The other answers are correct, but I just wanted to add more detail in case you are interested in what is meant by these numbers.
Suppose you were to draw a horizontal line on your graph which represented the average value of y, ie the average of the mortality rates in your data. For your example, 9.02 is approximately the average mortality. If you didn't have an explanatory variable (the proportion of women in parliament), the average of the mortality values (the horizontal line) would actually be your best guess for predicting a countries mortality. After all, if you didn't have any other data than the mortality values, what else could you do?
The purpose of regression is to take one or more explanatory variables (the x variables) and find a better predictor of mortality for the country than simply the average of the mortality values. In other words, for your example, what you are saying is that if you know the countries proportion of women in parliament, you can make a much better guess of that countries mortality than simply saying "9.02". You instead give the value found on the dashed line. The value given by the dashed line is a much better estimate than simply using the horizontal line (the average value)!
Thus, R^2 represents a percentage which can be thought of in this way:
We can see there is a variation in the values of mortality for all the countries. How much of that variation in mortality values can we explain by using the dashed line instead of the horizontal line? In this case, we can explain about 72% of the variation in mortality values by also knowing the proportion of women in parliament. This is good, but not quite as good as an R^2 value of 1. If it was 1 then we would know exactly what the mortality value should be for each country by knowing the proportion of women in parliament, because the amount of variation explained would be 100%! Which is to say, if all the points fell on the dashed line there would be no variation from the points to the line.
Further analysis and Conclusion
Suppose that in addition to having the proportion of women in parliament, you also had a variable which represented the proportion of people in the country who are in poverty. One would suspect that using both of those variables together would give you an even better prediction of mortality rate for a country than either alone! And that's what regression hopes to accomplish. You consider all things which have a positive or negative correlation to the outcome variable of interest and come up with an equation that relates these explanatory variables to the outcome. As you add in more and more explanatory variables, you will find that you explain more and more of the variation in your outcome, which is to say your R^2 will be closer and closer to 1.