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differences between regression and correlation analysis




What are the differences between regression and correlation analysis?;I need a comment (verify solution) in 25-75 words from the response given below from the question listed above.;(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


Paper#26923 | Written in 18-Jul-2015

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