Are you confused about the difference between covariance and correlation? Despite being closely related statistical concepts, these terms are often used interchangeably, leading to confusion for beginners
Covariance and correlation are statistical concepts related to measuring the relationship between two variables in data analysis and statistics they are typically studied in the fields of statistics, data analysis, and mathematics.
Covariance vs.Correlation
| Covariance | Correlation |
|---|---|
| Covariance measures the degree to which two variables are linearly related. | Correlation measures the strength and direction of the linear relationship between two variables. |
| The range of values for covariance is not standardized, and it can take any value from negative infinity to positive infinity. | The range of values for correlation is standardized, and it can take values between -1 and +1. |
| The sign of the covariance indicates the direction of the relationship, while the magnitude of the covariance indicates the strength of the relationship. | The sign of the correlation indicates the direction of the relationship, while the magnitude of the correlation indicates the strength of the relationship. |
| The unit of measurement for covariance is the product of the units of the two variables. | Correlation is a unitless measure. |
| Covariance is sensitive to outliers, as the value can be heavily influenced by a few extreme values. | Correlation is less sensitive to outliers than covariance. |
| It is not affected by the scale of the variables, but it assumes that the variables are independent. | It is not affected by the scale of the variables and does not assume independence between the variables. |
| Covariance is useful in identifying the direction of the relationship between two variables, but it does not provide information about the strength of the relationship. | Correlation is useful in identifying both the strength and direction of the relationship between two variables. |
What is covariance?
Covariance is a measure of how two variables change together. A positive covariance means that the variables increase and decrease together, while a negative covariance means that one variable increases as the other decreases. Covariance can be calculated using the following formula:
Cov(X,Y) = Σ((X-μX)(Y-μY)) / N
where μX is the mean of X and μY is the mean of Y.
What is correlation?
Correlation is a statistical measure that describes the relationship between two variables correlation measures the degree to which two variables are linearly related.
Correlation is a measure of how two variables are related to each other. Correlation can be positive, negative, or zero. A positive correlation means that the variables move in the same direction, while a negative correlation means that the variables move in opposite directions. Correlation can be calculated using the following formula:
Corr(X,Y) = Cov(X,Y) / σXσY
where σX is the standard deviation of X and σY is the standard deviation of Y.
Similarities between covariance and correlation
- Both measures indicate how two variables vary together. A positive covariance means that the variables increase or decrease together, while a negative covariance means that one variable increases as the other decreases.
- Correlation is a standardized measure of association, which means that it is unitless and therefore easier to compare across different types of data.
- Both covariance and correlation can range from -1 to 1, with 0 indicating no association.
How to calculate covariance and correlation
To calculate covariance:
1) Calculate the mean of each variable.
2) For each value in one variable, subtract the mean from that value (this creates what’s called a “deviation”). Do this for every value in both variables.
3) Multiply deviations from one variable with deviations from another variable. Do this for every pair of values in both variables.
4) Add up all these products. This is your covariance!
To calculate the correlation
coefficient between two variables, you can use the following formula:
r = (nΣxy – ΣxΣy) / sqrt[(nΣx^2 – (Σx)^2)(nΣy^2 – (Σy)^2)]
where:
- r is the correlation coefficient
- n is the number of observations
- Σxy is the sum of the products of the paired observations (x and y)
- Σx and Σy are the sums of the x and y observations, respectively
- Σx^2 and Σy^2 are the sums of the squared x and y observations, respectively
The correlation coefficient (r) ranges from -1 to +1, where -1 indicates a perfectly negative correlation, +1 indicates a perfectly positive correlation, and 0 indicates no correlation.
Examples of covariance and correlation
Positive Covariance: Height and weight are positively correlated because taller people tend to weigh more than shorter people.
Negative Covariance: The prices of oil and gas are negatively correlated because when the price of oil goes up, the price of gas tends to go down (and vice versa).
Zero Covariance: The number of hours spent studying and the number of hours spent watching television are not related because they are not moving in the same direction (one goes up while the other goes down).
Positive correlation: A study found that there is a positive correlation between exercise and happiness, meaning that as the frequency of exercise increases, the level of happiness also tends to increase.
Negative correlation: There is a negative correlation between smoking and lung health, meaning that as the number of cigarettes smoked per day increases, lung health tends to decrease.
No correlation: A study on the height and weight of a group of people found no correlation between the two variables, meaning that there was no significant relationship between height and weight in that population.
Key differences between covariance and correlation
- Range of values: The range of values for covariance is not standardized and can take any value from negative infinity to positive infinity. In contrast, the range of values for correlation is standardized between -1 and +1.
- Interpretation of values: Covariance measures the strength of the linear relationship between two variables, but it does not indicate the direction of the relationship. In contrast, correlation measures both the strength and direction of the linear relationship between two variables.
- Units of measurement: Covariance is measured in the units of the product of the two variables, which can make it difficult to compare the strength of the relationship between variables with different units of measurement. In contrast, correlation is a unitless measure, which makes it easier to compare the strength of the relationship between variables with different units of measurement.
- Difference between Rhombus and Parallelogram
- Difference between Equation and Expression
- Difference between Rational and Irrational Numbers
Conclusion
Both are important concepts in statistics, and it is important to have a good understanding of both before you begin any statistical analysis. Covariance measures how two variables change together, while correlation measures the strength of that relationship. As with any skill or technique, practice makes perfect when it comes to understanding these concepts, so take your time and keep learning!
