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FAQs on Chapter 12: Interpolation and Extrapolation - B.com ten Year Question paper - B Com

1. What is interpolation and extrapolation?
Ans. Interpolation is a mathematical technique that estimates values between two known values, while extrapolation estimates values beyond the known data points. Interpolation is used when the data is within the given range, while extrapolation is used when the data is beyond the given range.
2. Why is interpolation and extrapolation important in business and economics?
Ans. Interpolation and extrapolation are important in business and economics because they help in making predictions and decisions based on limited data. These techniques allow us to estimate values or trends within or beyond the observed data range, which is crucial for planning, forecasting, and analyzing business and economic trends.
3. What are some common methods used for interpolation?
Ans. Some common methods used for interpolation include linear interpolation, polynomial interpolation, spline interpolation, and nearest neighbor interpolation. These methods vary in complexity and accuracy, and the choice of method depends on the nature of the data and the required level of precision.
4. How can interpolation and extrapolation be used in financial analysis?
Ans. In financial analysis, interpolation and extrapolation can be used to estimate missing or future values of financial variables. For example, if historical data on a company's revenue is known up to a certain year, interpolation can be used to estimate the revenue for the missing years. Similarly, extrapolation can be used to forecast future revenue based on the historical data.
5. What are the limitations of interpolation and extrapolation?
Ans. Interpolation and extrapolation have certain limitations. Interpolation assumes a smooth and continuous relationship between data points, which may not always be the case. Extrapolation assumes that the relationship between data points extends beyond the observed range, which can lead to inaccurate predictions if the underlying relationship changes. Additionally, both techniques are sensitive to outliers and can produce unreliable results if the data is not well-behaved. Therefore, caution should be exercised when using interpolation and extrapolation in data analysis.
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