Managerial Economics homework help

  

Managerial EconomicsComplete assignment as described in attachment530_s3_0.docx
_s3_0.docx

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you are given a data set of cost function data. The data is based
on 145 U.S. Electricity Producers in 1955. The source of the original
data is:
Nerlove, M. (1963) Returns to Scale in Electricity Supply. In C.
Christ (ed.), Measurement in Economics: Studies in Mathematical
Economics and Econometrics in Memory of Yehuda Grunfeld.
Stanford University Press.
This is a classic work in cost analysis, and has been used in many
managerial economics courses, starting with Harvard Business
School. The original data have been revised and updated by
Christensen, L. R., and Greene, W. H. The attached dataset is
based on the revised data.
DATASET
Variables
TC = total cost (in 1970 Million USD)
Q = total output (Billion KwH)
PL = price of labor (wages)
PF = price of fuel
PK = price of capital
Observations
TC
Q
PL
PF
PK
0.082
2
2.1
17.9
183
0.661
3
2.1
35.1
174
0.99
4
2.1
35.1
171
0.315
4
1.8
32.2
166
0.197
5
2.1
28.6
233
0.098
9
2.1
28.6
195
0.949
11
2
35.5
206
0.675
13
2.1
35.1
150
0.525
13
2.2
29.1
155
0.501
22
1.7
15
188
1.194
25
2.1
17.9
170
0.67
25
1.7
39.7
167
0.349
35
1.8
22.6
213
0.423
39
2.3
23.6
164
0.501
43
1.8
42.8
170
0.55
63
1.8
10.3
161
0.795
68
2
35.5
210
0.664
81
2.3
28.5
158
0.705
84
2.2
29.1
156
0.903
73
1.8
42.8
176
1.504
99
2.2
36.2
170
1.615
101
1.7
33.4
192
1.127
119
1.9
22.5
164
0.718
120
1.8
21.3
175
2.414
122
2.1
17.9
180
1.13
130
1.8
38.9
176
0.992
138
1.8
20.2
202
1.554
149
1.9
22.5
227
1.225
196
1.9
29.1
186
1.565
197
2.2
29.1
183
1.936
209
1.9
22.5
169
3.154
214
1.5
27.5
168
2.599
220
1.9
22.5
164
3.298
234
2.2
36.2
164
2.441
235
2.1
24.4
170
2.031
253
1.9
22.5
158
4.666
279
2.1
35.1
177
1.834
290
1.7
33.4
195
2.072
290
1.8
20.2
176
2.039
295
1.8
21.3
188
3.398
299
1.7
26.9
187
3.083
324
2.1
35.1
152
2.344
333
2.2
29.1
157
2.382
338
1.9
24.6
163
2.657
353
2.2
29.1
143
1.705
353
2.1
10.7
167
3.23
416
1.5
26.2
217
5.049
420
1.5
27.5
144
3.814
456
2.1
30
178
4.58
484
1.8
42.8
176
4.358
516
2.3
23.6
167
4.714
550
2.1
35.1
158
4.357
563
2.3
31.9
162
3.919
566
2.3
33.5
198
3.442
592
1.9
22.5
164
4.898
671
2.1
35.1
164
3.584
696
1.8
10.3
161
5.535
719
1.7
26.9
174
4.406
742
2
20.7
157
4.289
795
2.2
26.5
185
6.731
800
1.7
26.9
157
6.895
808
1.7
39.7
203
5.112
811
2.3
28.5
178
5.141
855
2
34.3
183
5.72
860
2.3
33.5
168
4.691
909
1.5
17.6
196
6.832
913
1.7
26.9
166
4.813
924
1.8
10.3
172
6.754
984
1.7
26.9
158
5.127
991
2.1
30
174
6.388
1000
1.6
28.2
225
4.509
1098
2.1
24.4
168
7.185
1109
2.1
35.1
177
6.8
1118
2.3
23.6
161
7.743
1122
2.2
29.1
162
7.968
1137
2
20.7
158
8.858
1156
2.3
33.5
176
8.588
1166
1.7
26.9
183
6.449
1170
2.1
35.1
166
8.488
1215
2.2
29.1
164
8.877
1279
2
34.3
207
10.274
1291
2.3
31.9
175
6.024
1290
1.6
28.2
225
8.258
1331
2.1
30
178
13.376
1373
2.2
36.2
157
10.69
1420
2.2
36.2
138
8.308
1474
1.9
24.6
163
6.082
1497
1.8
10.3
168
9.284
1545
1.8
20.2
158
10.879
1649
2.3
31.9
177

8.477
1668
1.8
20.2
170
6.877
1782
2.1
10.7
183
15.106
1831
2
35.5
162
8.031
1833
1.8
10.3
177
8.082
1838
1.5
17.6
196
10.866
1787
2.2
26.5
164
8.596
1918
1.7
12.9
158
8.673
1930
1.8
22.6
157
15.437
2028
2.1
24.4
163
8.211
2057
1.8
10.3
161
11.982
2084
1.8
21.3
156
16.674
2226
2
34.3
217
12.62
2304
2.3
23.6
161
12.905
2341
2
20.7
183
11.615
2353
1.7
12.9
167
9.321
2367
1.8
10.3
161
12.962
2451
2
20.7
163
16.932
2457
2.2
36.2
170
9.648
2507
1.8
10.3
174
18.35
2530
2.3
33.5
197
17.333
2576
1.9
22.5
162
12.015
2607
1.8
10.3
155
11.32
2870
1.8
10.3
167
22.337
2993
2.3
33.5
176
19.035
3202
2.3
23.6
170
12.205
3286
1.6
17.8
183
17.078
3312
1.7
28.8
190
25.528
3498
2.1
30
170
24.021
3538
2.1
30
176
32.197
3794
2.1
35.1
159
26.652
3841
2.3
28.5
157
20.164
4014
2.1
24.4
161
14.132
4217
1.5
18.1
172
21.41
4305
2.1
24.4
203
23.244
4494
2
20.7
167
29.845
4764
2.2
29.1
195
32.318
5277
1.9
29.1
161
21.988
5283
2
20.7
159
35.229
5668
2.1
24.4
177
17.467
5681
1.8
10.3
157
22.828
5819
1.8
18.5
196
33.154
6000
2.1
24.4
183
32.228
6119
1.5
26.2
189
34.168
6136
1.9
22.5
160
40.594
7193
2.1
28.6
162
33.354
7886
1.6
17.8
178
64.542
8419
2.3
31.9
199
41.238
8642
2.2
26.5
182
47.993
8787
2.3
33.5
190
69.878
9484
2.1
24.4
165
44.894
9956
1.7
28.8
203
67.12
11477
2.2
26.5
151
73.05
11796
2.1
28.6
148
139.422
14359
2.3
33.5
212
119.939
16719
2.3
23.6
162
Cut the data set and paste it into Excel. Then, in Excel, obtain the
logarithmic transformation of all the variables using the Excel
function: =LOG( . ), i.e.,
logTC = log(total cost)
logQ = log(total output)
logPL = log(price of labor)
logPF = log(price of fuel)
logPK = log(price of capital)
Run the following regression using the Excel add-in Data Analysis:
logTC=
where is an error term, and the variables and their logarithmic
transformations are defined above.
Read the Background material, run the multiple regression outlined
above and then write a 3- to 4-page report (and attach the Excel
printout) answering the following questions:
•
What is the R-square of the regression? What does it mean?
•
What is the elasticity of TC with respect to Q? Test the significance
of
• What is the elasticity of TC with respect to PL? Test the significance of
•
What is the elasticity of TC with respect to PF? Test the significance of
•
What is the elasticity of TC with respect to PK? Test the significance of
•
Can you forecast (predict) what happens to TC if PL doubles (keeping
everything else constant)?
• Looking at the ANOVA table, can you conclude the independent
variables jointly affect the average housing price? See the ANOVA
note in Module 2 SLP.
• Do you find any anomaly in the results? That is, is there any result that
does not make sense to you?
• How would inclusion of modern generation mix (Coal, Nuclear, Natural
Gas) change the specification of the demand for electricity?

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