Sentence examples for means are generated from inspiring English sources

As Neuman and Sándor pointed out in [5], the first and second Seiffert means are generated by the Schwab-Borchardt mean, because S B ( G, A ) = P ( a, b ), S B ( A, Q ) = T ( a, b ).

The three means are generated from a uniform (−1.5, 1.5) distribution with a probability of 0.6, a uniform (−1.2, 1.2) distribution with a probability of 0.3 and a uniform (−1, 1) distribution with a probability of 0.1, respectively (see online supplementary appendix 2 for this non-normal distribution).

Kolmogorov-Smirnov test for normality of data, analysis of variance of the rice genotypes for the nine strains of Xoo using the mixed model analysis of SAS PROC MIXEDD), and least square means were generated using SAS version 9.3.

Unbiased estimates of biomass or chlorophyll levels (least-square means) were generated for each effect (genotype, concentration, or genotype and concentration) per experiment.

p-Values based on means were generated using analysis of variance (F-test) for continuous data.

Geometric means were generated to analyze cycle threshold (Ct) values; averages were generated to analyze copy numbers and mass values.

Least squared means were generated for all traits and Tukey Kramer adjustments were used for mean separations.

Based on the aforementioned test and the results of general statistics package, graphs and a table representing mean values and standard errors of means were generated.

Data were imported into SAS (version 10.2.1; SAS Institute, Cary, NC) with which an annual mean was generated for each grid point.

To graphically describe the relationship between ESA dose and sEpoR, a scatter plot with a linear regression line (95% confidence intervals of the predicted mean) was generated.

The periodic microstructures considered are generated by means of a statistical microstructure generator.