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The results show that the elimination of herbaceous species already germinated in the field is technically possible, but it requires quite a slow speed of 0.2 m s−1 and tractors with a minimum power of 48 kW.
Analysis of the statistical test power was verified with a minimum power of 0.86.
A total of 210 patients will be recruited to detect a non-inferiority margin of 15% between the two treatments with a minimum power of 80% considering an alpha error of 2.5% for a unilateral hypothesis and maximum possible losses of 15%.
Also, while the minimum power guarantee is beneficial for observing a range of possible effect sizes, the experiments in this replication, and all experiments in the project, are designed to detect the originally reported effect size with a minimum power of 80%.
Considering a success rate of 85% for the group treated with amoxicillin [ 20, 21] a total of 105 patients will be required in each treatment group (total of 210) to detect a non-inferiority margin of 15% at maximum between the two treatments with a minimum power of 80% considering an alpha error of 2.5% for a unilateral hypothesis and maximum possible losses of 15%.
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Statistical power analysis was employed to calculate the delta value required (i.e. effect size) to achieve a minimum power of 80% with the specified cohort sample size (α = 0.05).
This recalculation showed that the current study had a minimum power of 90% with a confidence level of 95% to detect a mean difference of 1.0 (SD: 2.7) in DALY losses between the groups with the smallest sample of 150 participants per group as a minimum.
It was determined that a site-specific minimum of 350 students would achieve a minimum power of 91%.
It can be seen that the CDF distribution of SINR values reach to similar range levels for both cell-center and cell-edge users after taking the proposed algorithm, which shows that the rate close to each other playing the same role with a minimum power, improving users' fairness of experience.
This requires a minimum power density of 10W/kg.
The RIP is a sufficient condition that provides us with the maximum and minimum power of the samples with respect to the input power and ensures that none of the k-sparse inputs fall in the null space of the sampling matrix.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com