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## Identifying and Assessing data

Extrapolation is an assessment of a value based on extending a sequence of values or facts beyond the area that is unquestionably known in the context of data identification. Interpolation, on the other hand, involves making an estimate of a value between two known values in a series of values.

A technique for guessing values between known data points is a polynomial interpolation. We can use interpolation to estimate values when there is a gap in graphical data, but data is accessible on either side of the gap or at a few selected spots inside the gap.

Understanding the determining element that will determine if the gap(s) in or extension of the data are caused by the random constraints contained in the sample data, limitations in the population metrics, or any other factors that can change the result of the study is the tricky part here.

There is no identifying issue when interpolation or extrapolation is used to close gaps or extend the data sample but not the population. There is an issue with identification when interpolation or extrapolation is employed to fill in gaps or a small portion of the population. No matter how much information is gathered from the population, it will not be beneficial to make any inferences about what is taking place in the range that is not being seen. The essential distinction between extrapolation and interpolation for use in classifying and evaluating data is as follows:

Whereas extrapolation uses correlations, interpolation uses causal estimates.

Whereas extrapolation uses non-linear function forms, interpolation uses linear functional forms.

When extrapolation extends beyond the scope of the data, interpolation fills in data gaps.

Extrapolation needs to be used with instrumental variables, whereas interpolation can be used with control variables.

## Reference

Prince, J. (2019). Predictive Analytics for Business Strategy: Reasoning from Data to Actionable Knowledge. McGraw Hill Education Publishing.

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Question

Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), the term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.

Identifying and Assessing data

Your initial post should be based on the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.

Also, provide a graduate-level response to each of the following questions:

1. In Chapter 10 the focus of the material is identifying and assessing data. One of the chief concerns of identifying and assessing data is extrapolation and interpolation. Please explain both of these concepts and give a reason why either of these scenarios would occur.

[Your post must be substantive and demonstrate insight gained from the course material. Postings must be in the student’s own words – do not provide quotes!] [Your initial post should be at least 150+ words and in APA format (including Times New Roman with font size 12 and double-spaced).

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