PCURIN03 was micro-sampled at 0.8-mm intervals, and the Sr/Ca ratios were measured to determine sample age using the method of Hirabayashi et al. (2013).
G*Power (Faul, Erdfelder, Buchner, & Lang, 2009) was used to determine sample size.
In order to determine sample size, we used the primary outcome of the CCQ score.
In Section 5, the algorithm to determine sample standards is described.
A priori power analysis was performed by using an online calculator (http://statpages.org/index.html#Power) to determine sample size needed for the binary logistic regression analysis.
We determine sample breakdown points of FUNTA and compare its performance with respect to outlier detection in simulation studies and a real data example.
We show how those correlations can be reduced using regression adjustments and used to determine sample size for future school-based smoking prevention studies.
Frequency response analyzer (FRA) algorithm was used to obtain impedance spectra so as to determine sample conductance and capacitance for evaluation of phthalate concentration in the sample solution.
Cluster sampling methodology (Bennett et al. 1991; Thrusfield 2007) was used to determine sample size, fixed to fulfil the trypanosomiasis survey assumptions in March and human brucellosis survey assumptions in June to October.
To determine sample homogeneity and reproducibility, duplicate measurements where taken on each sample, and the results show that for elements found with greater than 2 atomic percent the signal variance was 1.5%—well within the accepted range of 5%[67]].
Several researchers have quantified the absolute intensity of this signal, to determine sample thickness (of single-element samples) [14 17] and composition (when the sample thickness is well known) [18, 19].
