Univariate analysis is a statistical method that involves the analysis of a single variable or feature at a time. In other words, it focuses on examining the distribution and characteristics of one variable in isolation, without considering the relationships between multiple variables. Univariate analysis is a fundamental step in exploratory data analysis and is used to understand the basic properties of individual variables.

Key aspects of univariate analysis include:

1. Descriptive Statistics: Descriptive statistics are used to summarize and describe the main features of a single variable. Common measures include mean, median, mode, range, variance, and standard deviation.
2. Visualization: Visualization techniques, such as histograms, bar charts, box plots, and probability density functions, are employed to visually represent the distribution and patterns within the data.
3. Central Tendency: Central tendency measures, such as the mean, median, and mode, help identify the central or typical value around which the data points tend to cluster.
4. Dispersion: Measures of dispersion, such as range, variance, and standard deviation, provide insights into the spread or variability of the values within the variable.
5. Skewness and Kurtosis: Skewness measures the asymmetry of the distribution, indicating whether the data is skewed to the left or right. Kurtosis measures the "tailedness" or shape of the distribution.

Univariate analysis is often the initial step in the data analysis process and is followed by bivariate and multivariate analyses, where relationships between two or more variables are explored. Univariate analysis is particularly useful for gaining insights into the characteristics of individual variables, identifying outliers, and understanding the overall distribution of data.

Examples of univariate analysis include:

• Analyzing the distribution of ages in a population.
• Examining the frequency distribution of exam scores.
• Investigating the patterns of a single sensor's readings over time.

While univariate analysis provides valuable insights, a comprehensive understanding of complex systems often requires considering the relationships between multiple variables through multivariate analysis.