Not
using quadratic fits, and certainly the non-parabolic trend which is present can't be found in such noisy data sets.
I have often
used quadratic fits of annual minimum Arctic sea ice extent to forecast the future value.
You can also calculate the acceleration rate by
using a quadratic fit.
It uses quadratic fit to 25 - y subintervals.
Not exact matches
Individual growth curve models were developed for multilevel analysis and specifically designed for exploring longitudinal data on individual changes over time.23
Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct model for our individual growth curves, we compared a series of MIXED models by evaluating the difference in deviance between nested models.23 Both fixed
quadratic and cubic MIXED models
fit our data well, but we selected the fixed
quadratic MIXED model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.
Write,
using technology,
quadratic functions that provide a reasonable
fit to data to estimate solutions and make predictions for real - world problems.
Just as an addition if a stats package like R is
used (for the above data), it will find not only the coefficients for the
quadratic fit but also the standard error for the coefficient values.
The lowess
fit uses locally weighted least squares to
fit either a linear or
quadratic at each predictor point.
In fact, it is readily shown that the statistics of temperature
fit obtained
using the abstracted flux curve are superior to your assumption of a
quadratic in temperature / straight line in flux.
We perform a least - squares
fit of a
quadratic using a time epoch of 2005.0 (the midpoint of the altimeter time series), where acceleration is twice the
quadratic coefficient.