ABX Delivery provides the service across all the states in Australia. Marketing manager of this company wants to identify key factors that affect the time to unload a truck. A random sample of 50 deliveries was observed following data were reported. Time to unload a truck (in minutes), total number of cartons and the total weight (in hundreds of Kilograms). Following tables shows the regression output of the sample data set. SUMMARY OUTPUT Regression Statistics Multiple R 0.836420803 R Square 0.699599759 Adjusted R Square 0.68681677 Standard Error 8.823384264 Observations 50 ANOVA df SS MS F Significance F Regression 2 8521.530836 4260.765 54.72897 0.000000 Residual 47 3659.049164 77.85211 Total 49 12180.58 Coefficients Standard Error t Stat P-value Intercept -13.669 7.829028389 -1.74599 0.087346 Cartons 0.5172 0.067246763 7.691119 0.000000 Weight 0.2901 0.11166803 2.597671 0.012494 1.Determine the multiple regression equation 2. Develop hypothesis and assess the independent variables significance at 5% level? 3. How well does the model fit the data? Propose minimum of 2 new explanatory variables to the model and discuss the implication of OLS assumptions in regression analysis
ABX Delivery provides the service across all the states in Australia. Marketing manager of this company wants to identify key factors that affect the time to unload a truck. A random sample of 50 deliveries was observed following data were reported.
Time to unload a truck (in minutes),
total number of cartons and
the total weight (in hundreds of Kilograms).
Following tables shows the regression output of the sample data set.
|
SUMMARY OUTPUT |
|
|
Regression Statistics |
|
|
Multiple R |
0.836420803 |
|
R Square |
0.699599759 |
|
Adjusted R Square |
0.68681677 |
|
Standard Error |
8.823384264 |
|
Observations |
50 |
|
ANOVA |
|||||
|
|
df |
SS |
MS |
F |
Significance F |
|
Regression |
2 |
8521.530836 |
4260.765 |
54.72897 |
0.000000 |
|
Residual |
47 |
3659.049164 |
77.85211 |
||
|
Total |
49 |
12180.58 |
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
|
Intercept |
-13.669 |
7.829028389 |
-1.74599 |
0.087346 |
|
Cartons |
0.5172 |
0.067246763 |
7.691119 |
0.000000 |
|
Weight |
0.2901 |
0.11166803 |
2.597671 |
0.012494 |
1.Determine the multiple regression equation
2. Develop hypothesis and assess the independent variables significance at 5% level?
3. How well does the model fit the data? Propose minimum of 2 new explanatory variables to the model and discuss the implication of OLS assumptions in regression analysis.
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