3 min read

Correlation: types, applications & example

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Correlation is a statistical technique that shows whether and how strongly pairs of variables are related. In simpler terms, it’s a measure that helps us understand the extent to which two things change together.
If knowing the value of one variable helps you predict the value of another, there's likely a correlation between them.

Types of Correlation

1. Pearson Correlation Coefficient (r):

This measures the strength and direction of the linear relationship between two continuous variables. The coefficient’s value ranges from -1 to 1.

+1 indicates a perfect positive linear relationship: as one variable increases, the other variable increases at a consistent rate.
-1 indicates a perfect negative linear relationship: as one variable increases, the other decreases at a consistent rate.
0 indicates no linear relationship between the variables.

2. Spearman's Rank Correlation Coefficient:

This is used for ordinal variables or when the relationship between variables is not linear. It measures the strength and direction of the monotonic relationship between two variables by using their ranks.

3. Kendall’s Tau:

Another rank-based correlation coefficient that measures the strength and direction of the association between two variables. It’s particularly useful for small datasets or data with many tied ranks.

Types by Nature of Relationship

Positive Correlation: When two variables move in the same direction, so an increase in one variable is associated with an increase in the other, and vice versa.
Negative (Inverse) Correlation: When two variables move in opposite directions, so an increase in one variable is associated with a decrease in the other, and vice versa.

Types by Degree

Perfect Correlation: When all the data points fall exactly on a straight line (either positively or negatively sloped). It’s rare in real-world data.
Strong Correlation: When the data points closely follow a straight line. The closer the coefficient is to either -1 or +1, the stronger the correlation.
Weak Correlation: When the data points somewhat scatter around a central trend but still suggest a linear relationship. The closer the coefficient is to 0, the weaker the correlation.
No Correlation: When there’s no apparent linear relationship between the variables. The data points are widely scattered and do not follow a specific trend.

Application in Pharmaceuticals

In the pharmaceutical industry, correlation can be used in various ways, such as:

Determining the relationship between drug dosage and its efficacy.
Exploring the link between patient age or weight and drug absorption rates.
Investigating connections between lifestyle factors and medication effectiveness.

Understanding the type and strength of correlations can help in making informed decisions in drug development, patient treatment plans, and research directions.

Pharmaceutical Example

Consider a study investigating the relationship between the dosage of a new medication for treating hypertension and the reduction in systolic blood pressure in patients.
If the study finds a Pearson correlation coefficient of +0.85, this indicates a strong positive correlation, meaning higher dosages of the medication are associated with greater reductions in blood pressure.
This type of analysis can help in understanding the effectiveness of the medication and guiding dosage recommendations.

Correlation is a powerful statistical tool, but it's important to remember that it does not imply causation. A high correlation between two variables does not necessarily mean that one variable causes the change in the other; there may be other factors at play, or the relationship could be coincidental.