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Sampling seven storms is the most efficient method for attaining small confidence interval width for annual concentration.
The percent mortality model resulted in pseudo median differences between observed and predicted values of 1.1%, RMSE of 12.4%, and 95% confidence interval width of 4.6%.
The optimal strategy was used to determine the appropriate number of storms to sample annually based upon confidence interval width for estimates of annual mass emissions and concentration.
When the sampling variation is considered, the variance of the sample sensitivity is slightly inflated, but its confidence interval width becomes widely dispersed.
A method for sample size calculation for ICC was suggested in [27], which is based on determination of necessary sample size to achieve pre-specified precision of ICC given by a corresponding confidence interval width.
Based on the spatial variance observed in the June nested sampling, and given a budget of £5000, the NO3− field mean could be estimated with a 95% confidence interval width of 1.70 μg N g−1 using 2 randomly positioned data loggers each with 5 sensors.
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The sub-canopy count models resulted in mean differences of 0.1 1.4 trees, RMSE of 3.0 13.4 trees, and 95% confidence interval widths of 1.1 5.0 trees, depending on species.
Confidence interval widths differed between states due to varying numbers of linked motorcyclists in each state and other state-specific factors.
Our PCC models resulted in pseudo median differences between observed and predicted values of 0.2 6.5%, RMSE of 10.9 17.0%, and 95% confidence interval widths of 4.4 24.9%, depending on the species.
Despite these limitations, the precision of the genetic estimate is comparable to that of traditional nest count estimates used to evaluate ape population size, for which 95% confidence interval widths (from lower to upper confidence bound) of 40% to 63% of the estimate are reported [14], [15].
Coverage probabilities and confidence interval widths were reported for the commonly encountered two-way, crossed-effects linear model without interaction.
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