How Many Types of Correlation

Introduction

A statistical calculation known as correlation shows that two variables are parallelly related (which means that the variables change together at a constant rate). It is an easy-to-use method for defining relationships without providing a cause-and-effect explanation.

In plain English, correlation is a statistical calculation that determines when two variables begin to shift about one another.

The coefficient correlation is exactly one of the correlations that are both positive and perfect. It means that when the direction of one variable changes—upward or downward—another variable also changes direction.

However, a negative and perfect correlation shows that the directions of the two variables are orthogonal. When there is no correlation, it indicates that there is no relationship at all.

There are three types of correlation:

1. Positive and Negative Correlation

2. Linear and Non-Linear Correlation

3. Simple, Multiple, and Partial Correlation

1. Positive and Negative Correlation

When two variables behave in unison, rising or falling in lockstep with one another, there is a positive correlation.

When two variables move in opposition to one another—that is, when one rises, the other falls—this is known as a negative correlation.

By spotting useful patterns, such as correlations in consumer behaviour or industry-specific practices, correlations can assist marketers, supply chain managers, and other professionals.

The types of correlations, or relationships, between two variables or information sets, are categorised as positive and negative. You can ascertain whether your data are positively or negatively correlated using a correlation coefficient. The correlation coefficient may occasionally be denoted by the letter "p." The correlation coefficient is most accurate when, for example, the relationship between your two sets of figures is linear rather than curved.

When a set of numbers or variables are plotted as dots along a set of axes, the terms "positive correlation" and "negative correlation" are used to describe how linearly they relate to one another.

• Positive correlations

A positive correlation exists when one set of data grows as the other does. A positive correlation would typically appear as a line going from the bottom-left of your chart to the top-right if your data were plotted on a graph.

The correlation coefficient is greater than zero for positive correlations. The numbers probably have a strong linear relationship if they rise at the same rate. A linear relationship with a perfect positive correlation would have a correlation coefficient of 1. The figures are more directly correlated when the coefficient is close to +1.

• Negative Correlations

A negative correlation exists when one set of data decreases while the other increases. Negative correlations frequently resemble a line that runs from the top left to the bottom right of the chart. Similar to positive correlations, negative correlations operate with correlation coefficients that are less than zero. A correlation coefficient of -1 would indicate a perfectly negative correlation.

2. Linear and Non-Linear Correlation

• Linear Correlation

If the ratio of change is constant, the correlation is said to be linear. An illustration of a linear correlation is when the output of a factory doubles when the workforce doubles.

In other words, the correlation is said to be linear when all of the points on the scatter diagram tend to be close to a line that appears to be straight.

• Non-Linear (Curvilinear) Correlation

If the ratio of change is not constant, the correlation is said to be nonlinear. In other words, the correlation is said to be nonlinear when all the points on the scatter diagram tend to lie close to a smooth curve (curvilinear).

3. Simple, Multiple, and Partial Correlation

• Simple Correlation

The strength and direction of the relationship between two variables, X and Y, are assessed using the simple correlation measure. A basic correlation coefficient can have a value between -1 and 1. However, some simple correlations' maximum (or minimum) values cannot be equal to unity (i.e., 1 or –1).

• Partial Correlation

A process known as partial correlation involves measuring the magnitude and direction of a linear relationship between two continuous variables while accounting for the impact of one or more additional continuous variables, also known as covariates or control variables. Independent and dependent variables have no distinction in partial correlation.

• Multiple Correlation

The statistical process of multiple correlations is known as the coefficient of multiple correlations, which measures how well a given variable can be predicted using a linear function of the set of other variables. The best predictions that can be computed linearly from the predictive variables are correlated with the values of the various variables. The range for the coefficient of multiple correlations is 0 to 1.