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The difference between correlation and regression is one of the commonly asked questions in interviews. Moreover, many people suffer ambiguity in understanding these two. So, take a full read of this article to have a clear understanding on these two. Content: Correlation Vs Regression
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Definition of CorrelationThe term correlation is a combination of two words ‘Co’ (together) and relation (connection) between two quantities. Correlation is when, at the time of study of two variables, it is observed that a unit change in one variable is retaliated by an equivalent change in another variable, i.e. direct or indirect. Or else the variables are said to be uncorrelated when the movement in one variable does not amount to any movement in another variable in a specific direction. It is a statistical technique that represents the strength of the connection between pairs of variables. Correlation can be positive or negative. When the two variables move in the same direction, i.e. an increase in one variable will result in the corresponding increase in another variable and vice versa, then the variables are considered to be positively correlated. For instance: profit and investment. On the contrary, when the two variables move in different directions, in such a way that an increase in one variable will result in a decrease in another variable and vice versa, This situation is known as negative correlation. For instance: Price and demand of a product. The measures of correlation are given as under:
Definition of RegressionA statistical technique for estimating the change in the metric dependent variable due to the change in one or more independent variables, based on the average mathematical relationship between two or more variables is known as regression. It plays a significant role in many human activities, as it is a powerful and flexible tool which used to forecast the past, present or future events on the basis of past or present events. For instance: On the basis of past records, a business’s future profit can be estimated. In a simple linear regression, there are two variables x and y, wherein y depends on x or say influenced by x. Here y is called as dependent, or criterion variable and x is independent or predictor variable. The regression line of y on x is expressed as under: y = a + bx where, a = constant, The points given below, explains the difference between correlation and regression in detail:
Video: Correlation Vs RegressionConclusionWith the above discussion, it is evident, that there is a big difference between these two mathematical concepts, although these two are studied together. Correlation is used when the researcher wants to know that whether the variables under study are correlated or not, if yes then what is the strength of their association. Pearson’s correlation coefficient is regarded as the best measure of correlation. In regression analysis, a functional relationship between two variables is established so as to make future projections on events. When one variable increases and the other decreases result to a?Negative correlation is a relationship between two variables in which one variable increases as the other decreases, and vice versa.
When increase in one variable results increase in another variable is called?When one variable increases as the other increases the correlation is positive; when one decreases as the other increases it is negative. Complete absence of correlation is represented by 0. Figure 11.1 gives some graphical representations of correlation. Figure 11.1 Correlation illustrated.
When an increase in a variable produces a decrease in another variable This is called a positive correlation?This is an example of a negative correlation: as one variable increases, the other decreases. A positive correlation is where the two variables react in the same way, increasing or decreasing together.
When increase in the value of one variable results in decrease in the value of another variable then they are said to be?There exists a negative correlation when increase in one variable leads to a decrease in another variable, i.e, they both move in the opposite direction.
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