Question
he trucking example we discussed this week has a regression equation shown below. y^=0.1273+0.0672x1+0.6900x2 The following shows part of the regression output for the Trucking
he trucking example we discussed this week has a regression equation shown below. y^=0.1273+0.0672x1+0.6900x2
The following shows part of the regression output for the Trucking company example discussed in this week.
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 0.1273 | 0.205203 | 0.620541 | 0.535378 | -0.2764999 | 0.531174204 |
Miles | 0.0672 | 0.002455 | 27.36551 | 3.54E-83 | 0.06235038 | 0.072013099 |
Deliveries | 0.6900 | 0.029521 | 23.37309 | 2.85E-69 | 0.63190133 | 0.748095234 |
What conclusion can you reach using the p-value 3.54E-83?
Group of answer choices
there is a linear relationship between miles and total time traveled
there is not a linear relationship between miles and total time traveled
there is a linear relationship between the number of deliveries and total time traveled
there is not a linear relationship between the number of deliveries and total time traveled
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