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Though the query is directed to Dr Philip Sedgwick, I may be allowed to intervene and react to the query. Co-relation shows the association between two quantitative variables and the cofficient of correlation varies from -1 to +1 and 0 means no co-relation whereas -1 and +1 show the perfect negative and positive co-relation. It shows the dose-response association. Whereas, to predict the value of Y (the dependent variable) from the independent variable 'x' the equation of line (linear regression) is y= a+ bx where the a is intercept and b is slope and will tell us the value of y to be derived when putting the values of other variables.
The presented relationship between PASP and RVESA is linear but correlation instead of regression would be more appropriate since one-way causation seems highly improbable (PASP may be equally or better presumed as the explanatory variable in this case). Therefore answer b) is in my opinion as true as the answer a) is, of course within the limits of the obtained measurements.
A response from Dr Sedgwitch would be highly appreciated.
Re: Simple linear regression
Though the query is directed to Dr Philip Sedgwick, I may be allowed to intervene and react to the query. Co-relation shows the association between two quantitative variables and the cofficient of correlation varies from -1 to +1 and 0 means no co-relation whereas -1 and +1 show the perfect negative and positive co-relation. It shows the dose-response association. Whereas, to predict the value of Y (the dependent variable) from the independent variable 'x' the equation of line (linear regression) is y= a+ bx where the a is intercept and b is slope and will tell us the value of y to be derived when putting the values of other variables.
I also require to be endorsed by Dr. Sedgewick.
Competing interests: No competing interests