How to work out Multiple R-Squared from the summary of a linear model in R I am aware that it is printed at the bottom of the summary function output, but only using the other things displayed from the summary function, how would you work out the multiple R-Squared value? I know that the formula for it is: 
RegressionSS/[RegressionSS+ResidualSS] 
But I don't know how to find these on the summary function either.

 A: This looks like a homework problem, so I'll be a bit coy with my answer.
If you're allowed to use adjusted $R^2$, you can do it.
$$R^2_{adj} = 1 - (1-R^2)\dfrac{n-1}{n-p}$$
Here, $p$ is the number of parameters estimated in the $\beta$ vector including the intercept.
The printout gives you $R^2_{adj}$. The printout also gives you the number of parameters you've estimated ($p$) and the number of observations ($n$) used to fit the equation. Then it's just algebra.
Do you see how you get $p$ and $n$ from the printout? (That's what makes this a statistics question for Cross Validated instead of a programming question that should be on Stack Overflow.)
EDIT
After discussing in the comments and chat enough that I can see that you get how to do the problem, I will give a full solution.
The first $df$ in the F-stat is $p-1$, where $p$ is the number of parameters including the intercept. There are five parameters, do $df_1=4$. Then it is given that $df_2=37$.
$df_2 = n-p$, so $n=df_2+p = 37+5=42$.
Now we go to the $R^2_{adj}$ equation and plug in these values.
$$
R^2_{adj} = 1 - (1-R^2)\dfrac{n-1}{n-p}
$$
$$
0.891 = 1 - (1-R^2)\dfrac{41}{37}
$$
The algebra shows that $R^2 = 0.9016341463414634$.
A comment by whuber mentioned that you can relate the F-stat to $R^2$, too. From this question, we see that the relationship is $F = \dfrac{ R^2 }{ 1- R^2} \times \dfrac{ df_2 }{ df_1 }$. Let's plug in the numbers.
$$
84.83 = \dfrac{ R^2 }{ 1- R^2} \times \dfrac{ 37 }{ 4} 
$$
The algebra shows that $R^2 = 0.9016794217687075$. This is almost the same as the value from the other method, and if you consider only the three significant figures in the given $R^2_{adj}$ value, they are the same: $0.902$. The F-stat and $R^2_{adj}$ value are rounded in the printout, so I think it is fair to round to three decimal places and say that $R^2 = 0.902$.
A: If your model object is called MODEL then you can retreive the coefficient-of-determination as an element of the output of the summary function, using the call   summary(MODEL)$r.squared.
