univariáns

Univariant analysis, or univariate analysis, is the statistical examination of data involving a single variable. Its primary aim is to describe the variable’s distribution, central tendency, and spread, providing a succinct summary of the data without considering relationships to other variables.

For quantitative variables, univariate statistics include measures of central tendency such as the mean, median, and mode, and measures of dispersion such as the range, variance, standard deviation, and interquartile range. Shape descriptors like skewness and kurtosis help characterize asymmetry and the heaviness of tails. For qualitative or categorical variables, univariate analysis focuses on frequency counts, relative frequencies, and patterns in distributions, often summarized by the mode and by bar charts or pie charts.

Common graphical tools used in univariate analysis include histograms and density plots to visualize the distribution of numerical data, box plots to display central tendency and variability, and bar charts for categorical data. Stem-and-leaf plots offer an alternative numeric display, particularly in smaller data sets.

Applications of univariate analysis include initial data exploration, data quality assessment, and preparation for more complex analyses. It helps identify outliers, unusual gaps, or skewness that may influence subsequent statistical methods and model assumptions.

Limitations should be noted: univariate analysis does not capture relationships between variables and can be misleading if data are not representative or if outliers disproportionately affect the chosen measures. In practice, both the nature of the variable and the research question guide the choice of statistics and plots.