A scientist is calibrating a laboratory apparatus that will be used to measure the concentration of ozone in air samples. To check the calibration, samples of known concentration are measured. The true concentrations in ppm (x) and the measured concentrations in ppm (y) data points are: (0, 1), (10, 11), (20, 21), (30, 28), (40, 37), (50, 48), (60, 56), (70, 68), (80, 75), (90, 86), (100, 96). Because of random error, repeated measurements on the same sample will vary. The apparatus is considered to be in calibration if its mean response is equal to the true concentration. To check the calibration, the linear model y = ß0 + ß1 + ε is fit. Ideally, the value of ß0 should be 0 and the value of ß1 should be 1. The least-squares estimate of the error standard deviation σ is closest to:
A scientist is calibrating a laboratory apparatus that will be used to measure the concentration of ozone in air samples. To check the calibration, samples of known concentration are measured. The true concentrations in ppm (x) and the measured concentrations in ppm (y) data points are: (0, 1), (10, 11), (20, 21), (30, 28), (40, 37), (50, 48), (60, 56), (70, 68), (80, 75), (90, 86), (100, 96). Because of random error, repeated measurements on the same sample will vary. The apparatus is considered to be in calibration if its mean response is equal to the true concentration. To check the calibration, the linear model y = ß0 + ß1 + ε is fit. Ideally, the value of ß0 should be 0 and the value of ß1 should be 1.
The least-squares estimate of the error standard deviation σ is closest to:
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