Description
Complete question 3 of the form, and question 4 of the form and use the model to make the regression equation
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Managerial Analys
Summer 20
1. Carrie's Corporate Catering ("Triple-C") provides breakfast, lunch, or dinner for m
of $8 (MC=$8.00) per attendee. Because MC is constant, average variable cost (AVC
also equal to average total cost (AC). Assume the demand for each event is identica
charged per attendee.
Assume, initially, Triple-C charges a single price per attendee for each event.
Use this information to answer the questions (a. through d.) below.
NOTE: For some of these questions, you may want to do the work outside of Excel
a. With a single price, how much will Triple-C charge per attendee in order to maximize profit, and
NOTE: Cost-plus pricing here is consistent with marginal-cost pricing, so follow the MR=MC appr
Attendees per event
(Round to nearest whole
number):
Profit-maximizing price
per attendee:
b. With a single price, what is Triple-C's markup and profit margin per attendee? (2 pts.)
c. With a single price, what is Triple-C's profit per event? (2 pts.)
Note: Enter a formula--using information above--to calculate profit.
Suppose Triple-C is considering an alternative pricing scheme, in which the company would char
$44 per attendee for the next 10 attendees, $32 per attendee for the next 10 attendees, and $8 for a
In other words, Triple-C would engage in block pricing, a form of second-degree price discriminat
d. Under this pricing scheme, how many attendees would be at each event and what would be Trip
Attendees per event:
Profit per event:
Managerial Analysis Final Assignment
Summer 2021 (Sec. 791)
unch, or dinner for meetings and events held by local businesses and organizations. For eac
e variable cost (AVC) is equal to $8, as well, and because Triple-C engages in costach event is identical, given by the expression, P = 68 - 1.2Q, where Q is the number of attend
r each event.
rk outside of Excel and just put your answer in the text box provided.
to maximize profit, and how many people will attend each event (attendees per event) at that price? (4 pts.)
follow the MR=MC approach.
ndee? (2 pts.)
attendees, and $8 for all remaining attendees (i.e. set price equal to marginal cost for each attendee after 30
izations. For each event, Triple-C incurs marginal cost
es in cost-plus pricing (i.e. prices for the long run), AVC is
the number of attendees for each event, and P is the price
nt) at that price? (4 pts.)
or each attendee after 30 in total).
Managerial Analysis Final Assign
2. You are a manager in a large hotel chain that is about to open a hotel in a new cit
customers that will stay at this hotel: tourists and business travelers. You can sepa
third-degree price discrimination. To help you determine the profit-maximizing price
50 other cities (these data can be found below). Based on prior research, you know
an exponential demand function, of the form: Q = APn, where Q is output (number o
demand.
Part I: For each group of customers, run a regression to estimate "n," the price elas
NOTE: Since this is an exponential function, you will have to first transform the dat
LN(Q) = LN(A) + nLN(P).
In this form, the estimated coefficient for the log of P (i.e. "n") will be, in fact, your es
you should obtain a negative value from your regression and not omit the minus sig
NOTE: Round each of your estimated coefficients (i.e. regression results) to three d
Part II: Use the results of your regressions to answer the question (determining the
HINT: Look at Slide 9 of the lecture notes.
NOTE: Assume marginal cost for each nightly stay is $25.
a. Based on your regression results, enter a formula in the respective boxes below to calculate th
NOTE: Round your answers to the nearest dollar.
Tourists:
Business Travelers:
DATA:
Tourists
Price (per night)
207
226
165
183
171
176
193
205
161
188
161
180
187
204
192
217
201
229
184
168
164
210
164
195
200
217
214
226
218
227
224
166
203
211
179
197
205
176
219
166
185
180
206
192
163
163
194
162
Nightly Stays per Month
(000s)
189
167
242
222
234
233
200
187
255
215
249
228
209
193
211
173
191
164
217
236
244
186
250
205
198
178
182
173
175
173
171
242
191
185
226
198
192
229
179
247
214
219
185
210
252
252
204
256
Business Travelers
Price (per night)
229
243
221
257
261
275
240
226
228
263
257
271
288
235
250
243
247
257
264
266
257
265
239
249
221
249
255
289
241
289
231
281
255
240
242
244
223
285
231
270
253
276
259
287
275
269
227
266
222
200
177
196
228
225
alysis Final Assignment (Summer 2021, Sec. 791)
n a hotel in a new city. You have been asked to determine the nightly rates that should be ch
velers. You can separate (and so identify) members of each group based on advanced booki
maximizing price for each group, you use a sample of data you have on these same grou
research, you know that demand for both groups exhibit constant elasticity. Therefore, in u
is output (number of nightly stays per month), P is price per night, A is a constant, and "n" i
ate "n," the price elasticity of demand for that group. (4 pts.)
rst transform the data into natural logs in order to estimate a linear regression of the form:
A) + nLN(P).
will be, in fact, your estimate of the price elasticity of demand for that group. ALSO, because "
ot omit the minus sign as you do your calculations.
on results) to three decimal places.
ion (determining the profit-maximizing price for each group).
xes below to calculate the profit-maximizing price for each group of customers. (6 pts.)
Business Travelers
Nightly Stays per Month
(000s)
25
24
26
23
22
21
24
27
25
22
22
21
19
25
24
23
24
22
21
21
22
22
25
24
27
24
23
20
24
20
26
21
22
24
24
23
26
21
25
22
22
21
22
19
21
22
25
22
26
26
tes that should be charged to the two distinct groups of
d on advanced booking. Therefore, you plan to implement
e on these same groups of customers for the hotel chain in
city. Therefore, in using the data below, you will assume
a constant, and "n" is the (constant) price elasticity of
ssion of the form:
up. ALSO, because "n" is the price elasticity of demand,
SUMMARY OUTPUT
Regression Statistics
Multiple R 0,886829
R Square 0,786466
Adjusted R Square
0,784287
Standard Error
2,019335
Observations
100
ANOVA
df
Regression
Residual
Total
SS
MS
F
Significance F
1 1471,824 1471,824 360,9437 1,26E-34
98 399,6158 4,077712
99 1871,44
Coefficients
Standard Error t Stat
P-value Lower 95%Upper 95%Lower 95.0%
Upper 95.0%
Intercept
130,5616 2,39826 54,44013 4,68E-75 125,8023 135,3208 125,8023 135,3208
X Variable 1 -1,78325 0,093863 -18,9985 1,26E-34 -1,96952 -1,59698 -1,96952 -1,59698
SUMMARY OUTPUT
Regression Statistics
Multiple R 0,981239
R Square
0,96283
Adjusted R Square
0,962451
Standard Error
0,554973
Observations
100
ANOVA
df
Regression
Residual
Total
SS
MS
F
Significance F
1 781,8564 781,8564 2538,532 7,12E-72
98 30,18356 0,307996
99
812,04
Coefficients
Standard Error t Stat
P-value L...