# Types of Relationship

Number of hours of practice of the players is related to the number of medals in the sports meet. Similarly, marks in the final exams are related to number of hours of study. Such examples of relationship may be given a cause and effect interpretation.

Whereas a few others may be just coincidence. The relation between the visitors in the zoo and rainfall in the locality can not be given any cause and effect interpretation. The relationships are simple coincidence. The relationship between homework given in school and money in your pocket is another such example.

Even if a relationship exists, it is difficult to explain it.

In another instance a third variable's impact on two variables may give rise to a relation between the two variables. Previously we saw that the visitors in the zoo and rainfall in the locality did not have any cause and effect relation. But if we know that many plants flower during the rainy season and this causes an abundance of small birds and variety of butterflies to be active at this time, then we have a third variable here that has an impact on the other two. Now we know that number of visitors increase during the rainy season because the rains cause the butterflies and birds to become active.

# What Does Correlation Measure?

Correlation is a statistical technique that can show whether and how intensely pairs of variables are related.

It is a measure of covariation, not causation which means that it should not be interpreted as implying cause and effect relation. The presence of correlation between two variables A and B simply means that when the value of one variable is found to change in one direction, the value of the other variable is found to change either in the same direction (i.e. positive change) or in the opposite

direction (i.e. negative change), but in a definite way.

Correlation works for data which is quantifiable, such as cost, height, weight, volume, etc. It cannot be used for purely categorical data, such as gender, nationality, caste, or favorite person, etc..

To make it easy for us to understand, we assume that the correlation, if it exists, is linear, i.e. the relative movement of the two variables can be depicted by drawing a straight line on graph paper.

# Types of Correlation

Correlation is commonly categorized as
1. negative correlation and
2. positive correlation.
Correlation is said to be positive when the variables increase or decrease together in

the same direction. For a student when hours of sincere study increases, marks also increase. When study time decreases, marks also decrease. Similarly sale of books and admission of students in a school move in the same direction.

The correlation is negative when they move in opposite directions. For example, sale of blankets increases as the temperature dips. Another example is, increase in number of hours of physical exercise causes decrease in blood sugar levels among diabetes patients. The variables move in opposite direction.