# What kind of graph should I choose

I have gathered criminal activity and GDP from 2012 to 2015 for over 30 countries and I'm trying to confirm my hypothesis about how higher GDP decreases criminal activity, but I'm baffled over which type of graph I should choose.

I can't use a regular plot, because I'd have too many lines... I'd like one axis to represent the GDP and one for criminal activity, however I don't where I'd fit the years (if that's even necessary).

I'm using Python for this, so something from matplotlib would be preffered

• Please tell us what information you are trying to convey with your graph and who your audience might be. What are the format and quantity of your data? Could you give a small example? What do you mean by a "regular plot," by the way? – whuber Jun 3 '15 at 22:34
• It's a school project. I'm trying to confirm my hypothesis about how higher gdp decreases criminal activity. I got data for 34 countries, from 2005-2012. I'd like if one axis would represent the gdp and one criminal activity, however I don't where I'd fit the years(if that's even necessary) . I meant it as a line that connects the points – jabk Jun 3 '15 at 22:47
• Good! Please include that information in the post itself (you can edit it). Be cautious about how you use these data, though: they won't confirm any causal hypothesis at all. (If they could, I would be able to use the same technique to "confirm" hypotheses such as artificial satellites in earth orbit cause autism: both have been going up at remarkable rates, in parallel, for over half a century.) – whuber Jun 3 '15 at 22:51
• Thank you for the guidance. What do you suggest, cross validation? – jabk Jun 3 '15 at 23:07
• @TheGuyWithStreetCred no, that won't help you prove causality. To show that $X$ causes $Y$, you need to demonstrate 1) that $Y$ does not cause $X$, and 2) that the relationship between $X$ and $Y$ is not confounded by some other variable $Z$. Since you have data over time, you can get some leverage by "lagging" $X$, or finding natural changes in $X$ that you know are not caused by $Y$, such as GDP collapsing due to a recession. The latter technique is used in "regression discontinuity" models. – shadowtalker Jun 3 '15 at 23:23