We have data on 143 brown bears and we construct a model to predict weight based

Question

We have data on 143 brown bears and we construct a model to predict weight based on head length and width, neck girth, length of the body, and chest girth. Partial regression output is shown below.

The regression equation is Weight = - 274 - 4.23 Head.L - 0.51 Head.W + 7.06 Neck.G + 0.807 Length + 8.98 Chest.G
Predictor Coef SE Coef t P Constant –273.71 18.83 –14.53 0.000 Head.L –4.231 2.810 –1.51 0.134 Head.W –0.510 2.891 –0.18 0.860 Neck.G 7.056 1.460 4.83 0.000 Length 0.8069 0.6312 1.28 0.203 Chest.G 8.9758 0.8655 10.37 0.000

According to this information, which variables do NOT significantly contribute to predicting a bear's weight? All of the variables contribute. Neck girth and chest girth do not contribute. Head length and width and body length do not contribute. We have data on 143 brown bears and we construct a model to predict weight based on head length and width, neck girth, length of the body, and chest girth. Partial regression output is shown below.

The regression equation is Weight = - 274 - 4.23 Head.L - 0.51 Head.W + 7.06 Neck.G + 0.807 Length + 8.98 Chest.G
Predictor Coef SE Coef t P Constant –273.71 18.83 –14.53 0.000 Head.L –4.231 2.810 –1.51 0.134 Head.W –0.510 2.891 –0.18 0.860 Neck.G 7.056 1.460 4.83 0.000 Length 0.8069 0.6312 1.28 0.203 Chest.G 8.9758 0.8655 10.37 0.000

According to this information, which variables do NOT significantly contribute to predicting a bear's weight? All of the variables contribute. Neck girth and chest girth do not contribute. Head length and width and body length do not contribute.

Explanation / Answer

The p - value for Head length and width and body length is large. Hence, these variables do not contribute.

Option C is correct.

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