Using the data below, I need answers to the following questions: a) Using the data in Table 1, specify a linear functional form for the demand for Combination 1 meals, and run a regression to estimate the demand for Combo 1meals. b) Using statistical software (Excel), estimate the parameters of the empirical demand function specifiedin part a.Write your estimated industry demand equation. c) Evaluate your regression results by examining signs of parameters, p-values, and the R2. d) Discuss how the estimation of demand might beimproved. e) If the owner plans to charge a price of 4.15 for a Combination 1 meal and spend $18,000 per week on advertising, how many Combination 1 meals do you predict will be sold each week? f) If the owner spends $18,000 per week on advertising, write the equation for the inverse demand function. Then, calculate the demand price for 50,000 Combination meals. Estimation and Analysis of Demand for Fast Food Meals You work for PriceWatermanCoopers as a market analyst. PWC has been hired by the owner of two Burger King restaurants located in a suburban Atlanta market area to study the demand for its basic hamburger meal package–referred to as “Combination 1\" on its menus. The two restaurants face competition in the Atlanta suburb from five other hamburger restaurants (three MacDonald’s and two Wendy’s restaurants) and three other restaurants serving “drive-through” fast food (a Taco Bell, a Kentucky Fried Chicken, and a small family-owned Chinese restaurant). The owner of the two Burger King restaurants provides PWC with the data shown in Table 1. Q is the total number of Combination 1 meals sold at both locations during each week in 1998. P is the average price charged for a Combination 1 meal at the two locations. [Prices are identical at the two Burger King locations.] Every week the Burger King owner advertises special price offers at its two restaurants exclusively in daily newspaper advertisements. A is the dollar amount spent on newspaper ads for each week in 1998. The owner could not provide PWC with data on prices charged by other competing restaurants during 1998. For the one-year time period of the study, household income and population in the suburb did not change enough to warrant inclusion in the demand analysis. TABLE 1: Weekly Sales Data for Combination 1 Meals (1998) week Q P A week Q P A 1 51,345 2.78 4,280 27 78,953 2.27 21,225 2 50,337 2.35 3,875 28 52,875 3.78 7,580 3 86,732 3.22 12,360 29 81,263 3.95 4,175 4 118,117 1.85 19,250 30 67,260 3.52 4,365 5 48,024 2.65 6,450 31 83,323 3.45 12,250 6 97,375 2.95 8,750 32 68,322 3.92 11,850 7 75,751 2.86 9,600 33 71,925 4.05 14,360 8 78,797 3.35 9,600 34 29,372 4.01 9,540 9 59,856 3.45 9,600 35 21,710 3.68 7,250 10 23,696 3.25 6,250 36 37,833 3.62 4,280 11 61,385 3.21 4,780 37 41,154 3.57 13,800 12 63,750 3.02 6,770 38 50,925 3.65 15,300 13 60,996 3.16 6,325 39 57,657 3.89 5,250 14 84,276 2.95 9,655 40 52,036 3.86 7,650 15 54,222 2.65 10,450 41 58,677 3.95 6.

1. Using the data below, I need answers to the following questions:
a) Using the data in Table 1, specify a linear functional form for the demand for Combination 1
meals, and run a regression to estimate the demand for Combo 1meals.
b) Using statistical software (Excel), estimate the parameters of the empirical demand function
specifiedin part a.Write your estimated industry demand equation.
c) Evaluate your regression results by examining signs of parameters, p-values, and the R2.
d) Discuss how the estimation of demand might beimproved.
e) If the owner plans to charge a price of 4.15 for a Combination 1 meal and spend $18,000 per
week on advertising, how many Combination 1 meals do you predict will be sold each week?
f) If the owner spends $18,000 per week on advertising, write the equation for the inverse
demand function. Then, calculate the demand price for 50,000 Combination meals.
Estimation and Analysis of Demand for Fast Food Meals
You work for PriceWatermanCoopers as a market analyst. PWC has been hired by the owner of
two Burger King restaurants located in a suburban Atlanta market area to study the demand for
its basic hamburger meal package–referred to as “Combination 1" on its menus. The two
restaurants face competition in the Atlanta suburb from five other hamburger restaurants (three
MacDonald’s and two Wendy’s restaurants) and three other restaurants serving “drive-through”
fast food (a Taco Bell, a Kentucky Fried Chicken, and a small family-owned Chinese restaurant).
The owner of the two Burger King restaurants provides PWC with the data shown in Table 1. Q
is the total number of Combination 1 meals sold at both locations during each week in 1998. P is
the average price charged for a Combination 1 meal at the two locations. [Prices are identical at
the two Burger King locations.] Every week the Burger King owner advertises special price
offers at its two restaurants exclusively in daily newspaper advertisements. A is the dollar
amount spent on newspaper ads for each week in 1998. The owner could not provide PWC with
data on prices charged by other competing restaurants during 1998. For the one-year time period
of the study, household income and population in the suburb did not change enough to warrant
inclusion in the demand analysis.
TABLE 1: Weekly Sales Data for Combination 1 Meals (1998)
week Q P A week Q P A
1
51,345
2.78
4,280
27
78,953

2. 2.27
21,225
2
50,337
2.35
3,875
28
52,875
3.78
7,580
3
86,732
3.22
12,360
29
81,263
3.95
4,175
4
118,117
1.85
19,250
30
67,260
3.52
4,365
5
48,024
2.65
6,450
31
83,323
3.45
12,250
6
97,375

3. 2.95
8,750
32
68,322
3.92
11,850
7
75,751
2.86
9,600
33
71,925
4.05
14,360
8
78,797
3.35
9,600
34
29,372
4.01
9,540
9
59,856
3.45
9,600
35
21,710
3.68
7,250
10
23,696
3.25
6,250
36
37,833

4. 3.62
4,280
11
61,385
3.21
4,780
37
41,154
3.57
13,800
12
63,750
3.02
6,770
38
50,925
3.65
15,300
13
60,996
3.16
6,325
39
57,657
3.89
5,250
14
84,276
2.95
9,655
40
52,036
3.86
7,650
15
54,222

5. 2.65
10,450
41
58,677
3.95
6,650
16
58,131
3.24
9,750
42
73,902
3.91
9,850
17
55,398
3.55
11,500
43
55,327
3.88
8,350
18
69,943
3.75
8,975
44
16,262
4.12
10,250
19
79,785
3.85
8,975
45
38,348

6. 3.94
16,450
20
38,892
3.76
6,755
46
29,810
4.15
13,200
21
43,240
3.65
5,500
47
69,613
4.12
14,600
22
52,078
3.58
4,365
48
45,822
4.16
13,250
23
11,321
3.78
9,525
49
43,207
4.00
18,450
24
73,113

7. 3.75
18,600
50
81,998
3.93
16,500
25
79,988
3.22
14,450
51
46,756
3.89
6,500
26
98,311
3.42
15,500
52
34,592
3.83
5,650
1
51,345
2.78
4,280
27
78,953
2.27
21,225
2
50,337
2.35
3,875
28
52,875

8. 3.78
7,580
3
86,732
3.22
12,360
29
81,263
3.95
4,175
4
118,117
1.85
19,250
30
67,260
3.52
4,365
5
48,024
2.65
6,450
31
83,323
3.45
12,250
6
97,375
2.95
8,750
32
68,322
3.92
11,850
7
75,751

9. 2.86
9,600
33
71,925
4.05
14,360
8
78,797
3.35
9,600
34
29,372
4.01
9,540
9
59,856
3.45
9,600
35
21,710
3.68
7,250
10
23,696
3.25
6,250
36
37,833
3.62
4,280
11
61,385
3.21
4,780
37
41,154

10. 3.57
13,800
12
63,750
3.02
6,770
38
50,925
3.65
15,300
13
60,996
3.16
6,325
39
57,657
3.89
5,250
14
84,276
2.95
9,655
40
52,036
3.86
7,650
15
54,222
2.65
10,450
41
58,677
3.95
6,650
16
58,131

11. 3.24
9,750
42
73,902
3.91
9,850
17
55,398
3.55
11,500
43
55,327
3.88
8,350
18
69,943
3.75
8,975
44
16,262
4.12
10,250
19
79,785
3.85
8,975
45
38,348
3.94
16,450
20
38,892
3.76
6,755
46
29,810

12. 4.15
13,200
21
43,240
3.65
5,500
47
69,613
4.12
14,600
22
52,078
3.58
4,365
48
45,822
4.16
13,250
23
11,321
3.78
9,525
49
43,207
4.00
18,450
24
73,113
3.75
18,600
50
81,998
3.93
16,500
25
79,988

13. 3.22
14,450
51
46,756
3.89
6,500
26
98,311
3.42
15,500
52
34,592
3.83
5,650
Solution
(a) The estimated demand function is:
Q = 100,626.05 - 16,392.65P + 1.58A
The regression output is as follows.
(b)
The estimated parameters are:
Intercept = 100,626.05 (Quantity sold if Price = 0, Advertising = 0)
Price coefficient = - 16,392.65 (reduction in weekly quantity sold, if Price increases by 1 unit,
holding advertising expense constant)
Advertising coefficient = 1.58 (increase in weekly quantity sold if advertising expense increases
by 1 unit, holding price constant)
(c)
Signs of the parameters are expected to be as they should be. The intercept term is positive,
indicating that if price (and advertising) is zero, a high quantity would be sold. The price
coefficient is negative, which abides by the law of demand, that is, as higher prices are charged,
lower quantities would be demanded (and sold). Finally, the advertising coefficient has a positive
sign, indicating the positive impact of every unit of advertising expense on quantity sold.
The P-value for each variable is used to taste the null hypothesis that the coefficient is zero,
indicating zero effect on the dependent variable. If the P-Value is < 0.05, the null hypothesis can
be rejected, indicating that variable does influence the dependent variable's value. Here, P-value

14. is lower than 0.05 for each independent variable, signifying that both price and advertising does
influence the quantity sold.
R2 provides the percentage of how much the dependent variable can be predicted by the
independent variables. A higher R2 signifies higher predictability of the dependent variable by
the independent variables.
Here, R2 = 0.26, signifying only 26% of the dependent variable is predicted by the model. It
indicates a low goodness of fit.
(d)
Estimation of demand can be improved by including higher number of obervations (more weekly
data sets). The higher the number of data sets, the more the regression model will tend toward
accuracy. Another method will be to include more explanatory variables into the model. The low
R2 signifies that quantity sold cannot be predicted to a great extent using price and advertising as
its determinants. So inclusion of other explanatory variables will improve the estimate. For
example, consumer Income and substitute price are two important variables that affect a
product's demand. The higher the income and the higher the substitute price, the higher the
product price will tend to be. Exclusion of these determinants have rendered a low goodness of
fit to this model.
(e) P = 4.15, A = 18,000
Q = 100,626.05 - 16,392.65P + 1.58A
= 100,626.05 - (16,392.65 x 4.15) + (1.58 x 18,000)
= 100,626.05 - 68,029.50 + 28,440
= 61,037 units
(f) If A = 18,000 then
Q = 100,626.05 - 16,392.65P + (1.58 x 18,000)
= 129,066 - 16,392.65P
Or, P = (129,066 - Q) / 16,392.65
When Q = 50,000 :
P = (129,066 - 50,000) / 16,392.65 = 4.82SUMMARY OUTPUTRegression StatisticsMultiple
R0.513597898R Square0.263782801Adjusted R Square0.233733119Standard
Error19176.2901Observations52ANOVAdfSSMSFSignificance
FRegression2645603354532280167738.778222820.000551628Residual4918018774997367730
102Total5124474808542CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept100626.049719216.402735.236466533.42163E-
0662009.24337139242.856162009.2434139242.8561Price-16392.65235105.3048-
3.2109057030.002337804-26652.14621-6133.158387-26652.1462-
6133.158387Advertising1.5763337810.6031793092.6133750910.011873730.3641996012.78846

15. 79610.36419962.788467961