Explain Bivariate Analysis.

Bivariate analysis is a statistical method used to examine the relationship between two variables in a dataset. It focuses on understanding how changes in one variable are associated with changes in another variable. The primary goal of bivariate analysis is to explore the strength, direction, and significance of the relationship between the two variables.

Key aspects of bivariate analysis include:

• Correlation: Bivariate analysis often involves calculating correlation coefficients (e.g., Pearson correlation, Spearman rank correlation) to measure the strength and direction of the linear relationship between two continuous variables.
• Comparison of Means: When one variable is categorical and the other is continuous, bivariate analysis can involve comparing means or averages of the continuous variable across different categories of the categorical variable (e.g., t-tests, ANOVA).
• Visualization: Bivariate relationships can be visualized using scatter plots, line graphs, or box plots to visually assess patterns and trends in the data.
• Hypothesis Testing: Bivariate analysis may also include hypothesis testing to determine whether the observed relationship between the two variables is statistically significant.

Overall, bivariate analysis provides insights into the association between two variables, helping researchers understand patterns and dependencies in the data and informing further investigation or modeling of more complex relationships.