Question: This item requires the dataset Utilities.xls which can be found on the subject Interact site. This dataset gives corporate data on 22 US public utilities.
This item requires the dataset Utilities.xls which can be found on the subject Interact site. This dataset gives corporate data on 22 US public utilities. We are interested in forming groups of similar utilities. The objects to be clustered are the utilities. There are 8 measurements on each utility described below. An example where clustering would be useful is a study to predict the cost impact of deregulation. To do the requisite analysis economists would need to build a detailed cost model of the various utilities. It would save a considerable amount of time and eort if we could cluster similar types of utilities and to build detailed cost models for just one typical utility in each cluster and then scaling up from these models to estimate results for all utilities. The objects to be clustered are the utilities and there are 8 measurements on each utility.
X1: Fixed-charge covering ratio (income/debt) X2: Rate of return on capital X3: Cost per KW capacity in place X4: Annual Load Factor X5: Peak KWH demand growth from 1974 to 1975 X6: Sales (KWH use per year) X7: Percent Nuclear X8: Total fuel costs (cents per KWH)
>> Conduct Principal Component Analysis (PCA) on the data. Evaluate and comment on the Results. Should the data be normalized? Discuss what characterizes the components you consider key and justify your answer.
| utility_name | utility | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 |
| Arizona | 1 | 1.06 | 9.2 | 151 | 54.4 | 1.6 | 9077 | 0 | 0.628 |
| Boston | 2 | 0.89 | 10.3 | 202 | 57.9 | 2.2 | 5088 | 25.3 | 1.555 |
| Central | 3 | 1.43 | 15.4 | 113 | 53 | 3.4 | 9212 | 0 | 1.058 |
| Common | 4 | 1.02 | 11.2 | 168 | 56 | 0.3 | 6423 | 34.3 | 0.7 |
| Consolid | 5 | 1.49 | 8.8 | 192 | 51.2 | 1 | 3300 | 15.6 | 2.044 |
| Florida | 6 | 1.32 | 13.5 | 111 | 60 | -2.2 | 11127 | 22.5 | 1.241 |
| Hawaiian | 7 | 1.22 | 12.2 | 175 | 67.6 | 2.2 | 7642 | 0 | 1.652 |
| Idaho | 8 | 1.1 | 9.2 | 245 | 57 | 3.3 | 13082 | 0 | 0.309 |
| Kentucky | 9 | 1.34 | 13 | 168 | 60.4 | 7.2 | 8406 | 0 | 0.862 |
| Madison | 10 | 1.12 | 12.4 | 197 | 53 | 2.7 | 6455 | 39.2 | 0.623 |
| Nevada | 11 | 0.75 | 7.5 | 173 | 51.5 | 6.5 | 17441 | 0 | 0.768 |
| NewEngla | 12 | 1.13 | 10.9 | 178 | 62 | 3.7 | 6154 | 0 | 1.897 |
| Northern | 13 | 1.15 | 12.7 | 199 | 53.7 | 6.4 | 7179 | 50.2 | 0.527 |
| Oklahoma | 14 | 1.09 | 12 | 96 | 49.8 | 1.4 | 9673 | 0 | 0.588 |
| Pacific | 15 | 0.96 | 7.6 | 164 | 62.2 | -0.1 | 6468 | 0.9 | 1.4 |
| Puget | 16 | 1.16 | 9.9 | 252 | 56 | 9.2 | 15991 | 0 | 0.62 |
| SanDiego | 17 | 0.76 | 6.4 | 136 | 61.9 | 9 | 5714 | 8.3 | 1.92 |
| Southern | 18 | 1.05 | 12.6 | 150 | 56.7 | 2.7 | 10140 | 0 | 1.108 |
| Texas | 19 | 1.16 | 11.7 | 104 | 54 | -2.1 | 13507 | 0 | 0.636 |
| Wisconsi | 20 | 1.2 | 11.8 | 148 | 59.9 | 3.5 | 7287 | 41.1 | 0.702 |
| United | 21 | 1.04 | 8.6 | 204 | 61 | 3.5 | 6650 | 0 | 2.116 |
| Virginia | 22 | 1.07 | 9.3 | 174 | 54.3 | 5.9 | 10093 | 26.6 | 1.306 |
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