Sky Eyes manufactures drones for security and enforcement organizations. They would like predict pre regression model. The regression model is based on historic production values. The regression statistics from Excel are provided below. Intercept Machine Hours Labor Hours Production Output Coefficients -477.7647341 44.97381045 -0.08890915 55.06503861 Standard Error 62.63384 5.9814 t Stat -7.627901659 5.957718 7.518943592 0.083252 -1.067948403 9.242639029 P-value 1.20603E-09 1.74367E-09 0.291239564 5.77595E-12 What can be concluded based on the output of the regression statistics? (Select all that apply.) The regression equation is y- 44.97 x (machine hours) - 0.0889 x (labor hours) + 55.065 x (production output). Labor hours is significant variable in the regression model. Machine hours is a significant variable in the regression model. The regression equation is y=-477.76+44.97 x (machine hours) - 0.0889x (labor hours) + 55.065x (production output).

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Sky Eyes manufactures drones for security and enforcement organizations. They would like to predict production cost using a
regression model. The regression model is based on historic production values. The regression statistics from Excel are provided
below.
Intercept
Machine Hours
Labor Hours
Production Output
Coefficients
-477.7647341
44.97381045
-0.08890915
55.06503861
Standard Error
62.63384
-7.627901659
7.518943592
0.083252
-1.067948403
5.957718 9.242639029
t Stat
5.9814
P-value
1.20603E-09
1.74367E-09
0.291239564
5.77595E-12
What can be concluded based on the output of the regression statistics? (Select all that apply.)
The regression equation is y 44.97 x (machine hours) - 0.0889 x (labor hours) + 55.065 x (production output).
Labor hours is significant variable in the regression model.
Machine hours is a significant variable in the regression model.
The regression equation is y=-477.76+44.97 x (machine hours) - 0.0889x (labor hours) + 55.065x (production output).
Transcribed Image Text:Sky Eyes manufactures drones for security and enforcement organizations. They would like to predict production cost using a regression model. The regression model is based on historic production values. The regression statistics from Excel are provided below. Intercept Machine Hours Labor Hours Production Output Coefficients -477.7647341 44.97381045 -0.08890915 55.06503861 Standard Error 62.63384 -7.627901659 7.518943592 0.083252 -1.067948403 5.957718 9.242639029 t Stat 5.9814 P-value 1.20603E-09 1.74367E-09 0.291239564 5.77595E-12 What can be concluded based on the output of the regression statistics? (Select all that apply.) The regression equation is y 44.97 x (machine hours) - 0.0889 x (labor hours) + 55.065 x (production output). Labor hours is significant variable in the regression model. Machine hours is a significant variable in the regression model. The regression equation is y=-477.76+44.97 x (machine hours) - 0.0889x (labor hours) + 55.065x (production output).
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