As the algorithm relies on linear relationships between genes, its inability to detect these two connections may be a result of the non-linear variability between Ngn2 and its targets that is created by the large increase in Ngn2 transcript levels after electroporation.
It is shown that when the test results are analyzed using a simplified linear analysis, the variability between the results of tensile and flexural strength can be as high as 200 300%.
The sampling employed a spatially stratified random sampling design in order to estimate the variability between 40-km linear sampling units, within them, at sample sites, and due to sample preparation and analytical chemical procedures.
Although this study and our previous study employed very different strain backgrounds, the generated data were very linear, indicating an absence of random variability between the two small scale studies.
Additionally, the distribution of the sugar concentrations was investigated by constrained linear ordination (direct gradient analysis), using Redundancy Analysis RDAA) to analyze the variability between Bt and non-Bt plants and/or among varieties based on the composition of different sugars.
To formally examine the types of loci demonstrating variability between donors and recipients, we employed a linear mixed effects model, including random effects for pair membership and donor-recipient status within a pair.
Both mean allele number and expected heterozygosity showed a non-linear pattern, with a higher rate of decline in genetic variability between generations 3 and 14.
Being structured on several levels that account for variability between and within students, the resulting data allows for hierarchical linear modelling and other complex analysis strategies.
The data were normalized using Robust Multiarray Averaging (RMA) method and a linear model was then used to average data between replicate arrays and to look for variability between them [ 7].
A linear model was fitted to the data and used to average data between replicate arrays and to identify variability between them.
Variability between each calibration set is reduced by over 40%% when modeled as quadratic curves compared with linear models using the same data.
