This document provides a dataset on cost function data for 145 U.S. electricity producers from 1955. It includes variables for total cost, total output, and prices of labor, fuel, and capital. Students are asked to: 1) Copy the dataset into Excel and take the logarithm of all variables 2) Run a multiple regression with the logarithmic variables 3) Write a 3-4 page report analyzing the regression results and answering questions about elasticities, predictions, and anomalies.

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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)

22. 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=
whereis 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?
INFO620: Enterprise Database Systems – Labs
Page | 1

23. Name: _________________________________________
Date: __________________
Week 4 – Lab #3: Chapter 8
Grade: 3% of Class Grade
For lab assignments you must use an actual database – Database
systems such as Oracle, MS SQL, DB2, or Access are all
acceptable if they can handle the capability to handle these
questions. Do the exercises in the actual database then copy and
paste the results or an image of the results here in the respective
question part. ER Diagrams and Schemas can be created with
MS Visio templates. Refer to your Lab Manual located under
Resources > Assignments folder.
I. Page 281 Lab Exercise #8.28 all sections [1% point]
II. Page 281 Lab Exercise #8.31 [1% point]
III. Page 281 Lab Exercise #8.32 [1% point]
American Public University System
Kageorgis