Identifying and Assessing data

Identifying and Assessing data

In terms of Data identification, Extrapolation is an estimation of a value based on extending a known sequence of values or facts beyond the area that is certainly known. At the same time, interpolation is estimating a value within two known values in a sequence of values.

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Polynomial interpolation is a method of estimating values between known data points. When graphical data contains a gap, but data is available on either side of the gap or at a few specific points within the gap, interpolation allows us to estimate the values within the gap.

The challenge here is to understand the determining factor of whether the gap(s) in or extension of the data is due to the random limitations present in the sample data, limitations in the population metrics, or some other factors that can fluctuate the outcome of the analysis.

When interpolation or Extrapolation is used to fill in gaps or limited extent of the data sample but not the population, there is no identification problem. When interpolation or Extrapolation is used to fill gaps or a limited extent of the population, there is an identification problem. No matter how much data is collected from the population, it will not help to conclude what is happening in the unobserved range.

 Here is the crucial distinction between Extrapolation and interpolation for being used in identifying and assessing data:

Interpolation involves causal estimates, while Extrapolation involves correlations.

Interpolation involves linear functional forms, while Extrapolation involves non-linear function forms.

Interpolation fills data gaps, while Extrapolation fills beyond the extent of the data.

Interpolation can be used with control variables, while Extrapolation must be used with instrumental variables.

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Reference

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

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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), term(s), and any other thing that you felt was worthy of your understanding in each assigned textbook chapter.

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 your discussion.

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

1. In Chapter 10, the material focuses on 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).