Difference Between Correlation and Regression (Correlation Vs. Regression)
Today in this articles we are discussing the Difference Between Correlation and Regression. Correlation Vs. Regression
Difference Between Correlation and Regression (Correlation Vs. Regression)
Correlation vs. Regression
The comparison between correlation and regression can be studied through a tabular format as given below:
| Basis of Difference | Correlation | Regression |
|---|---|---|
| Meaning | Correlation refers to a statistical measure that determines the association or co-relationship between two variables. | Regression depicts how an independent variable serves to be numerically related to any dependent variable. |
| Utility | Used for representing the linear relationship existing between two variables. | It is used for fitting the best line and estimating the value of one variable based on its relationship with the other. |
| Dependent /Independent variables | There is no difference between the two. Both variables are mutually dependent. | Both variables serve to be different in terms of regression analysis. One variable is independent, while the other is dependent. |
| Indicator of | It indicates the extent and way in which two variables make their movements together. | Regression depicts the impact of any unit change in the value of the known variable (x) on the value of the estimated variable (y). |
| Objective | To find the numerical value that defines and shows the relationship between variables. | To estimate the values of random variables based on the values shown by fixed variables. |
| Purpose | The primary purpose is to predict the most dependable forecasts. | The primary purpose is to predict/ estimate the value of any unknown variable by taking the help of the known variable. |
| Scope | Correlation analysis offers limited applications. | Regression analysis provides a broader scope of applications. |
| Range | Coefficients may range from -1.00 to +1.00. | If byx > 1, then bxy < 1 in regression analysis. |
| Responding Nature | The correlation coefficient serves to be independent of any change of Scale or shift in Origin. | The regression coefficient shows dependency on the change of Scale but is independent of its shift in Origin. |
| Nature of Coefficient | The correlation coefficient is mutual and symmetrical. | Regression coefficient fails to be symmetrical. |
| Exceptional Cases | Non-sense correlation may find a place in some correlation analyses. | Non-sense regression is non-existent in regression analysis. |
| Mathematical treatment. | Not very useful for advanced mathematical treatment. | It is widely used for advanced mathematical treatment. |
| Measures | This type of analysis measures the degree/extent to which any two variables make their movements in unison. | It depicts the fundamental level as well as the nature of existing linear relationships between two variables. Regression describes one variable in the form of a linear function of the other variable. |
| Relationship | It is confined to the linear relationships existing between variables only. Correlation does not depict the cause of the effect of the variables. | It encompasses both linear as well as non-linear relationships. The cause and effect relationship between the two is indicated, and a functional link is established. |
| Variables | Both variables x and y are random variables. | In regression, x is a random variable while y is a fixed variable. At times, both variables may be like random variables. |
| Coefficient | The coefficient correlation serves to be a relative measure. | The regression coefficient is generally an absolute figure. |
Key Differences Between Correlation and Regression
The points given below, explains the difference between correlation and regression in detail:
- A statistical measure which determines the co-relationship or association of two quantities is known as Correlation. Regression describes how an independent variable is numerically related to the dependent variable.
- Correlation is used to represent the linear relationship between two variables. On the contrary, regression is used to fit the best line and estimate one variable on the basis of another variable.
- In correlation, there is no difference between dependent and independent variables i.e. correlation between x and y is similar to y and x. Conversely, the regression of y on x is different from x on y.
- Correlation indicates the strength of association between variables. As opposed to, regression reflects the impact of the unit change in the independent variable on the dependent variable.
- Correlation aims at finding a numerical value that expresses the relationship between variables. Unlike regression whose goal is to predict values of the random variable on the basis of the values of fixed variable.
