What is the difference between relation and correlation

Technically, association refers to any relationship between two variables, whereas correlation is often used to refer only to a linear relationship between two variables. The terms are used interchangeably in this guide, as is common in most statistics texts. Scatter plot A scatter plot shows the association between two variables. A scatter plot matrix shows all pairwise scatter plots for many variables. Covariance Covariance is a measure of how much two variables change together. A covariance matrix measures the covariance between many pairs of variables.

As one set of values increases the other set tends to decrease then it is called a negative correlation. If the change in values of one set doesn't affect the values of the other, then the variables are said to have "no correlation" or "zero correlation.

The first event is called the cause and the second event is called the effect. A correlation between two variables does not imply causation. On the other hand, if there is a causal relationship between two variables, they must be correlated. A study shows that there is a negative correlation between a student's anxiety before a test and the student's score on the test.

But we cannot say that the anxiety causes a lower score on the test; there could be other reasons—the student may not have studied well, for example. The correlations along the diagonal will always be 1. When interpreting correlations , you should be aware of the four possible explanations for a strong correlation:.

The strength of UV rays varies by latitude. The higher the latitude, the less exposure to the sun, which corresponds to a lower skin cancer risk. So where you live can have an impact on your skin cancer risk.

The Prism graph right shows the relationship between skin cancer mortality rate Y and latitude at the center of a state X. Based on the slope of Since regression analysis produces an equation, unlike correlation, it can be used for prediction. For example, a city at latitude 40 would be expected to have Regression also allows for the interpretation of the model coefficients:.

Improve your linear regression with Prism. Start your free trial today. In summary, correlation and regression have many similarities and some important differences. Correlation is primarily used to quickly and concisely summarize the direction and strength of the relationships between a set of 2 or more numeric variables.

The table below summarizes the key similarities and differences between correlation and regression. For a quick and simple summary of the direction and strength of pairwise relationships between two or more numeric variables. To predict, optimize, or explain a numeric response Y from X, a numeric variable thought to influence Y. Extension to curvilinear fits. Learn more about how to choose between regression and correlation on Prism Academy.

Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required.