A) FUEL COST

B) SALES

C) LOAD FACTOR

D) NUCLEAR

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Which variable(s) have the highest influence on PC2 in the table below? PC1 PC2 P03 PC4 POS ## Fixed_charge 0.44554526 -0.23217669 0.06712849 -0.55549758 0.4008403 ## RoR 0.57119021 -0.10053490 0.07123367 -0.33209594 -0.3359424 # Cost -0.34869054 0.16130192 0.46733094 -0.40908380 0.2685680 ## Load_factor -0.28890116 -0.40918419 -0.14259793 -0.33373941 +0.6800711 # Demand growth -0.35536100 0.28293270 0.28146360 -0.39139699 -0.1626375 ## Sales 0.05383343 0.60309487 -0.33199086 -0.19086550 -0.1319721 $# Nuclear 0.16797023 -0.08536118 0.73768406 0.33348714 -0.2496462 ** Fuel_Cost -0.33584032 -0.53988503 -0.13442354 -0.03960132 0.2926660 PC6 PC7 POS ** Fixed_charge -0.00654016 0.20578234 -0.48107955 ## ROR -0.13326000 -0.15026737 0.62855128 $# Cost 0.53750238 -0.11762875 0.30294347 ## Load_factor 0.29890373 0.06429342 -0.24781930 #* Demand growth -0.71916993 -0.05155339 -0.12223012 # Sales 0.14953365 0.66050223 0.10339649 # Nuclear 0.02644086 0.48879175 -0.08466572 ## Fuel_Cost -0.25235278 0.48914707 0.43300956 Removing variables that are strongly correlated to others is useful for avoiding multicollinearity problems that can arise in various models. O True O False