Compensating for autocorrelation in global temperature data
Autocorrelation in global temperature data simply means that the average temperature for any one month is correlated with the average temperature of the previous month. It is an unfortunately common problem when dealing with time series and spatial statistics. The gist of the issue is that most of the standard statistical analysis techniques such as ANOVA, regression, and the like assume that variation in the data is random or white noise when calculating standard errors and p-values. Autocorrelation means that the noise in the data is not random but correlated or red noise. The degree of correlation reduces the effective size of the data set and means that the standard errors and p-values calculated from normal statistical tests will be lower than they should be and biased toward showing statistical significance when in reality the tests should not show significance. One of the best ways to compensate for autocorrelation is to use an Autoregressive Integrated ...