For the measurements of each weather balloon, we calculated the best linear fit for each of the regions (using a statistical technique known as «
ordinary least squares linear regression»).
Speaking for my own number of 0.1 deg C / dec, it's called an «
ordinary least squares linear trend», the same sort of mysterious device the IPCC used.
Not exact matches
Applying simple
linear regression using
ordinary least squares to the data shows that this trend is statistically significant at the 95 per cent level.
It should be noted simple
linear regression using
ordinary least squares is not really the best method for assessing these data as it depends on assumptions which are violated by global temperature measurements.
The
ordinary least squares (OLS) regression approach used will, however, underestimate Y in the presence of fluctuations in surface temperature that do not give rise to changes in net radiative flux fitting the
linear model.
I tried to bring out the point about internal cloud oscillations, in writing: «The
ordinary least squares (OLS) regression approach used will, however, underestimate Y in the presence of fluctuations in surface temperature that do not give rise to changes in net radiative flux fitting the
linear model.