co
eletion matrix
PownE PownL Pcomp session weather unempl flights/wk canc/wk holiday wrecks TotlAD ADblbd ADonTV QE Q_length Age<25 26-50 51+ Q_length E_days age/weeks QE
PownE 1
PownL XXXXXXXXXX 1
Pcomp XXXXXXXXXX XXXXXXXXXX 1
session XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
weather XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
unempl XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
flights/wk XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
canc/wk XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
holiday XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
wrecks XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
TotlAD XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
ADblbd XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
ADonTV XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
QE XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
Q_length XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
Age<25 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
26-50 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
51+ XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
Q_length XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
E_days XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
age/weeks XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
QE XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 1
egression model
SUMMARY OUTPUT
Regression Statistics
Multiple R XXXXXXXXXX
R Square XXXXXXXXXX
Adjusted R Square XXXXXXXXXX
Standard E
or XXXXXXXXXX
Observations 52
ANOVA
df SS MS F Significance F
Regression 10 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 7.29168868361882E-21
Residual 41 XXXXXXXXXX XXXXXXXXXX
Total 51 XXXXXXXXXX
Coefficients Standard E
or t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
week XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
PownE XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
weather XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
canc/wk XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
holiday XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
TotlAD XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
Age<25 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
26-50 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 9.39061193087085E-17 XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
Q_length XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
age/weeks XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
I have conducted multiple linear regression model to forecast the demand of cars economy in the next four weeks. I have considered QE number of economy car contracts initiated each week as dependent variable. For independent variables, we have conducted co
elation matrix to test the association between the variables. We have chosen ten variables from co
elation matrix showing higher co
eletion. We have trained multiple linear regression using these variables. The adjusted r square value is 0.916, which implies 91.6 percent variability in dependent variable can explain with this model.
From Anova table output, F(10,41) = 57.23 and p value
0.05. Hence our regression model is statistically significant at five percent level of significance.
Independent variable like week, TotlAD, Age<25 and 26-50 are statistically significant to predict our dependent variable | p value <0.05. One unit increase in week will cause 0.09 time week increase in economy demand. Age segment age<25 , 26-50 has positive beta coefficient that means increase in these variables will cause significant increase on economy car demand.TotlAD has low beta coefficient but it's statistically significant to predict economy car demand.
Subset
week PownE weather canc/wk holiday TotlAD Age<25 26-50 Q_length age/weeks QE
1 29.99 4 9 0 430 9 64 334 XXXXXXXXXX 87
2 29.99 1 2 0 430 13 46 327 XXXXXXXXXX 76
3 24.99 2 3 0 430 20 51 315 XXXXXXXXXX 82
4 28.99 1 0 1 430 24 42 275 XXXXXXXXXX 77
5 24.99 0 0 0 430 20 51 316 XXXXXXXXXX 76
6 29.99 3 6 0 430 15 59 301 XXXXXXXXXX 78
7 28.99 1 0 0 815 14 55 355 XXXXXXXXXX 81
8 21.99 0 0 0 815 20 61 332 XXXXXXXXXX 91
9 26.76 0 0 0 815 20 40 260 XXXXXXXXXX 77
10 28.99 2 3 0 815 22 44 317 XXXXXXXXXX 84
11 25.99 0 0 0 2197 14 49 291 XXXXXXXXXX 76
12 25.99 0 2 0 2520 10 54 350 XXXXXXXXXX 75
13 25.99 0 0 0 1646 22 36 448 XXXXXXXXXX 68
14 24.99 0 0 0 815 38 38 481 XXXXXXXXXX 89
15 24.99 1 0 0 815 21 35 261 XXXXXXXXXX 68
16 23.99 0 0 0 815 16 32 227 XXXXXXXXXX 63
17 30.99 0 0 0 815 9 31 186 XXXXXXXXXX 52
18 24.99 1 3 0 815 20 64 405 XXXXXXXXXX 94
19 26.99 0 0 0 815 14 54 314 XXXXXXXXXX 78
20 25.99 0 0 0 1455 12 66 338 XXXXXXXXXX 87
21 26.99 0 0 0 4965 19 34 248 XXXXXXXXXX 70
22 29.99 2 1 0 4325 27 45 287 XXXXXXXXXX 86
23 29.99 0 1 0 4325 21 31 264 XXXXXXXXXX 68
24 29.99 0 0 0 4325 15 51 405 XXXXXXXXXX 84
25 24.99 0 0 0 4325 15 52 374 XXXXXXXXXX 86
26 28.99 0 2 0 4325 9 60 458 XXXXXXXXXX 84
27 24.99 0 0 1 4325 10 68 400 XXXXXXXXXX 92
28 29.99 0 0 0 4325 11 53 459 XXXXXXXXXX 81
29 28.99 0 0 0 4325 14 60 396 XXXXXXXXXX 85
30 27.99 0 0 0 4018 14 63 458 XXXXXXXXXX 89
31 29.99 0 0 0 6268 16 48 344 XXXXXXXXXX 80
32 26.99 1 0 0 4018 17 48 269 XXXXXXXXXX 80
33 30.99 0 3 0 4018 15 56 332 XXXXXXXXXX 85
34 31.99 0 0 0 4018 22 37 303 XXXXXXXXXX 77
35 30.99 0 0 0 4853 23 28 297 XXXXXXXXXX 67
36 27.99 0 0 1 3477 14 51 478 XXXXXXXXXX 81
37 28.99 0 0 0 4485 21 48 263 XXXXXXXXXX 84
38 29.99 0 0 0 2642 20 44 367 XXXXXXXXXX 78
39 30.99 0 0 0 2642 22 32 263 XXXXXXXXXX 71
40 30.99 0 0 0 2642 14 43 222 XXXXXXXXXX 77
41 26.99 0 0 0 508 9 63 279 XXXXXXXXXX 89
42 31.99 1 3 0 508 16 38 343 XXXXXXXXXX 71
43 34.99 0 0 0 1237 16 27 294 XXXXXXXXXX 61
44 28.99 0 0 0 1237 14 42 349 XXXXXXXXXX 72
45 25.99 0 0 0 3117 14 71 441 XXXXXXXXXX 101
46 34.99 0 0 0 1237 16 24 215 XXXXXXXXXX 52
47 25.99 2 2 1 1237 17 74 388 XXXXXXXXXX 103
48 28.99 0 2 0 1237 12 49 354 XXXXXXXXXX 75
49 27.99 2 4 0 3852 7 71 372 XXXXXXXXXX 95
50 34.99 0 0 0 1237 9 53 452 XXXXXXXXXX 73
51 34.99 1 0 0 1237 15 55 362 XXXXXXXXXX 89
52 26.99 0 0 1 1237 14 51 353 XXXXXXXXXX 82
FullSet
week PownE PownL Pcomp session weather unempl flights/wk canc/wk holiday wrecks TotlAD ADblbd ADonTV Age<25 26-50 51+ Q_length E_days age/weeks BedTax QE
1 29.99 37.99 37.75 0 4 701 41 9 0 22 430 430 0 9 64 14 334 3.839 50.3 $104,025.67 87
2 29.99 41.99 41.5 0 1 739 41 2 0 16 430 430 0 13 46 17 327 4.303 51.3 76
3 24.99 26.99 35.25 0 2 814 41 3 0 12 430 430 0 20 51 11 315 3.841 52.3 82
4 28.99 37.99 35.5 1 1 880 47 0 1 6 430 430 0 24 42 11 275 3.571 53.3 77
5 24.99 36.99 24.5 1 0 881 47 0 0 10 430 430 0 20 51 5 316 4.158 54.3 76
6 29.99 43.99 28.75 1 3 799 47 6 0 17 430 430 0 15 59 4 301 3.859 55.3 $70,251.75 78
7 28.99 44.99 34.5 1 1 857 47 0 0 20 815 815 0 14 55 12 355 4.383 56.3 81
8 21.99 25.99 33 1 0 871 47 0 0 4 815 815 0 20 61 10 332 3.648 57.3 91
9 26.76 48.99 29.5 1 0 870 47 0 0 12 815 815 0 20 40 17 260 3.377 58.3 77
10 28.99 42.99 38.25 1 2 889 47 3 0 19 815 815 0 22 44 18 317 3.774 59.3 $80,998.15 84
11 25.99 37.99 28 1 0 855 47 0 0 9 2197 815 0 14 49 13 291 3.829 60.3 76
12 25.99 37.99 30.25 1 0 911 48 2 0 4 2520 815 0 10 54 11 350 4.667 61.3 75
13 25.99 28.99 31.5 1 0 894 48 0 0 15 1646 815 0 22 36 10 448 6.588 62.3 68
14 24.99 38.99 28.5 0 0 909 48 0 0 5 815 815 0 38 38 13 481 5.404 63.3 $72,072.62 89
15 24.99 40.99 30.25 1 1 956 48 0 0 12 815 815 0 21 35 12 261 3.838 64.3 68
16 23.99 34.99 28.25 1 0 988 48 0 0 8 815 815 0 16 32 15 227 3.603 28.5 63
17 30.99 41.99 36 1 0 983 48 0 0 9 815 815 0 9 31 12 186 3.577 29.5 52
18 24.99 41.99 30.5 1 1 938 62 3 0 1 815 815 0 20 64 10 405 4.309 30.5 $83,166.36 94
19 26.99 41.99 31 1 0 939 62 0 0 1 815 815 0 14 54 10 314 4.026 31.5 78
20 25.99 45.99 32 1 0 948 62 0 0 3 1455 815 0 12 66 9 338 3.885 32.5 87
21 26.99 45.99 32.5 1 0 902 64 0 0 7 4965 815 3510 19 34 17 248 3.543 33.5 70
22 29.99 45.99 31 0 2 888 64 1 0 17 4325 815 3510 27 45 14 287 3.337 34.5 86
23 29.99 41.99 33.75 0 0 937 64 1 0 12 4325 815 3510 21 31 16 264 3.882 35.5 $92,470.99 68
24 29.99 41.99 31.25 1 0 953 64 0 0 12 4325 815 3510 15 51 18 405 4.821 36.5 84
25 24.99 41.99 32.5 1 0 983 58 0 0 8 4325 815 3510 15 52 19 374 4.349 37.5 86
26 28.99 40.99 34.75 1 0 988 58 2 0 9 4325 815 3510 9 60 15 458 5.452 38.5 84
27 24.99 46.99 33 1 0 995 58 0 1 11 4325 815 3510 10 68 14 400 4.348 39.5 $91,174.48 92
28 29.99 40.99 31.5 0 0 961 58 0 0 2 4325 815 3510 11 53 17 459 5.667 40.5 81
29 28.99 37.99 37.75 1 0 996 58 0 0 6 4325 815 3510 14 60 11 396 4.659 41.5 85
30 27.99 37.99 37.5 1 0 945 58 0 0 1 4018 508 3510 14 63 12 458 5.146 42.5 89
31 29.99 37.99 37.25 1 0 986 59 0 0 5 6268 508 5760 16 48 16 344 4.300 43.5 80
32 26.99 40.99 31 1 1 953 59 0 0 5 4018 508 3510 17 48 15 269 3.363 44.5 $182,486.48 80
33 30.99 39.99 37.25 0 0 989 59 3 0 6 4018 508 3510 15 56 14 332 3.906 45.5 85
34 31.99 46.99 38.25 0 0 1031 59 0 0 13 4018 508 3510 22 37 18 303 3.935 46.5 77
35 30.99 46.99 31.25 1 0 1042 59 0 0 5 4853 508 3510 23 28 16 297 4.433 47.5 67
36 27.99 38.99 32.25 1 0 1023 59 0 1 2 3477 508 2134 14 51 16 478 5.901 48.5 $56,038.77 81
37 28.99 40.99 37 1 0 1045 61 0 0 7 4485 508 2134 21 48 15 263 3.131 49.5 84
38 29.99 37.99 38.75 1 0 1065 61 0 0 11 2642 508 2134 20 44 14 367 4.705 50.5 78
39 30.99 41.99 37.75 1 0 1037 61 0 0 15 2642 508 2134 22 32 17 263 3.704 33.2 71
40 30.99 42.99 39.5 1 0 1052 61 0 0 11 2642 508 2134 14 43 20 222 2.883 34.2 $123,935.45 77
41 26.99 41.99 31 1 0 1055 61 0 0 12 508 508 9 63 17 279 3.135 35.2 89
42 31.99 38.99 31.25 1 1 1071 61 3 0 12 508 508 16 38 17 343 4.831 36.2 71
43 34.99 39.99 35 0 0 1104 61 0 0 11 1237 1237 16 27 18 294 4.820 37.2 61
44 28.99 40.99 35.75 1 0 1145 61 0 0 7 1237 1237 14 42 16 349 4.847 38.2 72
45 25.99 41.99 37.5 1 0 1157 61 0 0 6 3117 1237 1880 14 71 16 441 4.366 39.2 $99,591.43 101
46 34.99 46.99 31.5 1 0 1136 61 0 0 9 1237 1237 16 24 12 215 4.135 40.2 52
47 25.99 37.99 33.25 1 2 1140 61 2 1 15 1237 1237 17 74 12 388 3.767 41.2 103
48 28.99 42.99 39.5 1 0 1146 58 2 0 12 1237 1237 12 49 14 354 4.720 42.2 75
49 27.99 45.99 37 1 2 1156 58 4 0 14 3852 1237 2615 7 71 17 372 3.916 43.2 $70,942.70 95
50 34.99 40.99 30.5 1 0 1166 53 0 0 18 1237 1237 9 53 11 452 6.192 44.2 73
51 34.99 39.99 30 0 1 1175 53 0 0 21 1237 1237 15 55 19 362 4.067 45.2 89
52 26.99 41.99 35.25 0 0 1155 53 0 1 6 1237 1237 14 51 17 353 4.305 46.2 82
EE 17 TT. FM Ts A SRI SE A ims tbe 1 ie Te ae mn, fa Le al Laff al AE Ct deal he] ee YA LVN WEL ro Np Lm al Wr Nu I I fl me Tl Ca MSY MASS
As manager of an A+ Rental Cars franchise, your responsibilities include setting prices to achieve organizational goals.
Part 1:
Previously you determined the revenue-maximizing price based on your demand equation. Is maximizing revenue the most appropriate
objective for A+ Rental Cars? Explain. If not, recommend an alternative approach and justify your stance.
The accounting department at the corporate office has provided you with some historical cost data. Economy rentals cost $17.10 pe
day while luxury vehicles cost $25.12 per day. Regardless of vehicle type, a rental contract requires $8.90 for reconditioning. Additional
vehicles can be delivered from the regional hub at a cost of $35/vehicle, on top of the costs above. Do you have any concerns about
using this accounting data to set prices? Hint: Consider the difference between average and marginal cost.
In addition to answering the questions above, provide pricing recommendations for economy vehicles for the next four weeks.
Part 2:
Cu
ently A+ Rentals charges all customers the same daily rate. The lone exception to this rule is the $12.50 surcharge on customers
under 25 years of age (required by company policy). Should A+ Rental Cars consider alternative pricing strategies? For instance, is it
possible to increase revenue and profit by charging different customers different prices? If so, how would you approach and implement
these strategies?
A ; ; ;
A+ Rental Cars customers who choose to keep a rental for an extra day are paying the same base daily rate. Is it worth exploring
alternative ways to price extra days? If so, what changes would you make? Discuss the important considerations and tradeoffs involved
in this decision and explain your rationale.
SnsSsigRMEent O 1S a ll a i tl
You woke up this morning to a troubling advertisement on TV: A+ Rental Cars' local competitor is discounting their economy vehicles. After doing 2
little digging, you discover that your competitor has launched an aggressive advertising campaign, reducing the price on their economy line from ’
$32.99 to $24.99. Based on your knowledge of previous pricing practices, you expect a similar price reduction across all vehicle types. In your memo
or short business report, include answers to the following questions:
1 How will A+ Rental Cars' weekly revenue be affected by the price cut if you maintain the revenue maximizing price that you specified
previously? Hint: If you did not include Pcomp in your estimated demand curve, then perhaps you should revisit this decision. hk
5» Should A+ Rental Cars respond to the competition by reducing their price as well, or ignore the actions of the competitor and run the business as
usual? If you decide that a price cut is prefe
ed, how deep should the discount be? Can game theory be used to analyze this situation? Explain
your reasoning and methodology thoroughly. : :
3 It would be labor intensive to re-analyze your situation every time your competitor changed their prices. Is there a way to develop a formula that ee
would help you quickly pick a price in response to the price setting behavior of your competitor? Be as systematic as possible. A general, :
concise solution is ideal. Document the process yoX used to a
ive at the "formula".
4. The corporate leadership team inquired about the use of a "price match guarantee". This policy would mean A+ Rental Cars will match ou
competitor's price for a certain vehicle category if their price is lower than ours. Please comment on the viability of the this policy. Isita good o
idea? Why or Why not? he
5. If the price match policy is a good idea, how widely should it be advertised? Explain.