DESCRIPTIVE STATISTICS
Descriptive statistics is the process of organizing, describing, and presenting data based on different characteristic metrics that can be used to describe its position, dispersion, shape, or association between other variables
Measures of central tendency
Mean
The mean or average is a central value widely used to characterize a distribution. It can be arithmetic, weighted, truncated or geometric
Used with: quantitative variables
Mode
The mode is the most frequent value or values of a data set and doesn't have to be unique
All types of variables
Median
The median is the central value of the distribution that splits the data in two halves when the data is in increasing order
Used with quantitative and qualitative ordinal variables
Measures of position
Measures of dispersion
Range
The range is the simplest measure of dispersion: the difference between the maximum and minimum values in a dataset
Used with quantitative variables
Standard Deviation and Variance
Learn about standard deviation and variance in statistics, their definitions, properties, and examples
Coefficient of Variation
The coefficient of variation measures relative variability, allowing comparison between datasets with different units or scales
Used with quantitative variables
Measures of shape
Coefficient of skewness
The coefficient of skewness is a key measure used to describe the asymmetry of a data distribution
Coefficient of kurtosis
Kurtosis is a measure that describes the shape of a distribution's tails in relation to its overall peak
Association between variables
Covariance
Covariance measures the direction of the linear relationship between two variables
Used with quantitative variables
Pearson correlation coefficient
The Pearson correlation coefficient measures the strength and direction of the linear relationship between two quantitative variables
Used with quantitative variables
Pearson's chi-squared
Pearson's chi-squared test determines whether there is a statistically significant association between two categorical variables
Cramér's V
Cramér's V measures the strength of association between two categorical variables, ranging from 0 (no association) to 1 (perfect association)