Interpolation & Extrapolation | Business Mathematics and Statistics - B Com PDF Download

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Extrapolation & Interpolation
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Extrapolation & Interpolation
• An extrapolation is similar to an educated guess or a hypothesis, involving
the use of facts and observations about a present or known situation to
predict future outcomes.
• In data science, extrapolation is a statistical technique employed to
estimate values of data points beyond the range covered by the known
values in a dataset.
• It is a mathematical approach used to project or extend existing trends,
patterns, or relationships in data, making predictions or estimates for
values outside the original set of data points.
Extrapolation
Page 3


Extrapolation & Interpolation
• An extrapolation is similar to an educated guess or a hypothesis, involving
the use of facts and observations about a present or known situation to
predict future outcomes.
• In data science, extrapolation is a statistical technique employed to
estimate values of data points beyond the range covered by the known
values in a dataset.
• It is a mathematical approach used to project or extend existing trends,
patterns, or relationships in data, making predictions or estimates for
values outside the original set of data points.
Extrapolation
• Extrapolation assumes that observed patterns or trends in known data
will persist in unobserved or future data points.
• Despite its utility in making predictions, extrapolation has certain
assumptions and limitations.
• Caution is required when using extrapolation techniques, as extending
too far beyond the observed data range can result in inaccurate or
unreliable predictions.
• Changes in underlying factors that influence the data, not considered in
the extrapolation, can affect the accuracy of predictions.
• Careful consideration of the context is essential when employing
extrapolation to ensure its reliability and relevance.
Extrapolation
Page 4


Extrapolation & Interpolation
• An extrapolation is similar to an educated guess or a hypothesis, involving
the use of facts and observations about a present or known situation to
predict future outcomes.
• In data science, extrapolation is a statistical technique employed to
estimate values of data points beyond the range covered by the known
values in a dataset.
• It is a mathematical approach used to project or extend existing trends,
patterns, or relationships in data, making predictions or estimates for
values outside the original set of data points.
Extrapolation
• Extrapolation assumes that observed patterns or trends in known data
will persist in unobserved or future data points.
• Despite its utility in making predictions, extrapolation has certain
assumptions and limitations.
• Caution is required when using extrapolation techniques, as extending
too far beyond the observed data range can result in inaccurate or
unreliable predictions.
• Changes in underlying factors that influence the data, not considered in
the extrapolation, can affect the accuracy of predictions.
• Careful consideration of the context is essential when employing
extrapolation to ensure its reliability and relevance.
Extrapolation
"Extrapolation is derived from the word 'extra,' which means 'outside,'
and a shortened form of the term 'interpolation.' While 'interpolation' may
sound unfamiliar, it refers to the insertion between two points. Therefore,
extrapolation involves inserting points outside of any existing data points.
For instance, consider if you have information about Monday and Tuesday.
In such a scenario, you could potentially make an extrapolation about
Wednesday."
Etymology 
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Extrapolation & Interpolation
• An extrapolation is similar to an educated guess or a hypothesis, involving
the use of facts and observations about a present or known situation to
predict future outcomes.
• In data science, extrapolation is a statistical technique employed to
estimate values of data points beyond the range covered by the known
values in a dataset.
• It is a mathematical approach used to project or extend existing trends,
patterns, or relationships in data, making predictions or estimates for
values outside the original set of data points.
Extrapolation
• Extrapolation assumes that observed patterns or trends in known data
will persist in unobserved or future data points.
• Despite its utility in making predictions, extrapolation has certain
assumptions and limitations.
• Caution is required when using extrapolation techniques, as extending
too far beyond the observed data range can result in inaccurate or
unreliable predictions.
• Changes in underlying factors that influence the data, not considered in
the extrapolation, can affect the accuracy of predictions.
• Careful consideration of the context is essential when employing
extrapolation to ensure its reliability and relevance.
Extrapolation
"Extrapolation is derived from the word 'extra,' which means 'outside,'
and a shortened form of the term 'interpolation.' While 'interpolation' may
sound unfamiliar, it refers to the insertion between two points. Therefore,
extrapolation involves inserting points outside of any existing data points.
For instance, consider if you have information about Monday and Tuesday.
In such a scenario, you could potentially make an extrapolation about
Wednesday."
Etymology 
Extrapolation Methods
Management | 2025
Various extrapolation methods are employed to predict and analyze
trends in data. Among these, two widely used methods are:
Extrapolation is the process of estimating or predicting values outside
the range of known or observed data. It involves extending a trend or
pattern identified in existing data to make predictions about future or
unseen values. There are various methods of extrapolation, each with
its own assumptions, advantages, and limitations. Here are some
common extrapolation methods: