Q# Which model - the single explanatory variable linear model or the multiple explanatory variable linear model - explains more of the variation in travel time for this sample of drivers? I need help for this part.

Multiple linear regression results:

Dependent Variable: Travel Time (minutes)

Independent Variable(s): Distance Traveled (miles), Number of Deliveries

Travel Time (minutes) = 2.8424328 + 3.972593 Distance Traveled (miles) + 27.087275 Number of Deliveries

Parameter estimates:

Parameter

Estimate

Std. Err.

Alternative

DF

T-Stat

P-value

Intercept

2.8424328

53.455847

0

17

0.053173469

0.9582

Distance Traveled (miles)

3.972593

0.42970343

0

17

9.2449646

<0.0001

Number of Deliveries

27.087275

8.5109593

0

17

3.1826348

0.0054

Analysis of variance table for multiple regression model:

Source

DF

SS

MS

F-stat

P-value

Model

2

119399.34

59699.672

44.958841

<0.0001

Error

17

22573.856

1327.8739

Total

19

141973.2

Summary of fit:

Root MSE: 36.440004

R-squared: 0.841

R-squared (adjusted): 0.8223

a.Which model - the single explanatory variable linear model or the multiple explanatory variable linear model - worked better for Driver #6? Justify your answer.

The multiple explanatory variable linear model worked better for Driver #6 because prediction value is closer to driver's actual travel time.(This is what I think for multiple explanatory variable , but getting stuck for above question).