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We first present a quantitative linear model for understanding the generation of mixed and low template STR data.
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Such linearly generated DNA data can be mathematically described through a quantitative linear model [8], [9].
Multivariate analyses were performed using logistic regression for qualitative variables and a general linear model for quantitative variables; the corresponding results were expressed as Odds-ratio (OR) and [95% confidence interval] or as adjusted mean ± standard error.
More formally, given a set of N individuals, P SNPs, and K confounders, a linear model for quantitative outcome vector y (N × 1), with a matrix of SNP data X (N × P), and a matrix of confounders Z (N × K) as predictors, is given by (1) y = X β + Z γ + ε, where β is the vector of SNP effects, γ the vector of effects of the confounders, and ε the phenotype noise.
The hourly recorded Raman spectra are characterized using factor analytic methods and a linear model for quantitative determination of the apparent elemental carbon (EC) filter loading is derived.
A linear model for estimating the OD600 values according to the metabolite quantities in individual time points provides the useful information associated with quantitative differences of the metabolite between exponential and stationary phases.
This test assumes a linear model for gene expression levels.
Logistic regression was used to test for association with EH and linear models for quantitative analyses.
A quantitative kinetic model for transcription elongation.
For quantitative traits, a linear model is used.
LR is a generalized linear model used for binomial regression.
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