Univariate analysis

Univariate analysis is a statistical technique used to analyze a single variable in isolation. It focuses on understanding the distribution and characteristics of a single variable without considering its relationship with other variables. Univariate analysis helps in summarizing the main features of the variable, identifying patterns, detecting outliers, and making preliminary assessments about the data. Here are some common methods used in univariate analysis:

1. Descriptive Statistics: Compute summary statistics to describe the central tendency, dispersion, and shape of the variable’s distribution. Common descriptive statistics include mean, median, mode, standard deviation, variance, range, and percentiles.
2. Histograms: Create histograms to visualize the distribution of numerical variables. Histograms display the frequency or density of data points within different intervals or bins. They provide insights into the shape, skewness, and spread of the distribution.
3. Box Plots: Construct box plots (also known as box-and-whisker plots) to visualize the distribution, central tendency, and variability of numerical variables. Box plots show the median, quartiles, and outliers of the data, making them useful for identifying outliers and comparing multiple distributions.
4. Bar Charts: Use bar charts to visualize the frequency or proportion of categories within a categorical variable. Bar charts display the counts or percentages of each category as bars, allowing for easy comparison between different categories.
5. Pie Charts: Create pie charts to visualize the relative proportions of categories within a categorical variable. Pie charts display the composition of the data as slices of a circle, with each slice representing a different category and its corresponding proportion.
6. Frequency Tables: Construct frequency tables to summarize the counts or percentages of each category within a categorical variable. Frequency tables provide a tabular representation of the data, making it easy to compare the frequencies of different categories.
7. Summary Statistics: Calculate summary statistics such as mean, median, mode, standard deviation, and variance for numerical variables. Summary statistics provide a concise summary of the variable’s distribution and central tendency.
8. Probability Density Function (PDF) and Cumulative Distribution Function (CDF): Plot the PDF and CDF of numerical variables to visualize their probability distributions. The PDF represents the probability density of different values, while the CDF represents the cumulative probability up to a certain value.
9. Measures of Central Tendency: Calculate measures of central tendency such as mean, median, and mode to describe the typical or central value of the variable.

Univariate analysis provides valuable insights into the characteristics of individual variables and serves as a foundation for further analysis, such as bivariate and multivariate analysis. It helps in understanding the data’s structure, identifying potential patterns or trends, and detecting any anomalies or irregularities.