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Correlation is used to measure how much one set of numbers relates to another set of numbers. It is measured by the Correlation Coefficient.
When To Use Correlation
Correlation is used to measure how much the changes in one variable relate to the changes in another variable. It is measured by the Correlation Coefficient.
Correlation analysis is a really useful tool to see how strongly two variables are related.
What Is the Correlation Coefficient?
The Correlation Coefficient is a number between -1 and 1. A value close to 1 means that when the values in your first set of numbers increase or decrease, then the numbers in your second set also increase or decrease. A value close to -1 means that when the numbers in your first set increase or decrease, then the numbers in your second set change in the opposite direction. E.g. if the numbers in your first set increase, then the numbers in your second set decrease.
Datasets with a Correlation Coefficient that is close to -1 or 1 are said to be highly correlated. This means that there is a strong relationship between the numbers in the first and second sets.
Datasets with a Correlation Coefficient close to 0 are said to be weakly correlated. This means that there is a weak, or no, relationship between the numbers in the first or second sets.
Correlation Does Not Imply Causation
A strong correlation between two sets of numbers does not mean that one causes the other. Correlation does not mean causation.
For example, ice cream stands at the beach tend to sell more ice cream on days when there are more shark attacks. But selling ice-creams does not cause shark attacks, and removing the stands will not stop shark attacks. There is a confounding variable at play, the temperature of the day.
More people go swimming at the beach on sunny days. More people swimming means that there is a higher chance of shark attacks, and it also means that more ice creams will be sold because there are more people at the beach.
What Does the Correlation Coefficient Mean
Correlation coefficients above 0 are called positive correlation. This means that when one variable changes then the other changes in the same direction. For example, when one variable increases then so does the other.
A correlation coefficient of 1 is called a perfect positive correlation. A correlation coefficient close to 1 is called a strong positive relationship.
Correlation coefficients below 0 are called negative correlation. This means that when one variable changes then the other one changes in the opposite direction. For example, when one variable decreases then the other increases.
A correlation coefficient of -1 is called a perfect positive correlation. A correlation near -1 is called a strong negative relationship.
Correlations near 0 mean that there is no or a weak, correlation between the variables.
What Are the Types of Correlation
There are a few different kinds of correlation and related calculators.
Pearson Correlation Coefficient
Pearson's correlation coefficient measures the correlation between two continuous variables.
Continuous variables are the raw values of something and often have decimals. One example is amount of rainfall on a day.
The difference between two variables has meaning. It is not just the ranking or ordering that is important.
Pearson's correlation coefficient calculator can be used to calculate Pearson correlation.
Covariance measures how two variables vary together. It is similar to correlation, but the result does not get normalized to be between -1 and 1.
The lack of normalization makes covariance harder to interpret than correlation.
The covariance calculator can be used to calculate the covariance between variables in a data set.