Univariate analysis is a fundamental step in statistical analysis, focusing on understanding individual variables within a dataset. It involves techniques such as calculating measures of central tendency (mean, median, mode), variability (standard deviation, variance, range), and visualizing data distributions using histograms or bar plots. Outlier identification is also crucial for univariate analysis. This initial exploration helps to build a solid foundation for further multivariate analyses, modeling, and exploratory data analysis. Univariate analysis provides insights into the distribution, central tendency, and spread of each variable, which are essential for validating assumptions, identifying missing values, and ensuring the reliability of more complex models.