On 15 February 2013 21:26, Janesh Devkota 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
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