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1 The intercept estimates how much the response changes on

1. The intercept estimates how much the response changes on average with changes in the predictor.

2. The estimated value ŷ = b0 + b1 x approximates the average value of the response when the explanatory variable equals x.

3. The horizontal distance between y and ŷ is known as the residual and so takes its scale from the predictor.

4. The sum of the fitted value ŷ plus the residual e is equal to the original data value y.

5. The plot of the residuals on the predictor should show a linear pattern, with the data packed along a diagonal line.

6. Regression predictions become less reliable as we extrapolate farther from the observed data.

2. The estimated value ŷ = b0 + b1 x approximates the average value of the response when the explanatory variable equals x.

3. The horizontal distance between y and ŷ is known as the residual and so takes its scale from the predictor.

4. The sum of the fitted value ŷ plus the residual e is equal to the original data value y.

5. The plot of the residuals on the predictor should show a linear pattern, with the data packed along a diagonal line.

6. Regression predictions become less reliable as we extrapolate farther from the observed data.

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