Association can be defined as a general term used to describe the relationship between two variables, where changes in one variable can affect the other variable. In other words, association measures the extent to which two variables are related.

**Types of Association**

Associations can be positive, negative or zero, indicating the direction and strength of the relationship. For instance, in a study investigating the association between exercise and heart disease, researchers may determine that there is a negative association between the two variables. This would mean that as exercise increases, the risk of heart disease decreases. On the other hand, a positive association may be found between smoking and lung cancer, as smoking increases the likelihood of developing lung cancer. Essentially synonymous with correlation.

**Explanation**

The association is a measure used to quantify the degree of relationship between two or more variables. It is important to note that correlation does not necessarily imply causation. Just because two variables are associated does not mean that one variable causes the other. Other factors may be at play, known as confounding variables, which can affect the relationship between the two variables.

Overall, understanding association is crucial in many fields, such as health sciences, psychology, and social sciences. By identifying associations between variables, we can gain valuable knowledge and insights into complex systems and phenomena. It is an important concept in data analysis as it can help identify underlying patterns and relationships between different elements within a dataset.

**Association in Statistics**

In statistics, the association is a measure of the relationship between two variables. It is used to determine whether a change in one variable has an effect on another. For example, if you wanted to know if there was a correlation between test scores and student age, you would use association to help answer the question. Association is measured using correlation coefficients which range from -1 to +1. A coefficient of -1 indicates perfect negative linear association (i.e., as one variable increases, the other decreases), while a coefficient of +1 indicates perfect positive linear association (i.e., as one variable increases, the other also increases). Values close to 0 indicate weak or non-existent relationships. In addition to correlational techniques, the association can also be studied using regression analysis and contingency tables.

Regression analysis helps predict how much one variable changes when another does, while contingency tables summarize frequencies of observations for two variables at once. Apart from measuring associations between variables, statisticians also use association measures such as odds ratios and relative risk ratios to compare groups with different characteristics (such as gender and smoking status) with respect to health outcomes or the likelihood of developing certain diseases. These measures are used in epidemiological studies that investigate the effects of lifestyle factors on health outcomes. Finally, statisticians also use correlation and regression techniques to study the strength of associations among multiple variables in multivariate analysis. This type of analysis helps them identify patterns in data sets with many features and can be extremely useful for understanding complex systems such as climate models or economic markets where multiple factors influence each other simultaneously.