Based on analyzing the existing approaches of thermal error modeling for machine tools, a new approach of regression orthogonal design is proposed, which combines statistical theory with machine structures, surrounding conditions, engineering judgements, and experience in modeling.
Different analysis methods are available when analyzing data with multiple potential predictors, these include tree analysis, neural networks, and the more traditional approach of regression modeling.
We attempted three approaches of logistic regression to analyze the simulated data as below.
To determine whether reproductive and non-reproductive age group women have an association with poor glycemic control, both univariate and multivariate approaches of logistic regression were applied.
Both univariate and multivariate approaches of logistic regression were applied to determine whether reproductive age women have an association with poor glycemic control.
Two complementary approaches of logistic regression analysis and classification analysis (CHAID) (Melchior et al, 2001; Barton et al, 2005; Chan et al, 2006; Ambalavanan et al, 2006; Courville et al, 2009) were used for development of the prognostic models (scoring system and decision tree, respectively).
In this paper, however, we consider a machine-learning approach of logistic regression for location classification.
Afterwards, the models developed using the new approach were compared with the models obtained using only the common modelling approach of the regression analysis.
The table shows the comparison of the Bayesian ZIP and Classical ZIP approach of count regression model with their respective error and with a number of significant variables.
Our multilocus analysis strategy makes use of the traditional parametric approach of logistic regression and the nonparametric method of multifactor dimensionality reduction (MDR) [27].
