Multifactor regression and structural equation modeling (SEM) analyses showed that clay content was the most important variable in regulating decomposition of SOC.
Multifactor regression models are limited in their ability to capture all of the relevant factors of transactions, however, because sample sizes are usually much smaller than the factor set, and so a small number of factors are usually selected for evaluation.
Which of these variables contribute independently was analysed using multifactor risk regression analysis.
Statistical analysis of associations between Braak stage, clinical and pathological features was carried out using SPSS (version 16) using Pearson's correlation, ANOVA, paired t-tests and multifactor linear regression analysis; P < 0.01 were considered statistically significant.
Multifactor risk regression analysis identified preoperative neurological pathology (P = 0.02), perioperative detected aortic pathology (P = 0.0001), and a perioperative myocardial infarction (P = 0.04) as independent predictors for postoperative neurological complications.
We evaluated potential gene-gene and gene-environment interactions using a multianalytic strategy combining logistic regression, multifactor dimensionality reduction and classification and regression tree approaches.
An increasing availability of high-throughput SNP data has led to the development of various statistical approaches for effectively analyzing epistasis among multiple polymorphisms, including logistic regression, multifactor dimensionality reduction (MDR), Bayesian analysis, and machine learning [15], [21], [30] [32].
We chose to model the effects of age, smoking, CFH Y402H, ARMS2 A69S, C3 R102G, and CFB R32Q using logistic regression, multifactor dimensionality reduction (MDR), and grammatical evolution of neural networks (GENN) in multiple distinct datasets.
The statistical analysis is based on three methods: unconditional logistic regression, multifactor dimensionality reduction and hierarchical cluster analysis.
Unconditional logistic regression and multifactor dimensionality reduction were programmed in C++.
Given the complementarity of logistic regression and multifactor dimensionality reduction, combining approaches may be an effective option.
