Estimated Statistical Variance of Least Squares Predicted (Extrapolated) Rising Temperatures From Climate Change

9 Pages Posted: 3 Feb 2019 Last revised: 16 May 2019

Date Written: January 20, 2019

Abstract

Although NOAA global temperature change data are deterministic rather than statistical, this paper suggests a method of approximating what the variance might be were temperature actually described by a polynomial corrupted with statistical measurement noise (errors). It is a novel and simple technique of estimating noisy polynomials with unique orthogonal polynomial coefficient estimators. This allows simple estimates of the temperature at any point on the estimated polynomial and the statistical variance of the extrapolated temperature, extrapolation being of particular interest.

Keywords: Climate Change, Global Temperature, Polynomial Least Squares, Multiple-Model

Suggested Citation

Bell, Jeff, Estimated Statistical Variance of Least Squares Predicted (Extrapolated) Rising Temperatures From Climate Change (January 20, 2019). Available at SSRN: https://ssrn.com/abstract=3319526 or http://dx.doi.org/10.2139/ssrn.3319526

Jeff Bell (Contact Author)

Independent ( email )

No Address Available

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