összefüggésmutató

The term *összefüggésmutató* (Hungarian for "correlation coefficient") refers to a statistical measure that quantifies the degree to which two variables move or change in relation to one another. It is a fundamental concept in descriptive statistics and is widely used in fields such as economics, psychology, biology, and social sciences to assess the strength and direction of linear relationships between variables.

The most commonly used correlation coefficient is Pearson’s *r*, which ranges from -1 to 1. A value of 1 indicates a perfect positive linear relationship, meaning that as one variable increases, the other also increases proportionally. Conversely, a value of -1 signifies a perfect negative linear relationship, where an increase in one variable corresponds to a decrease in the other. A value of 0 suggests no linear correlation between the variables. The closer the coefficient is to either -1 or 1, the stronger the linear relationship.

While Pearson’s *r* is the most widely recognized, other correlation coefficients exist for different types of data. Spearman’s rank correlation, for instance, assesses monotonic relationships (whether linear or nonlinear) and is based on the ranks of the data rather than their raw values. This makes it useful for ordinal data or when the relationship between variables is not strictly linear.

It is important to note that correlation does not imply causation. A high correlation between two variables does not necessarily mean that one variable causes the other; it only indicates that they tend to vary together. Additionally, correlation coefficients are sensitive to outliers and assume a linear relationship, so their interpretation should be done cautiously in real-world applications.

In summary, the *összefüggésmutató* serves as a valuable tool for summarizing the strength and direction of relationships between variables, but its results must be interpreted within the broader context of the data and research objectives.