# Instead of showing the trend for all the twenty

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below appears. Instead of showing the trend for all the twenty variables in the table above, R can be used to draw a screen plot. The screen plot is a better means for evaluation as it helps to visually recognize the proportion of variance in the RIDIT scores thus can be used to find out the entire effect that every score has on the threat identification process. Screen plot

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IDENTIFICATION OF LOSSES IN COMPANY XYZ 5 As it can be seen, IND_01 has the highest influence on outcomes, however, the nineteen issues cannot be ignored. All the twenty IND issues must, therefore, be included in the analysis. R has a vital package, which can be installed to help achieve this ( FactoMineR , 2018). The FactoMineR package will carry out more technical roles in the PCA without using an intense code. It can also be used to give more details concerning the XYZ dataset.
IDENTIFICATION OF LOSSES IN COMPANY XYZ 6 The individual factors map above indicate a detailed disintegration of how every individual perspective associate to the initial and second dimensions of the PCA. In totality, it can be seen that dimension one and two represent 13.310% of the entire variance in the PCA. Claim two hundred and thirteen is closely associated with dimension two while claim three hundred is the most associated with factors in dimension one. This can be important in addressing the claims that may need a closer look. The variables factor map is crucial for identifying which IND values are more closely associated with the dimensions of concern. For instance, IND_09 is highly related to dimension two while IND_12 is highly related to dimension one.

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IDENTIFICATION OF LOSSES IN COMPANY XYZ 7 To emphasis more on the individual factors map, R and FactorMineR can be used to add more confidence ellipses on these claims. These indicators of confidence can help give more analysis on the potential threat posed by the claims depending on the new claims that arise, and which are similar to the regions in the plot.
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