On 15 February 2013 21:26, Janesh Devkota <janesh.devk...@gmail.com> wrote:
> Hi I am trying to find the relationship between two variables. > > First I fitted a linear model between two variables and I found the > following results: > Residual standard error: 0.03253 on 2498 degrees of freedom > Multiple R-squared: 0.5551, Adjusted R-squared: 0.5549 > F-statistic: 3116 on 1 and 2498 DF, p-value: < 2.2e-16 > > Then I used the cor function to see the correlation between two variable > I get the following result > -0.7450344 > > r is a correlation (it actually stands for regression). R (upper case) is a multiple correlation. But you only have one predictor, so it's a correlation. R squared is R (or r), squared. So -0.7450433^2 = 0.555. > How can we interpret the result based on R-squared and correlation ? From > the p-value we can see that there is very strong relationship between > variables as it is way less that 0.001 > > The p-value doesn't tell you about the strength of the relationship. > Can anyone kindly explain the difference between Multiple R squared, > adjusted R-squared and correlation and how to report these values while > writing a report ? > > I can suggest a number of books that do this much better than I could in an email. But you probably have a favorite of your own. Jeremy [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.