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The two main objectives are:
To get at a single value: Measures of central value, by considering the mass of data in one single value, enable us to get a bird's-eye view of the entire data. Thus one value can represent thousands, lakh and even millions of values. For example, it is impossible and is hardly of any use to remember the incomes of individual companies in a particular industry. But if the average income is obtained by dividing the total income by the number of companies, we get one single value that represents the entire industry.
To facilitate comparison: Reducing the mass of data to one single figure facilitates comparisons. Comparison can be made either at a point of time or over a period of time. For example, we can compare the average annual profits of different industries for a particular year, say, 20x3-x4 and thereby conclude which industry is the best or we can compare the growth percentage in sales of a particular industry for different time periods and thereby conclude as to whether the results are improving or deteriorating. Such comparisons are of immense help in framing suitable and timely policies.
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1. The standard approach here is to calculate some conventional ratios. These ratios can afterwards be used along with regression analysis to estimate the default probability.
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