A short post by me today. I wanted to look at the which states are important in winning the national election. Looking at the last 14 presidential elections, I generated the following correlation plot:

For those not familiar with how correlation plots work, the number bar on the right-hand-side of the graph indicates the correlation between a state on the left side with a state at the top, with the last row and column respectively indicating the national presidential election winner. Dark blue circles representing a correlation close to 1, indicate a strong relationship between the two variables, while orange-to-red circles representing a correlation close to -1 indicate a strong anti-correlation between the two variables, while almost white circles indicate no correlation between the two variables.

For example, one can see there is a very strong correlation between who wins Nevada and the winner of the national election. Indeed, Nevada has picked the last 13 of 14 U.S. Presidents. Darker blue circles indicate a strong correlation, while lighter orange-red circles indicate a weak correlation. This also shows the correlation between winning states. For example, from the plot above, candidates who win Alabama have a good chance of winning Mississippi or Wyoming, but virtually no chance of winning California.

This could serve as a potential guide in determining which states are extremely important to win during the election season!

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## 12 replies on “The Effect of Individual State Election Results on The National Election”

Could you please explain more about what axis means.Thanks

Hi. Sure, The number bar on the right-hand-side of the graph indicates the correlation between a state on the left side with a state at the top, with the last row and column respectively indicating the national presidential election winner. Dark blue circles representing a correlation close to 1, indicate a strong relationship between the two variables, while orange-to-red circles representing a correlation close to -1 indicate a strong anti-correlation between the two variables, while almost white circles indicate no correlation between the two variables.

Thanks . It seems “N” & “C” states are the key to the White House.

Yes. Ohio and Florida as well!

Interesting – but can you be a bit more explicit on what the graph shows exactly. It’s a bit too ‘dense’ for me ! I am intrigued by the small linear patterns that seem to emerge. What a wonderful graph !

Hi, thanks! The number bar on the right-hand-side of the graph indicates the correlation between a state on the left side with a state at the top, with the last row and column respectively indicating the national presidential election winner. Dark blue circles representing a correlation close to 1, indicate a strong relationship between the two variables, while orange-to-red circles representing a correlation close to -1 indicate a strong anti-correlation between the two variables, while almost white circles indicate no correlation between the two variables.

Got it ! I first thought it was just correlation between winning individual state elections (e.g. strong correlation between winning Connecticut and Michigan). You should – perhaps – highlight the last column (and last row), because that’s the association people are really interested in. I sent this to a friend of mine who works for ABC. Your blog is fantastic. Just love the speed with which you turn out all that stuff. You should gamble on your predictions. That’s how (some) people get rich, right? Statistics and gambling – a nice association. 🙂

Just thinking… You mean election wins in the state – not primaries. Hmmm… Could you find some correlation between winning primaries and the US Presidency? Now THAT would be interesting!

Yes! That was my next posting, I’m still working on it though, as it is slightly more complicated! 🙂

Ha! I first misread election and electrons, and I was very confused for a second.

But this graph is definitely very interesting. Hmm, it seems southern states definitely have different voting patterns when compared to the rest of the nation.

Hi. Thanks… I misread things all the time!

Great work.