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NPS was obtained using the autocorrelation function technique.
Evaluation of the dominant slug frequency was completed using the autocorrelation coefficient and power spectrum.
Stationarity was assessed using the autocorrelation function and the augmented Dickey Fuller test.
Data were fit using the autocorrelation function with polystyrene as a mass model.
We then used the autocorrelation function in SPSS version 13 (SPSS Inc. Chicago) to estimate autocorrelation coefficients, and for the death vs. birth time series the crosscorrelation function.
The size of the exclusion zone in the autocorrelation was quantified using the effective radius (ER) as previously described [43].
surface roughness (parametrized using the root mean square roughness and autocorrelation length), number of particles and their size (parametrized using the surface coverage and the particle radius), AFM tip shape (parametrized using the tip radius and the apex ratio).
To test for spatial autocorrelation, we used Moran's I test and local tests of spatial autocorrelation using the local index of spatial autocorrelation statistic (14, 15).
We used the technique radius matching with a propensity score radius of 0.1 [ 25].
An autocorrelation analysis was performed to explore whether the monthly HFRS cases were affected by the cases in previous months, by using the Ljung-Box Q test, autocorrelation coefficient (AC) and partial autocorrelation coefficient (PAC).
Data were corrected for first-order serial autocorrelations using the AR(1) model in SPM5.
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