Examples of 'univariate analysis' in a sentence
Meaning of "univariate analysis"
univariate analysis - This phrase refers to a statistical method or technique that involves analyzing a single variable or factor at a time. It is used to examine the characteristics, patterns, or relationships of a particular variable independently, without considering other variables. Univariate analysis is widely used in research, data analysis, and various fields of study, including social sciences, economics, and biology
How to use "univariate analysis" in a sentence
Basic
Advanced
univariate analysis
Descriptive statistics for univariate analysis were used.
Univariate analysis was performed for each variable.
Descriptive and univariate analysis of data were used.
Univariate analysis was performed to describe the study population.
On the basis of a univariate analysis of unselected ethnic.
Univariate analysis was performed using the McNemar test.
Both studies performed only univariate analysis.
A univariate analysis of the data was performed.
These two variables were not significant in univariate analysis.
Descriptive and univariate analysis was performed.
Univariate analysis was used to identify candidate independent predictors.
Multivariate and univariate analysis were performed.
Univariate analysis were performed to assess differences between groups.
The frequency of consumption was investigated by means of univariate analysis.
Univariate analysis of variance was used to test for equality of means.
See also
Result of the univariate analysis.
The univariate analysis associated biomolecular markers with node metastases.
Descriptive and univariate analysis.
The univariate analysis identified possible risk and protective factors.
Also called univariate analysis.
The univariate analysis included the absolute and relative frequencies.
Prognostic factors in univariate analysis.
Univariate analysis was applied in order to compare the above parameters and survival.
The same results could not be obtained by applying a univariate analysis.
Significant variables in the univariate analysis were introduced into the multivariate analysis.
Comparisons between the groups at different moments were carried out by univariate analysis.
The variables that showed significance in univariate analysis underwent logistic regression.
Several studieshave found significant differences related to tumor size in the univariate analysis.
Statistically significant variables in univariate analysis were used to adjust the logistic regression model.
Clinical data and laboratory test results were analyzed statistically using univariate analysis.
We carried out univariate analysis of cofactors that could potentially influence the outcome.
Lymph node involvement was also a prognostic factor in univariate analysis of several studies.
The univariate analysis showed that asthma was not a risk factor for cesarean delivery.
Variables with a significant p value in the univariate analysis were included in the multivariate analysis.
Univariate analysis between the outcome and predictor variables was performed obtaining odds ratio.
This finding was statistically significant in both the univariate analysis and multivariate analysis.
A univariate analysis evaluating the relation between risk factors and level of carotid disease was performed.
Independent variables were chosen based on univariate analysis results and previously known biological relevance.
The fragility of the employment bond was positively associated with smoking in the univariate analysis.
Bivariate analysis can be contrasted with univariate analysis in which only one variable is analysed.
The univariate analysis showed no statistically significant correlation between these variables and auditory temporal processing.
Only those variables with significant results in univariate analysis were included in the multivariate analysis.
Univariate analysis showed that the abundance and richness among the sampled sites was not homogeneous.
This method represented the first unsupervised univariate analysis of MS imaging.
On univariate analysis the SOFA score was found to be a useful predictor of mortality.
Previous tests met the proposed criteria in the univariate analysis ANOVA.
The results of univariate analysis are shown in Table III.
Exposure to severe IPV was a factor associated with aggressive behaviors in the univariate analysis.
Continuous variables significant in the univariate analysis were tested for correlation by the Spearman test.
The association between IPV and individual explanatory variables was assessed in the univariate analysis p.
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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
