Sentence examples for freedom for error from inspiring English sources
Researchers and journals need to be more rigorous in providing statistical information including: degrees of freedom for error terms, treatment variances, standard error of differences or similar to enable readers to compare treatment effects.
where Ve is the degrees of freedom for error = 8, F (1,Ve) is the F value for 95%% CI = 4.4138, Ve is the variance of error = 0.499, ( {n}_{mathrm{eff}}=frac{N}{1+v} ), N is the total trial number = 27, V is the degrees of freedom of p process parameters = 8, and η ver is the validation test trial number = 3.
Critical values for the number of means in the range between and including the means being tested are obtained from the tabulated upper percentage points of the studentized range with 120 degrees of freedom for error.
Test-retest reliability of pre-test Short Form McGill Pain Questionnaire scores (SPRI, APRI, Total PRI, VAS, and Overall Intensity of Total Pain Experience) was assessed by Intraclass Correlation Coefficients (ICC) [ 15]: (1) Where and ; DF - degree of freedom for error term related to the independent variables of "subject", different "test", "repeat" and the overall "error" term.
The IHC scores of the different tumor grade groups and the non-cancerous group were evaluated by ANOVA (single-factor) with ranked prostasin staining data (rank 1 = no staining, rank 2 = positive staining), yielding an F ratio of 15.46 (F critical = 2.74, p = 8.57E-08, and within-group degrees of freedom for error at 69).
So for a 60 year old male diabetic: ξ DM = ln(OR for diabetics)* individual value - population prevalence) ξ DM = ln(2.37)*(1 - 0.081) = 0.793 The model was implemented first in Matlab and then in Microsoft Excel to ensure freedom for errors.
where α is the level of risk, Ve is the error variance, νe is the degrees of freedom for the error, η eff is the effective number of replications, and r is number of test trials.
Due to insufficient design freedom for absolute error decoupling, we turn to designing an observer-based stochastic H∞ filter.
Quite intuitively, the loss of degrees of freedom for the error becomes relatively more important the lower the sample sizes.
This is because there are fewer degrees of freedom for the error when testing individual predictors in a multiple-predictor model rather than individually.
