# What is Correlation and Regression with an Example?

Correlation and Regression are two analytical techniques in statistics that outline multivariate distribution. A multivariate distribution is a distribution of several different variables. Correlation is basically an analysis that shows us the linkage or non-linkage of a relationship between two different variables. This article will highlight the differences between regression vs correlation.

The regression analysis shows the value of the dependent variable entailing the known value of an independent variable, assuming that there is a normal relationship between two different variables.

The question of differences between correlation and regressions is quite commonly asked. A lot of people have difficulty in differentiating between the two measures. However, you don’t have to be confused anymore because this article will highlight the important differences between the two measures perfectly.

Correlation is a measure of statistics that finds a co-relationship between two different variables. It then represents this linear relationship between both of the variables. The value of a correlation coefficient can be different, in the range of minus one to plus one. A negative one shows that there is a perfectly negative correlation while a positive one shows that there is a perfectly positive correlation. In a correlation that is negative, the variables work against each other. On the other hand, in a correlation where variables are positive, the variables work towards each other. When two variables have a positive correlation, if the value of one variable increases, the value of the other variable also increases.

The standard error of correlation is a tool that helps you determine the different confidence intervals towards a correlation of zero which is true. However, if your correlation coefficients are out of the defined range, the interval becomes quite different from zero. A standard error of internal or ratio data can also be calculated.

A regression analysis shows how an independent variable has a numeric connection with a dependent variable. It allows you to fit the best line depending on the other variable.

The dependent and independent variables in correlation have no difference but in regression, both variables are quite different.

You can use regression analysis on an intuitive level regularly. In a business environment, a man is known to be well-off if he is well dressed. A quantities regression enables precision in a calculation with the help of a mathematical formula that is used for predictive purposes. For instance, the regression line which is also known as a least-squares line is a plot of expected blue of variables for independent variables. The line is what minimizes the residuals, making the calculation more accurate. The regression line allows for the data to be the best fit for the scatterplot. It is most commonly used in the business context. Another point to notice is that if you use the regression analysis, you can predict the dependent variable through the help of the independent variable.

**Conclusion**

In conclusion, regression and correlation hold a few differences. However, both are a form of mathematical formulas and are quite helpful in the field of statistics.