What is the difference between correlation and regression?

Question

The person who gives the maximum difference points (meaningful) will be chosen as the best answer..

Answer 1

(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.

(2a) Correlation is calculated whenever:
* both X and Y is measured in each subject and quantify how much they are linearly associated.
* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied
* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.
* correlation is not used when the variables are manipulated, for example, in experiments.

(2b) Linear regression is used whenever:
* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.
* if one manipulates the X variable, e.g. in an experiment.


(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.
On the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.

(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or <= -0.80

(5) The same underlying distribution is assumed for all variables in linear regression. Thus, linear regression will underestimate the correlation of the independent and dependent when they (X's and Y) come from different underlying distributions.

Answer 2

Difference Between Correlation And Regression

Answer 3

Correlation

Correlation regularly alludes to the degree to which two variables contain a relationship among one another. A positive correlation demonstrates the degree to which the variables decrease or increase in parallel whereas a negative correlation shows the degree where one variable increases when other decreases.

Regression

Regression is measure in statistics that endeavors to decide the strength of relationship exist between one dependent variable and a progression of other explanatory variables.

http://researchpedia.info/difference-between-correlation-and-regression/

Answer 4

A correlation, most simply put, is the relationship between 2 variables. A positive correlation means that as one value goes up, the other value goes up. On a scatter plot, you will notice as you read the dots from left to right, the height will rise as well. A negative correlation means that the variable act with an opposite effect. As one value decreases, the other value invreases.

When applying a regression analysis, tries to apply a line to the scatter plot to make comment on the scatter plot patterns observed. A regression line is not defined by points at each x,y pair. It is calculated so that it is the single best line representing all the data values that are scattered on the graph. Regression lines are derived so that the distance between every value and the regression line when squared and summed across all the values is the smallest possible value. Thus, the values on the Y-axis for the regression line are not directly derived from the values, but from expected values.

Answer 5

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RE:
What is the difference between correlation and regression?
The person who gives the maximum difference points (meaningful) will be chosen as the best answer..

Answer 6

Correlation simply measures the degree of fit of the data. Regression gives you also an estimated mathematical relationship between the dependent and independent variables.

Answer 7

Correlation is, as observed by several, is a measure of the mutual relationship between two variables.

Regression is to find a function that predicts one variable, given the other. A root - mean - squared regression will produce a function that predicts the variable with minimum RMS error.

Answer 8

correlations defines the strength of a relationship while regression determines the extend toward which variables determines that predict the outcome of others

Answer 9

coorelation is a mutual relationship between two or more things and regression is the act of going back to a previous place or state; return or reversion

Answer 10

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