Research Topic · Peer-Reviewed

Bivariate Analysis

Bivariate analysis is a class of statistical methods used to examine the relationship between two variables, determining whether they are associated and, if so, the strength, direction, and form of that association. It begins with the joint description of paired observations, often visualized in a scatter plot or su…

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 80× across the literature 🔖 ISSN 2377-2549

Overview

Bivariate analysis is a class of statistical methods used to examine the relationship between two variables, determining whether they are associated and, if so, the strength, direction, and form of that association. It begins with the joint description of paired observations, often visualized in a scatter plot or summarized in a contingency table, and proceeds to quantitative measures such as the Pearson correlation coefficient for linear association between continuous variables, the Spearman rank coefficient for monotonic relationships, and the chi-square statistic for association between categorical variables. Simple linear regression extends bivariate analysis by modeling one variable as a function of another, yielding slope, intercept, and goodness-of-fit metrics that support prediction and calibration. In the chemical and laboratory sciences, bivariate techniques underpin calibration curves, method comparison, dose-response characterization, and the assessment of how one measured property varies with another, while hypothesis tests evaluate the statistical significance of an observed relationship and guard against spurious correlation. Bivariate analysis is frequently a precursor to multivariate modeling, which accounts for several explanatory variables simultaneously. By isolating pairwise relationships, it provides an interpretable foundation for identifying meaningful dependencies, controlling confounding at a basic level, and informing experimental design and data interpretation across quantitative research.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 80 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Nov 2026.

A sample of recent works citing this journal's research on Bivariate Analysis, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Journal of New Developments in Chemistry (ISSN 2377-2549).

Journal editorial board
Zhe-Sheng Chen · United States Abel Jose de Sousa Costa Vieira · Portugal Amirhassan Amiri · Iran, Islamic Republic of

This page summarises published research for orientation; it is not medical or professional advice.