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In particular, I am referring to the Pearson product-moment correlation coefficient.

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I have no idea what this question is about. –  Robby McKilliam Aug 26 '10 at 1:35
It sounds like it is about the difference between correlation and simple linear regression. –  Brett Magill Aug 26 '10 at 2:19
Note that one perspective on the relationship between regression & correlation can be discerned from my answer here: What is the difference between doing linear regression on y with x versus x with y?. –  gung Sep 20 '12 at 19:39

8 Answers 8

up vote 41 down vote accepted

What's the difference between the correlation between $X$ and $Y$ and a linear regression predicting $Y$ from $X$?

First, some similarities:

  • the standardised regression coefficient is the same as Pearson's correlation coefficient
  • The square of Pearson's correlation coefficient is the same as the $R^2$ in simple linear regression
  • Neither simple linear regression nor correlation answer questions of causality directly. This point is important, because I've met people that think that simple regression can magically allow an inference that $X$ causes $Y$.

Second, some differences:

  • The regression equation (i.e., $a + bX$) can be used to make predictions on $Y$ based on values of $X$
  • While correlation typically refers to the linear relationship, it can refer to other forms of dependence, such as polynomial or truly nonlinear relationships
  • While correlation typically refers to Pearson's correlation coefficient, there are other types of correlation, such as Spearman's.
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Hi Jeromy, thank you for your explaination, but I still have a question here: What if I don not need to make predictions and just want to know how close two variable are and in which direction/strength? Is there still a different using these two technique? –  yue86231 Jun 15 at 22:15
@yue86231 Then it sounds like a measure of correlation would be more appropriate. –  Jeromy Anglim Jun 16 at 2:40

Here is an answer I posted on the graphpad.com website:

Correlation and linear regression are not the same. Consider these differences:

  • Correlation quantifies the degree to which two variables are related. Correlation does not fit a line through the data.
  • With correlation you don't have to think about cause and effect. You simply quantify how well two variables relate to each other. With regression, you do have to think about cause and effect as the regression line is determined as the best way to predict Y from X.
  • With correlation, it doesn't matter which of the two variables you call "X" and which you call "Y". You'll get the same correlation coefficient if you swap the two. With linear regression, the decision of which variable you call "X" and which you call "Y" matters a lot, as you'll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the same as the line that predicts X from Y (unless you have perfect data with no scatter.)
  • Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate. With linear regression, the X variable is usually something you experimentally manipulate (time, concentration...) and the Y variable is something you measure.
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"the best way to predict Y from X" has nothing to do with cause and effect: X could be the cause of Y or vice versa. One can reason from causes to effects (deduction) or from effects to causes (abduction). –  Neil G Nov 20 '13 at 7:07

In the single predictor case of linear regression, the standardized slope has the same value as the correlation coefficient. The advantage of the linear regression is that the relationship can be described in such a way that you can predict (based on the relationship between the two variables) the score on the predicted variable given any particular value of the predictor variable. In particular one piece of information a linear regression gives you that a correlation does not is the intercept, the value on the predicted variable when the predictor is 0.

In short - they produce identical results computationally, but there are more elements which are capable of interpretation in the simple linear regression. If you are interested in simply characterizing the magnitude of the relationship between two variables, use correlation - if you are interested in predicting or explaining your results in terms of particular values you probably want regression.

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Correlation analysis only quantifies the relation between two variables ignoring which is dependent variable and which is independent. But before appliyng regression you have to calrify that impact of which variable you want to check on the other variable.

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From correlation we can only get an index describing the linear relationship between two variables; in regression we can predict the relationship between more than two variables and can use it to identify which variables x can predict the outcome variable y.

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Quoting Altman DG, "Practical statistics for medical research" Chapman & Hall, 1991, page 321: "Correlation reduces a set of data to a single number that bears no direct relation to the actual data. Regression is a much more useful method, with results which are clearly related to the measurement obtained. The strength of the relation is explicit, and uncertainty can be seen clearly from confidence intervals or prediction intervals"

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Although I am sympathetic with Altman--regression methods often are more suitable than correlation in many cases--this quotation is setting up a straw man argument. In OLS regression the information produced is equivalent to that afforded by the information that goes into a correlation calculation (all first and second bivariate moments and their standard errors) and the correlation coefficient provides the same information as the regression slope. The two approaches differ somewhat in the underlying data models they assume and in their interpretation, but not in the ways claimed by Altman. –  whuber Aug 20 at 13:20

Correlation is an index (just one number) of the strength of a relationship. Regression is an analysis (estimation of parameters of a model and statistical test of their significance) of the adequacy of a particular functional relationship. The size of the correlation is related to how accurate the predictions of the regression will be.

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Correlation is a term in a statistics which determine whether that there is a relation between two and then the degree of relationship. It's range is from -1 to +1. While regression means going back towards average . From regression we predict the value by keeping one variable dependent and other independent but it should be clarify the value of which variable we want to predict.

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Hello, @shakir, and welcome to Cross Validated! You probably noticed that this is an old question (from 2010) and there are seven (!) answers given to it already. It would be a good idea to make sure that your new answer adds something important to the discussion that has not been covered before. At the moment I am not sure it is the case. –  amoeba Aug 14 at 10:31

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