# Evaluation of scores and observations the initial

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Evaluation of scores and observations The initial step requires feeding the data into the R environment, which uses R language for statistical computing and graphics ( What is R , 2018). The data will be entered into the software environment then processed to give the required output. Snippets of the codes utilized and the results obtained from the codes will be incorporated. What follows after this step is plotting the new RIDIT scores against the IND values to attempt to find out if there is a type of association between the variables. It seems that both IND and RIDIT

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IDENTIFICATION OF LOSSES IN COMPANY XYZ 3 scores incline with close margins if not similar. This implies that the RIDIT method used can regularize or systemize the process but still retain most of the attributes that the initial IND score gives to the whole risk. Based on the graph above, it seems that each score has a minimal influence on the increase and overall estimation of IND_01. This means that one score can have a bigger proportion of variance (Starmer, 2015). The bigger proportion may imply that a single distinct variable has a larger influence on the results of the model. Recognizing this vital variable, therefore implies that XYZ can emphasize its association with other variables.
IDENTIFICATION OF LOSSES IN COMPANY XYZ 4 Component importance Comp.1 Comp.2 Comp.3 Comp.4 Standard Deviation 0.62045740 0.609892780 0.59353170 0.587159410 Variance Proportion 0.06763170 0.065348160 0.06188910 0.060567330 Cumulative Proportion 0.06763170 0.132979870 0.19486900 0.255436300 Comp.5 Comp.6 Comp.7 Comp.8 Standard Deviation 0.578026990 0.572554720 0.562353830 0.554677050 Variance Proportion 0.058697910 0.057591770 0.055557890 0.054051380 Cumulative Proportion 0.314134210 0.371725980 0.427283860 0.481335240 Comp.9 Comp.10 Comp.11 Comp.12 Standard Deviation 0.550025170 0.541440350 0.536939810 0.516130350 Variance Proportion 0.053148560 0.051502420 0.050649780 0.046799930 Cumulative Proportion 0.534483800 0.585986220 0.636636010 0.683435940 Comp.13 Comp.14 Comp.15 Comp.16 Standard Deviation 0.512395840 0.501824630 0.492396700 0.481692120 Variance Proportion 0.046125130 0.044241550 0.042594810 0.040762940 Cumulative Proportion 0.729561060 0.773802610 0.816397420 0.857160360 Comp.17 Comp.18 Comp.19 Comp.20 Standard Deviation 0.472090120 0.457794130 0.44018710 0.432261890 Variance Proportion 0.039154010 0.036818560 0.03404090 0.032826180 Cumulative Proportion 0.896314370 0.933132930 0.96717380 1.000000000 On analyzing all the variables indicated above using PCA, a trend as the one shown below appears. Instead of showing the trend for all the twenty variables in the table above, R

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