Test specimens according to ASTM D638 standard were fabricated using glass filled polyamide (PA 3200GF) material, and regression models were developed to correlate the process parameters to the respective experimental design response.
Using multivariate analysis and a training set of known material mixes, a regression model can be built for predicting the quantity of illicit drugs in an 'unknown' sample (Luggar 1996b; Speller 2001).
We employed a multinomial logistic regression model (see Material and Methods) to use the FVI as a predictor for the mouse virulence class of the mutants.
A low-cost Turgo turbine is built and tested from readily available materials and a second order regression model is developed to predict its efficiency as a function of each parameter above and their interactions.
This result is again not an artifact of intergenic distance (Poyatos and Hurst 2007) or of the coexpression level of these genes (P = 2.9 × 10−7 in a logistic regression model, see Materials and Methods for details).
The apoptotic response in the 30th line is then estimated using the expression of those N genes and a predictor based on a multiple regression model (see Materials and Methods for details).
Another set of algorithms is proposed to connect the material processing parameters with the resulting microstructure by mapping nonlinear, nonphysical regression parameters in microstructure correlation functions to a physically based, simple regression model of key material characteristic parameters.
Between-array-section quantile-normalization was performed within each starting material level, and a non-linear regression model fitted across dilution levels within each starting RNA level.
For two key endpoints, ACR50 response and DAS28 change, supportive post hoc analyses used propensity scores [ 11] computed using a logistic regression model (Electronic supplementary material (ESM) Table S1).
The two surfaces were highly correlated after assignment to the subject (Pearson's r = 0.73), and were averaged for inclusion in the regression model [see Supplemental Material, Appendix, for more detail (http://www.ehponline.org/members/2009/11533/suppl.pdf)].
Briefly, the top 50 core samples were selected by their highest SW of all five subtypes, and subdivided into two sets of data: training set A and training set B. These training sets were utilized to determine appropriate parameters (Supporting Information Table 17; Supporting Information Materials and Methods) for the binary regression model.
