Explain Bivariate Analysis.

Bivariate analysis is a statistical method used to explore the relationship between two variables in a research study. It aims to understand how changes in one variable may be associated with changes in another. Here's a concise explanation:

1. Two Variables: Bivariate analysis focuses on the examination of two variables simultaneously. These variables can be any measurable factors, such as age and income, height and weight, or time and performance.

2. Types of Variables: The variables under study can be of different types: quantitative (numerical) or categorical (qualitative). Bivariate analysis techniques differ based on the types of variables involved.

3. Correlation or Association: Bivariate analysis helps to determine the degree and direction of association or correlation between the two variables. It explores whether there is a positive, negative, or no correlation.

4. Graphical Representation: Common methods of bivariate analysis include scatter plots, line charts, and cross-tabulations. These visual representations provide insights into the patterns and trends between the variables.

5. Statistical Tests: Various statistical tests, such as correlation coefficients, chi-square tests, or t-tests, are employed in bivariate analysis to quantify the strength and significance of the relationship between the variables.

Bivariate analysis is a foundational step in statistical exploration, offering insights into the nature of relationships between two variables and informing further multivariate analyses in research and data interpretation.