Monte Carlo simulation is a technique that allows resampling from the same population while varying several inputs simultaneously, thus resulting in random multivariate sampling of input variables across their corresponding ranges.
In multivariate analysis, two input variables predicted the aggregated measure of clinical performance: the proportion of breast care nurses in the team and workload (caseload per Whole Time Equivalent team member) positively predicted clinical effectiveness (β=0.376, P=0.003 and β=0.331, P=0.009, respectively).
Only Tumour stage remained significant in a multivariate analysis with the input variables SPHK1 (negative or weak versus moderate or strong), surgical resection margins positive versus negative), histology (squamous versus adenocarcinoma), site (oesophagus versus gastric) neo-adjuvant chemotherapy (yes versus no) and tumour stage (I or II versus III).
The developed multivariate regression models with three controllable input variables, i.e. current density J (A/dm2), temperature T (°C) and pH have been found statistically significant to predict the electroplating responses, such as platting current efficiency Y and the average thickness of Fe Ni deposited layer U (μm).
Input variables for the multivariate analysis were selected from significant variables obtained from the univariate analysis.
Therefore, factor analysis is quite often used on multivariate data for dimension reduction or to condense multiple input variables to several orthogonal and meaningful factors (Hair et al. 1998).
The measures of both input variables and performance variables in our model were multivariate.
Component analysis is a multivariate statistical technique which can be used for reducing complications of input variables when there is a large volume of information and it is anticipated to have an enhanced explanation of variables (Noori et al. 2010).
However, the predicting capabilities of Artificial Neural Networks (ANNs) and Fuzzy Inference System (FIS) as well as the Multivariate Regression (MR) techniques have been evaluated and compared using the same input variables.
The independence of hCGβ as a predictor of survival was analyzed by multivariate analysis using the Cox proportional hazard model with serum hCGβ, nuclear grade and stage as input variables.
