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Thus, if this statistic decreases over time (sigma convergence), it means that the different provinces are allocating a more similar weighting to each specific group, which constitutes a neat convergence process in the different provinces' shopping basket.
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After removing the cluster, the χ2 statistic decreases to χ2 = 0.871 with 4 degrees of freedom, and then the hypothesis cannot be rejected with a 99% significant level.
The maximum amplitude of the simplified statistic decreases slightly due to the cross-correlation of the C/A codes, which means that the detection performance of the GLRT-based statistic is little better than those of the simplified statistic.
After the declustering process, the χ2 statistic decreases to χ2 = 0.010 with 1 degree of freedom for Area 5, and χ2 = 0.060 with 3 degrees of freedom for Area 6.
It can be observed that the estimation variance of the test statistic decreases with an increasing number of samples N, which is to be expected since asymptotically the sample covariance matrix converges to the statistical covariance matrix.
The value of the GIF statistic decreases with increasing genetic distance between individuals; unrelated individuals have 0 relatedness.
The Akaike Information Criterion AICC), which attempts to include both the estimated residual variance and the model complexity in one statistic, decreases as the residual variance σ ^ 2 decreases and increases as the number p of parameters increases.
The Akaike Information Criterion (AIC), which attempts to include both the estimated residual variance and the model complexity in one statistic, decreases as the residual variance S e decreases, and it increases as the number of parameters p increases.
Of interest, the statistical heterogeneity as measured by the I2 statistic decreased (from 58%to32%2%) by reporting results in MID units as opposed to SD units.
Model calibration did not change (Hosmer-Lemeshow fit statistic decreased from 37.56 to 36.51).
That same statistic decreased significantly in the control clinics from 2.20 to 1.79 (t-test: p = 0.000).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com