Description
The “Utilities” dataset includes information on 22 public utility companies in the US. The variable definitions are provided below.
Fixed_charge = fixed-charge covering ratio (income/debt)
RoR = rate of return on capital
Cost = cost per kilowatt capacity in place
Load_factor = annual load factor
Demand_growth = peak kilowatthour demand growth from 1974 to 1975
Sales = sales (kilowatthour use per year)
Nuclear = percent nuclear
Fuel_Cost = total fuel costs (cents per kilowatthour)
For Questions 1-4 below, do not scale the data.
- Compute the minimum, maximum, mean, median, and standard deviation for each of the numeric variables using data.table package. Which variable(s) has the largest variability? Explain your answer.
- Create boxplots for each of the numeric variables. Are there any extreme values for any of the variables? Which ones? Explain your answer.
- Create a heatmap for the numeric variables. Discuss any interesting trend you see in this chart.
- Run principal component analysis using unscaled numeric variables in the dataset. How do you interpret the results from this model?
- Next, run principal component model after scaling the numeric variables. Did the results/interpretations change? How so? Explain your answers.
