Examples of 'regression analyses' in a sentence
Meaning of "regression analyses"
regression analyses - Refers to statistical methods used to analyze the relationship between variables and predict outcomes. It involves examining the impact of multiple independent variables on a dependent variable
How to use "regression analyses" in a sentence
Basic
Advanced
regression analyses
Regression analyses were performed in a forward stepwise fashion.
Multiple logistic regression analyses were carried out.
Hypotheses were tested using hierarchical regression analyses.
Several linear regression analyses were performed.
Regression analyses are able to adjust for these differences.
Variables were examined with regression analyses.
Multiple regression analyses indicate that.
Thus this group was excluded from regression analyses.
Hierarchical regression analyses were performed.
Data wereprocessed by multiple regression analyses.
Logistic regression analyses showed that significantly higher levels of.
Hierarchical multiple regression analyses were used.
Independent variables analysed in the multivariate logistic regression analyses.
Three multivariate regression analyses were made.
Age remains significant in multiple logistic regression analyses.
Hierarchical regression analyses were conducted.
To answer this question we performed hierarchical regression analyses.
Two multivariate logistic regression analyses will be conducted.
Linear regression analyses were performed for continuous variables to evaluate associations.
Typical curve fitting or regression analyses may used.
Regression analyses reinforced this evidence for persistence and predictability.
This is exactly what our regression analyses indicated.
Logistic regression analyses were performed for alcohol and cannabis separately.
Bivariate and multivariate logistic regression analyses were conducted.
Regression analyses indicated that enjoyment was a significant predictor of sport commitment.
Number included in logistic regression analyses.
Multiple logistic regression analyses controlled for potentially confounding factors.
Connections were studied with correlation and regression analyses.
Correlation and weighted regression analyses were the key analytic techniques.
The hypotheses were tested using linear regression analyses.
Quantitation was performed with regression analyses of the external calibration standards.
The outcome variable had normal distribution in the regression analyses.
Univariate linear and multivariate regression analyses were performed where appropriate.
Associations were examined using linear and logistic regression analyses.
There are several types of linear regression analyses available to researchers.
Groups were compared using univariate and multivariable logistic regression analyses.
Regression analyses were conducted to predict which variables made a difference for survival.
Interactions are often considered in the context of regression analyses or factorial experiments.
Multivariate regression analyses were performed to identify risk factors associated with hyponatremia.
The second stage of the analysis consisted of a series of multivariate regression analyses.
Univariate and multiple logistic regression analyses were used to determine predictors of depression.
Seedling stem volume at time of planting was used as a covariate in the regression analyses.
Univariate and multivariate regression analyses were performed to determine predictors of coronary heart disease.
Components of the analytic approaches utilised in the multi variatelogistic regression analyses.
Multiple regression analyses of the prevalence ratios for the factors in question was performed.
The data were analyzed with hierarchical regression analyses and correspondence analysis.
Logistic regression analyses indicate that few of these variables resist multivariate analyses.
An examination of residues will provide an assessment of model assumptions for the regression analyses.
All linear and logistic regression analyses were adjusted for potential confounders.
See linear regression for discussion of statistical evaluation of parameter estimates in regression analyses.
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Analyses are undertaken for two types of scenarios
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Your regression models are the stuff of legend
Inhibition results in the regression of prostatic tumours
Regression analysis was used to estimate this effect
