Which term describes the measure of how two variables change in relation to each other?

The term that describes the measure of how two variables change in relation to each other is correlation. Correlation quantifies the degree to which two variables are related and how they can influence each other. It is typically represented as a coefficient that ranges from -1 to 1, where a value close to 1 indicates a strong positive relationship, a value close to -1 indicates a strong negative relationship, and a value around 0 suggests no linear relationship.

Understanding correlation is fundamental in statistics and data science because it helps in identifying patterns and making predictions based on the relationships between variables. In various applications, such as financial analysis or scientific research, assessing the correlation between factors can lead to insightful conclusions regarding causality and dependence.

The other terms, while important in their contexts, do not specifically measure the relationship between two variables. Regression involves modeling the relationship between dependent and independent variables to make predictions. Variance measures how much a set of data points differs from the mean, reflecting the data's spread rather than its relationship. Normalization is a data preprocessing technique used to adjust the values in the data set to a common scale, without any particular focus on the relationship between variables.