English what each assumption means and justify (according to your common sense as a data scientist) whether or not each assumption is reasonable for real data. 1. The underlying relationship between the response Yi and the predictor xi is linear. 2. E(i) = 0 3. var(i) = σ 2 4. i are uncorrelated.

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