Sentence examples for bivariate analyses to test from inspiring English sources
We first performed bivariate analyses to test for associations between diabetes status and baseline characteristics.
We firstly did bivariate analyses to test for associations between baseline characteristics and race by using χ tests for categorical variables and t tests for continuous variables.
(2) Bivariate analyses to test the relationship between each independent variable and each outcome, using either the Cochrane-Mantel-Haenszel test (to calculate the odds ratios, relative risks), T-test, or ANOVA, depending on the nature of the variables.
Bivariate analyses to test for independent associations between potentially confounding variables and both case status (conditional logistic regression) and circulating 25(OH D concentrations among controls (linear regression) based on the Wald test were conducted.
In two-tailed, χ bivariate analyses to test for significant association between potential covariates and the outcomes, we found the following covariates to be significantly associated with at least one of the outcomes (P < 0.10): age, race/ethnicity, employment, geographic region, residence location, education, household income, primary language, health insurance type, and health status.
We conducted bivariate and multiple regression analyses to test for differences in several variables of interest between controls and cases.
Descriptive frequencies and bivariate analyses were analysed to test for statistical significance by time period of VPD policy implementation (prepolicy vs postpolicy) and reported using ORs, 95% CIs and p values.
We used χ and t tests in bivariate analyses to compare high-cost patients with individuals in the remaining population on the basis of sociodemographic characteristics, chronic conditions and multimorbidity prevalence, using an a priori significance level of p<0.01 to adjust for multiple comparisons.
Second, bivariate analyses were performed to test for associations between the principal outcome (employment status) and each covariate.
Bivariate analyses were performed to test for the independence from tumour stage, nodal status and tumour size.
Second, bivariate analyses were performed to test for associations between survey responses and the principal outcome variable, "serious intent to leave academic medicine".
