Sentence examples for regression was preferred from inspiring English sources
Simple regression was preferred as a high correlation was found between the h- and the m-index (r = 0.91, p<0.001).
As shown in table 3, a random intercept model with a fixed effect of MHS, and not ordinary logistic regression, was preferred for all eight MHS.
Robust regression was preferred as it permitted efficiency in the modeling of all data points in subjects with any CAC and yielded meaningful results expressed as absolute means, rather than less meaningful logarithmic means.
Negative binomial regression was preferred over Poisson regression wherever there was over-dispersion with the Poisson model (i.e. the variance in the mean number of heterozygous SNPs within MIs being substantially higher than the mean).
Logistic regression was preferred over linear discriminant analysis (LDA), because it does not require the independent variables to be normally distributed, linearly related, nor of equal variance within each group [ 24].
Logistic regression was preferred above Cox regression analyses because we were mainly interested in estimating the absolute risk difference (and the effect of known confounders on this risk difference), rather than assessing the relative risk of receiving benefits for a person at risk per unit time.
The use of the PWRMA regression is preferred for determining the LMWL for the Tianshan Mountains, and results in a regional meteoric water line of δD = 7.9δ18O + 10.16.
Negative binomial regression is preferred over Poisson regression for modeling the count data because of the overdispersion that is common with actual research data [ 40, 77].
When multiple classes of the dependent variable can be ranked, then ordinal logistic regression is preferred to multinomial logistic regression [ 61].
In terms of variable selection, BMA and LASSO regression are preferred with lower FPRs, high TPRs, and average model sizes close to the true one.
For model (f_{i},;i > 1), auto-regression is preferred considering practical problems such as computational efficiency and storage space for online systems.
