An article in Wood Science and Technology ["Creep in Chipboard, Part 3: Initial Assessment of the Influence of Moisture Content and Level of Stressing on Rate of Creep and Time to Failure" (1981, Vol. 15, pp. 125-144)] reported a study of the deflection (mm) of particleboard from stress levels of relative humidity. Assume that the two variables are related according to the simple linear regression model. The data follow: 68 61 61 Deflection, y (mm) 16.473 18.69314.305 15.121 13.505 11.640 11.168 12.534 11.224 Stress Level, x (%) 54 54 68 75 75 75 How much variability (in %) in data does the fitted linear regression model capture?

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An article in Wood Science and Technology ["Creep in Chipboard, Part 3: Initial Assessment of the
Influence of Moisture Content and Level of Stressing on Rate of Creep and Time to Failure" (1981, Vol.
15, pp. 125-144)] reported a study of the deflection (mm) of particleboard from stress levels of relative
humidity. Assume that the two variables are related according to the simple linear regression model.
The data follow:
Stress Level, x (%) 54
75
Deflection, y (mm) 16.473 18.69314.305 15.121 13.505 11.640 11.168 12.53411.224
54
61
61
68
68
75
75
How much variability (in %) in data does the fitted linear regression model capture?
Transcribed Image Text:An article in Wood Science and Technology ["Creep in Chipboard, Part 3: Initial Assessment of the Influence of Moisture Content and Level of Stressing on Rate of Creep and Time to Failure" (1981, Vol. 15, pp. 125-144)] reported a study of the deflection (mm) of particleboard from stress levels of relative humidity. Assume that the two variables are related according to the simple linear regression model. The data follow: Stress Level, x (%) 54 75 Deflection, y (mm) 16.473 18.69314.305 15.121 13.505 11.640 11.168 12.53411.224 54 61 61 68 68 75 75 How much variability (in %) in data does the fitted linear regression model capture?
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