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Image of random data plus trend, with best-fit line and different smoothings
The data is 1000 points (plotted in black), with an increasing trend of 1-in-100, with random normal noise of standard deviation 10 superimposed. The red-line is the same data but averaged every 10 points. The blue line is averaged every 100 points.
For the three series, the least squares fit line is virtually the same, with a slope of 0.01, as expected.
The r2 fit for the raw data is 0.08; for the 10-pt-filtered, 0.57; for 100-pt-filtered, 0.97.
Ignoring autocorrelation, a confidence limit for the slope of the fit line is [0.0082, 0.0127] for the raw data (which include 0.01, as it should). For the 10-pt-filtered the limits are slightly narrower at [0.0084, 0.0125] and for the 100pt-filtering the limits are again slightly narrower.
So what does that all mean?
for the raw data, the simple trend line explains almost none of the variance of the time series (only 8%).
for the 100-pt filtering, the trend line explains almost all of the data (97%).
nonetheless, the trend lines are almost identical as are the confidence levels.
The time series are, of course, very closely related: the same except for the filtering. This shows that a low r2 value should not be interpreted as evidence of lack of trend.
Source code
Source in IDL. pp_regress and reg_explain not given.
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La bildo estas kopiita de wikipedia:en. La originala priskribo estas: '''Image of random data plus trend, with best-fit line and different smoothings''' {{GFDL}} The data is 1000 points, with a trend of 1-in-100, with random normal noise of SD 10 super
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