Examples of 'univariate and multivariate' in a sentence
Meaning of "univariate and multivariate"
Univariate and multivariate: Univariate refers to the analysis of a single variable, while multivariate involves the analysis of multiple variables simultaneously. These terms are commonly used in statistics and research to describe the scope of analysis regarding data and variables
How to use "univariate and multivariate" in a sentence
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univariate and multivariate
Classification of univariate and multivariate time series.
Univariate and multivariate risk factor analyses were performed.
We used logistic regression univariate and multivariate analysis.
Both univariate and multivariate cases need to be considered.
This was found in both univariate and multivariate analyses.
Univariate and multivariate statistics were used.
The second study was analyzed by univariate and multivariate techniques.
Univariate and multivariate logistic regression models were used.
Data analysis involved routine univariate and multivariate procedures.
Univariate and multivariate analyses were performed with logistic regression.
The variables were compared in both univariate and multivariate analyses.
Univariate and multivariate strategies were used in the methods optimization.
The course is an introduction to univariate and multivariate time series models.
Univariate and multivariate represent two approaches to statistical analysis.
Statistical methods may involve both univariate and multivariate techniques.
See also
Univariate and multivariate data.
All anthropometric variables studied were submitted to univariate and multivariate analyses.
Results indicated univariate and multivariate normality of data.
Deaths due to tuberculosis and other causes were excluded from univariate and multivariate analyses.
Univariate and multivariate logistic regressions were performed to identify risk factors.
Descriptive analysis and poisson regression univariate and multivariate analysis were held.
Univariate and multivariate regression analyses were performed to determine predictors of coronary heart disease.
Statistical analyses included multiple univariate and multivariate data analysis techniques.
Univariate and multivariate analyses were conducted by generalized linear models for discrete variables.
Regarding the interpretation of analytical data univariate and multivariate statistical methods were used.
Univariate and multivariate analyses revealed the reference diameter as an independent predictor of restenosis.
The experimental variables were optimized using univariate and multivariate methods mixture of design.
Univariate and multivariate logistic regression were estimated to access the independent effects of explanatory variables.
The carotid atherosclerosis plaque variables were identified in univariate and multivariate regression analysis.
Univariate and multivariate logistic regression were used to identify factors associated with hospital mortality.
Sets of central moments can be defined for both univariate and multivariate distributions.
Univariate and multivariate analyses identified associations among population characteristics and screening status.
There was also a higher mortality by both univariate and multivariate analysis in patients with arrhythmia.
Univariate and multivariate logistic regression identified associations between independent variables and favorable results and conclusions.
Descriptive statistics and stepwise univariate and multivariate logistic regression analyses were performed.
There were no material differences between the ORs resulting from univariate and multivariate analyses.
Statistical analysis included univariate and multivariate analysis to assess for prognostic significance.
The modelling approach used here permits a straightforward comparison between the univariate and multivariate solutions.
Univariate and multivariate Cox regression analysis.
We compare the predictive ability of the model with other univariate and multivariate specifications.
Table 4 shows the univariate and multivariate linear regression analyses.
The factors associated with AKI and death were investigated through univariate and multivariate analyses.
Table 2 showed the results of univariate and multivariate logistic regression analyses.
Description of Analyses Analysis of the questions entailed both univariate and multivariate tests.
With the rANOVA, standard univariate and multivariate assumptions apply.
In predicting genetic diversity, we used different methods univariate and multivariate.
Regression models were performed on univariate and multivariate bases, adjusted to age at diagnosis.
Univariate and multivariate analyzes were performed, adjusting the model for length of job.
The method of optimization studies were performed using univariate and multivariate methods mixture design.
For this, univariate and multivariate analysis, and also bioeconomic models were used.
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Examples of using Univariate
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Univariate and bivariate descriptive analyses were performed
Other descriptive univariate analyses were also obtained
Univariate analysis was performed for each variable
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
