Case Study 3 CS3-1 Deregulation of the Intrastate Trucking Industry 1... CS3-2 Deregulation of the Intrastate Trucking Industry a.. The interval plot for lnprice with carriers shows tha
Trang 1Case Study 3 CS3-1
Deregulation of the Intrastate Trucking
Industry
1 Deregulated for x3= 1
2 4
ˆ 12.192 598 00598 01078 086 00014 677 275 026
.013 782 0399 021 0033
11.41 5581 02698 01408 086 0014 677 275
.026 01
x x
− + 3x x x1 2 4
Regulated for x3= 0
1 2 4
ˆ 12.192 598 00598 01078 086 00014 677 275 026
.013
x x x
+
For x4= 0, x2 = 15, y ˆregulated− y ˆderegulated = 1.097 0096 + x1
2 Deregulated y ˆ = 12.5632 − 086 x12
Regulated y ˆ = 11.5712 − 8439 x1+ 086 x12
The difference between the regulated and deregulated prices is given by
regulated deregulated 1
6 5
4 3
2 1
0
12.5
12.0
11.5
11.0
10.5
10.0
9.5
DISTANCE
0 1 Regulation
Scatterplot of Predicted Value of LNPRICE vs DISTANCE
Case Study
3
Trang 2CS3-2 Deregulation of the Intrastate Trucking Industry
a The interval plot for lnprice with carriers shows that carrier B is significantly different from the other carriers
CARRIER_A CARRIER_B CARRIER_D
1 0
1 0
1 0
1 0 1 0 1 0 1 0
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
11.2 11.1 11.0 10.9 10.8 10.7 10.6 10.5 10.4
Interval Plot of y-LNPRICE
95% CI for the Mean
MINTAB results shown below indicate that there is a difference in the carriers
The regression equation is
LNPRICE = 11.9 - 0.287 DISTANCE - 0.0326 WEIGHT + 0.180 ORIGIN_MIA
Predictor Coef SE Coef T P
Constant 11.8980 0.0608 195.79 0.000
DISTANCE -0.28700 0.01674 -17.14 0.000
WEIGHT -0.032593 0.002660 -12.25 0.000
ORIGIN_MIA 0.17980 0.04651 3.87 0.000
S = 0.489209 R-Sq = 51.0% R-Sq(adj) = 50.7%
PRESS = 108.478 R-Sq(pred) = 49.99%
Analysis of Variance
Source DF SS MS F P
Regression 3 110.635 36.878 154.09 0.000
Residual Error 444 106.261 0.239
Total 447 216.895
If we let
5
6
7
1 if Carrier A
0 else
1 if Carrier C
0 else
1 if Carrier D
0 else
x
x
x
⎧
= ⎨
⎩
⎧
= ⎨
⎩
⎧
= ⎨
⎩
and add interaction terms for each of these dummy variables
(except with x12 and x22), the model becomes
( )
E y = β + β x + β x + β x x + β x + β x + β x + β x + β x x + β x x
+ β12 2 3x x + β13 2 4x x + β15 1 2 3x x x + β16 1 2 4x x x
+ β17 5x + β18 1 5x x + β19 2 5x x + β20 1 2 5x x x + β21 3 5x x + β22 4 5x x + β23 1 3 5x x x
+ β24 1 4 5x x x + β25 2 3 5x x x + β26 2 4 5x x x + β27 1 2 3 5x x x x + β28 1 2 4 5x x x x
+ β29 6x + … β40 1 2 4 6x x x x + β41 7x + + … β52 1 2 4 7x x x x
Trang 3Case Study 3 CS3-3
In running a partial least squares procedure in MINITAB with the above model, the optimal model obtained had the same variables as in Model 7
y_LNPRICE = 12.2 - 0.567 x1_DISTANCE - 0.0167 x2_WEIGHT - 0.373 x3_dereg
+ 0.600 x4_origin + 0.0748 x1_Sq + 0.000349 x2_Sq - 0.00754 x1x2
+ 0.0077 x1x3 - 0.224 x1x4 - 0.0093 x2x3 - 0.0263 x2x4
+ 0.00111 x1x2x3 + 0.00864 x1x2x4
However, if variable x5 is defined as a dummy variable for Carrier B, with x5 = x6 = x7 = 0 denoting Carrier A, then the added terms in the model for Carrier B are significant