This article throws light on two analyses finding a base in multivariate distribution - correlation and regression. A distribution comprising of multiple variables is called a multivariate distribution. Therefore, it is essential to understand their significance and gain a clear understanding of the terms correlation and regression before moving ahead with the differences between them. Show
Correlation vs. RegressionThe comparison between correlation and regression can be studied through a tabular format as given below:
Definition of CorrelationCorrelation is described as the analysis that informs users about the association or the absence of any relationship between any two variables ‘x’ and ‘y.’ The word correlation combines ‘Co’ (together) and relation (interaction/connection) in context to any two quantities. Correlation between two given variables exists when a unit change in any one variable gains a retaliation (in response) in the form of an equivalent change in the other variable. The answer can be either direct or indirect. Conversely, the two variables are said to be uncorrelated in case the movement in any one variable fails to generate any flow in the other variable, be it directly or indirectly. Correlation is, therefore, a statistical technique representing the strength of the connection between any given pairs of variables. Given below are the measures of correlation:
Types of CorrelationThe three types of relationship to their nature are: 1. Positive Correlation: When two variables are seen moving in the same direction, wherein an increase in the value of one variable results in an increase in other, and vice versa, then they are said to be positively correlated, e.g., profit and investment. 2. Negative Correlation: On the other hand, when two variables are seen moving in different directions, and in a manner that any increase in one variable results in a decrease in value of the other, and vice versa, then the variables are said to be negatively correlated; e.g., price and demand of any product. 3. Zero Correlation: If any given change in a variable is not dependent on the other, then the variables are said to have Zero Correlation, e.g., marks and height of students in a class. Correlation can be either positive or negative. Definition of RegressionRegression analysis is useful for predicting the value of a dependent variable based on the known value of any independent variable. It is assumed that an average mathematical relationship exists between the two variables. Regression refers to the statistical technique for assessing the changes occurring in the metric dependent -variable caused due to the transition occurring in one/more independent variables. The incurring analysis is based on the average mathematical relationship existing between the two/more variables. Regression is known to play an essential role in terms of several human activities. Overall, it serves to be a powerful and flexible instrument in the hands of analysts. Regression is used for forecasting any event based on past or present events; e.g., a business’s annual profit may be ascertained based on records with the help of regression. There exist two variables x and y in any simple linear regression. Herein, y depends on x, or in other words. It is influenced by x. While x is referred to as the predictor or independent variable, y is termed as the criterion or dependent variable. Types of RegressionBased on their functionality, the different types of regression are as follows: 1.Simple linear Regression: It is a statistical method used for summarizing and studying the relationships between any two continuous variables – an independent variable and a dependent variable. 2. Multiple Linear Regression: This type of regression examines the linear relationship existing between a dependent variable and more than one independent variable. Conclusion: The difference between correlation and regression, the two crucial mathematical concepts, cannot be studied independently of each other. Correlation analysis is best used when a researcher has to assess whether the variables under study are directly/ indirectly correlated or not. In case they are correlated, then this type of analysis showcases the strength of their association. The most popular measure of correlation is Pearson’s correlation coefficient. In regression analysis, it is possible to establish a functional relationship between any pair of given variables with the intent of making future projections concerning events. What is the difference between a regression coefficient and a correlation coefficient?Both variables are different. Correlation coefficient indicates the extent to which two variables move together. Regression indicates the impact of a change of unit on the estimated variable ( y) in the known variable (x). To find a numerical value expressing the relationship between variables.
How is regression different than correlation?Regression is primarily used to build models/equations to predict a key response, Y, from a set of predictor (X) variables. Correlation is primarily used to quickly and concisely summarize the direction and strength of the relationships between a set of 2 or more numeric variables.
What is the difference between regression coefficient and correlation coefficient Mcq?Correlation is a statistical tool that shows the association between two variables. Regression, on the other hand, evaluates the relationship between an independent and a dependent variable.
Is the correlation coefficient equal to the regression coefficient?So the correlation coefficient and regression slope MUST have the same sign (+ or -), but will not have the same value.
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