# Difference Between Correlation and Regression

## Key difference - correlation vs. regression

Correlation and regression are two methods used to study the relationship between variables in statistics. The main difference between correlation and regression is that correlation measures the degree of relationship between the two variables , while regression is a method of describing the relationship between two variables . Regression also allows a more accurate prediction of the value the dependent variable would take for a given value of the independent variable.

## What is correlation?

In statistics, we say that there is a correlation between two variables when the two variables are related. If the relationship between the variables is linear, we can express the degree of their relationship in terms of a number called the Pearson correlation coefficient $\mathbf{\left( \mathit {\rho}\right)}$ . $\rho$ takes a value between -1 and 1. A value of 0 means that the two variables are not correlated. Negative values ​​indicate that the correlation between the variables is negative: that is, as one variable increases, the other variable decreases. Likewise is a positive value for $\rho$ means that the data are positively correlated (if one variable increases, the other variable increases as well).

A value of $\rho$ ie -1 or 1 gives the strongest possible correlation. When $\rho =-1$ the variables are completely negatively correlated and if $\rho =1$ the values ​​should be completely positively correlated. The following figure shows different forms of scatter plot between two variables and the correlation coefficients for each case: Pearson's Correlation Coefficient for Different Types of Scatterplots

Pearson's correlation coefficient for two variables $x$ and $y$ is defined as follows: $\rho=\frac{\mathrm{cov\left( \mathit{x,y}\right )}}{\sigma_{x}\sigma_{y}}$

Here, $\mathrm{cov}\left( \mathit{x,y}\right )$ is the covariance between $x$ and $y$ : $\mathrm{cov}\left( \mathit{x,y}\right )=\frac{1}{N}\sum_{i=1}^{N}\left( x_i-\bar{x}\right)\left( y_i-\bar{y}\right)=\left( \frac{1}{N}\sum_{i=1}^{N} x_iy_i\right)-\bar{x}\bar{y}$

The conditions $\sigma_x$ and $\sigma_y$ stand for standard deviations of $x$ and $y$ or this is defined as: $\sigma_x=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{\left( x_i-\bar{x}\right )}^2}$ and $\sigma_y=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{\left( y_i-\bar{y}\right )}^2}$

Let's look at an example of how the correlation coefficient is calculated. We will try to calculate the correlation coefficient for the following set of 20 values ​​for $x$ and $y$ : $x$ $y$ -0.9557 0.5369 -1.6441 -0.1560 1.2254 1.9230 1.9062 1.9957 1.9679 2.1673 -0.3469 0.7954 -0.2328 0.5415 1.5064 1.2335 0.4278 0.7754 -0.6359 0.3534 0.0061 0.7565 0.8407 1.5326 0.2713 1.3354 0.4664 1.9980 -0.1813 1.2539 1.4384 2.0383 1.9001 2.7755 0.1022 0.7861 0.1251 0.7456 -0.6314 0.9942

The values ​​of $y$ are plotted against the values ​​of $x$ in the graphic shown below: When we look at the equations needed to calculate the correlation coefficient, we first calculate values ​​for $\bar{x}, \bar{y}$ . These are the mean values ​​of $x$ and $y$ or we believe that: $\bar{x}=0.3778$ $\bar{y}=1.2191$

Next we calculate $x_iy_i, {\left( x_i-\bar{x}\right )}^2,$ and ${\left( y_i-\bar{y}\right )}^2$ . We will put these values ​​next to our values ​​of $x$ and $y$ in the table above: $x$ $y$ $x_iy_i$ ${\left( x_i-\bar{x}\right )}^2$ ${\left( y_i-\bar{y}\right )}^2$ -0.9557 0.5369 -0.5131 1.7782 0.4654 -1.6441 -0.1560 0.2565 4.0881 1.8909 1.2254 1.9230 2.3564 0.7184 0.4955 1.9062 1.9957 3.8042 2.3360 0.6031 1.9679 2.1673 4.2650 2.5284 0.8991 -0.3469 0.7954 -0.2759 0.5252 0.1795 -0.2328 0.5415 -0.1261 0.3728 0.4592 1.5064 1.2335 1.8581 1.2737 0.0002 0.4278 0.7754 0.3317 0.0025 0.1969 -0.6359 0.3534 -0.2247 1.0276 0.7495 0.0061 0.7565 0.0046 0.1382 0.2140 0.8407 1.5326 1.2885 0.2143 0.0983 0.2713 1.3354 0.3623 0.0113 0.0135 0.4664 1.9980 0.9319 0.0079 0.6067 -0.1813 1.2539 -0.2273 0.3126 0.0012 1.4384 2.0383 2.9319 1.1249 0.6711 1.9001 2.7755 5.2737 2.3174 2.4223 0.1022 0.7861 0.0803 0.0760 0.1875 0.1251 0.7456 0.0933 0.0639 0.2242 -0.6314 0.9942 -0.6277 1.0185 0.0506

With these values ​​we can calculate the covariance: $\frac{1}{N}\sum_{i=1}^{N} x_iy_i=1.0922$ $\bar{x}\bar{y}=0.4606$ $\therefore \mathrm{cov}\left( \mathit{x,y}\right)=1.0922-0.4606=0.6316$

We can also calculate the standard deviations: $\sum_{i=1}^{N}{\left( x_i-\bar{x}\right )}^2=19.94$ $\sigma_x=\sqrt{\frac{19.94}{20}}=0.9985$ $\sum_{i=1}^{N}{\left( y_i-\bar{y}\right )}^2=10.43$ $\sigma_y=\sqrt{\frac{10.43}{20}}=0.7221$ $\sigma_x\sigma_y=0.7211$

Now we can calculate the correlation coefficient: $\rho=\frac{\mathrm{cov\left( \mathit{x,y}\right )}}{\sigma_{x}\sigma_{y}}=\frac{0.6316}{0.7221}=0.876$

## What is regression

Regression is a method of finding the relationship between two variables. Specifically, we consider linear regression , which provides an equation for a “best-fit line” for a given sample of data in which two variables have a linear relationship. A straight line can be described with an equation in the form $y=mx+c$ Where $m$ is the slope of the line and $c$ Axis, and linear regression allows us to calculate the values ​​of $m$ and $c$ . After we have calculated the correlation coefficient $\rho$ , we can calculate these values ​​as: $m=\rho\left( \frac{\sigma_y}{\sigma_x}\right)$ $c=\bar{y}-m\bar{x}$

Note that in these cases $y$ is assumed to be the dependent variable while $x$ is the independent variable. We know this from our calculations so far $\rho=0.876$ , $\sigma_x=0.9985$ and $\sigma_y=0.7221$ . Because of this, $m=0.876\times\left( \frac{0.7221}{0.9985}\right)=0.634$ . $\bar{y}=1.2191$ and $\bar{x}=0.3778$ . Because of this, $c=1.2191-\left( 0.634\times 0.3778\right)=0.980$ .

The image below shows the previous scatter plot with the line $y=0.634x+0.980$ : The data with the best fitting straight line from the regression analysis

As mentioned earlier, regression analysis helps us make predictions. For example, if the value of the independent variable ( $x$ ) was 1,000, then we can predict that $y$ would be close $y=\left( 0.634\times 1.000\right)+0.980=1.614$ . In reality, the value of $y$ doesn't necessarily have to be exactly 1.614. Due to the uncertainty, the actual value is likely to differ. Note that the accuracy of the prediction is higher for data with a correlation coefficient closer to ± 1.

## Difference Between Correlation and Regression

### Describe relationships

Correlation describes the degree to which two variables are related.

Regression provides a method of finding the relationship between two variables.

### Make predictions

Correlation simply describes how well two variables are related. Analyzing the correlation between two variables does not improve the accuracy with which the value of the dependent variable could be predicted for a given value of the independent variable.

Regression allows us to more accurately predict values ​​of the dependent variable for a given value of the independent variable.

### Dependency between variables

When analyzing the correlation , it does not matter which variable is independent and which variable is independent.

When analyzing the regression , it is necessary to distinguish between the dependent and the independent variables.

Image courtesy:

“Redesign File: Correlation_examples.png with vector graphics (SVG file)” by DenisBoigelot (own work, original uploader was Imagecreator ) [ CC0 1.0 ], via Wikimedia Commons 