Interpreting linear regression. I'm not very versed in statistics or anything so I'm in the dark for this. For my biology (Grade 12) class I've been looking at journals and papers and I've seen a lot of graphs expressed in the form 'linear regression'. Wikipedia and other sites are going way over my head. I want to use the data in the graphs in a computer program I'm making but I don't know how to convert it to a comprehend-able form (I'm thinking y = mx + b).
Here is an example of a graph I'm having trouble understanding (Pleasants & Oberhauser, 2012):

What are the values F_1,11; P; and r^2 -- and is it possible for me to create a function out of them? 
Thanks!
-Dillon
 A: The idea is: We have data (here it are the tuples: (monarch egg production,overwintering population)). We ask ourselves if there may exist a lineair relationship between the two. 
This question may look odd to you. Why would we care of a linear relationship? 
Well, it is important to get more insight in the thing you want to study. 
See this example:
We measure the force $F$ needed on a spring to get a displacement $x$ from the initicial length of the spring. Physics tells us there is a lineair relationship between them: $F = kx$. Notice $m=k$ and $b=0$ in this case.
If we can find 'the best' fitting line between the data, we can then find the 'best' value of $k$ to explain our data. But this value represents properties of the material from wich the spring is made and this can be verry important to know.
But there doesn't always exists a linear relationship in the data. It may be unclear wether there is such a relationship or not. We need a sort of test wich says yes or no. The values $F_{1,11}, r^2$ and $P$ are the outcomes of such tests. The most intuitive value and probably most important to you is: $r^2$. $r^2$ is the percentage of the data wich can be explained with the linear model. In this case it is $0.47$ and isn't verry much. From the graph you can see that indeed the data isn't well fitted.
You cannot know the values $m$ and $b$ from $F_{1,11}, r^2$ and $P$. These values only tells us information about the statistics of the fit.
There is one exception and it is when $P$ is close to $1$. In this case the 'best fitting' line is the horizontal line.
