Examples of 'multivariate model' in a sentence
Meaning of "multivariate model"
A multivariate model is a statistical or mathematical representation that analyzes the relationships and interactions between multiple variables at the same time. It allows for the examination and prediction of outcomes based on the influence of several factors or variables simultaneously. Multivariate models are used in various fields, such as economics, finance, social sciences, and data analysis, to understand complex relationships and make informed predictions
How to use "multivariate model" in a sentence
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multivariate model
The multivariate model included variables with p.
This is not confirmed in the multivariate model.
The multivariate model selected by the process variables stepwise.
This was confirmed in the multivariate model.
In the multivariate model osteoarthritis ceased to have a significant effect.
All independent variables were included in the multivariate model.
The associations within each multivariate model were considered significant if p.
Genetics parameters were estimated by bayesian analysis with a multivariate model.
Associations within each multivariate model were considered to be significant when p.
The data was organized in panels and were analyzed in an econometric multivariate model.
The final multivariate model only contains significant variables.
The backward method was applied to select variables in the multivariate model.
All variables were taken to the multivariate model kept in the value of p.
All features were selected for inclusion in the initial multivariate model.
The data were not analyzed in a multivariate model due to the study design.
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In this multivariate model all domains and facets of quality of life were inserted.
All variables of the simple model were selected for the multivariate model.
The final multivariate model includes the four indicators of social capital.
A system according to the present invention may use a multivariate model to provide information about a process.
The multivariate model included all variables associated to both outcome and exposure p.
The risks factors adjusted in a multivariate model for nec were first minute apgar sc.
The most parsimonious Cox regression model was used for the final multivariate model.
Further this multivariate model can make some justified estimates of the fractions of the maltodextrins.
However, this variable did not remain in the multivariate model.
We develop a multivariate model for predicting population dynamics of Dreissena in novel lake systems.
Table 3 depicts the significant variables in the multivariate model.
The multivariate model supports this for both PGS and CGS.
Consequently, these disorders were not included in the multivariate model.
The multivariate model was built using a forward-fitting approach.
After bivariate analyses, all variables were included in the multivariate model.
Figure 3 illustrates a multivariate model to distinguish between live fingers and other objects.
Then, a stepwise procedure allowed obtaining the final multivariate model.
Nonsignificant variables at multivariate model were removed from sequential deletion analysis, generating a new explicative model.
The spatial occurrence is evaluated using a multivariate model ( logistic regression ).
Multivariate Model 3 reports hazard ratios following addition of echocardiographic LVSD to model 2.
After the bivariate analysis, we included all variables in the multivariate model.
Generally the process for identifying the multivariate model for predicting survival included the following steps,.
As used herein, a composite variable may be the principal component of a multivariate model.
The variables included in the multivariate model of multinomial logistic regression are presented in Table 3.
In some configurations, systems according to the invention may create a multivariate model in real time.
Goodness-of-fit of the multivariate model was evaluated by the Hosmer and Lemeshow test.
However, individual analysis of each AADL was not significant in the multivariate model.
Third, for both continuous measures a separate multivariate model was carried out using a three factorial ANOVA.
Therefore, only weight and time of CPB were assessed in the multivariate model.
The multivariate model presented better accuracy than isolated TDI parameters for the diagnosis of CR > 3A.
We also tested the influence of individual factors in life expectancy in a multivariate model Table 2.
The variables included in the multivariate model were all those with p < 0.1 in the univariate analysis.
The MET-AMI variable was tested again using the Cox multivariate model.
For males, this multivariate model is presented in Table 2.
RP, Prevalence ratio obtained from the Poisson multivariate model.
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Examples of using Multivariate
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It does not engage in multivariate analysis or extensive interpretation
Multivariate analysis was performed by means of logistic regression
To answer this question a multivariate analysis was performed
