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I give the Brady bunch a score of -100. Hows that?
 

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I'm missing the correlation on this.

California with the best Brady score has one of the highest gun homicide rates but then Louisiana has one of the lowest Brady scores but has a gun homicide rate twice the rate of California (I don't see the data point for LA).

On the lower end of gun homicide rates, Hawaii is one of the higher Brady scores but has one of the lowest gun homicide rates along with Wyoming with a very low Brady score.
 

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(Just) a chart does not show correlation, nor cause and effect. It takes linear regression to isolate linear correlation between random variables (IIRC college statistics and quant methods from a wee yaer or two ago, LOL)
 

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Here ya' go. Pretty sure that says about zero correlation. I put the tables in Excel, sorted by state, used Brady score as the Y-variable.
Multiple R 0.012047097
R Square 0.000145133
Adjusted R Square -0.020685177
Standard Error 18.42939996
Observations 50

________________F Significance F
Regression________0.0069 0.9338

t Stat
Intercept ______3.69
X Variable 1 ____.083471
 

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Since the Brady's give California a mere 79 in spite of all it's restrictions, it's pretty clear what they have in mind with their "common sense gun laws".
 

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Discussion Starter #11
Names, Son, give us state names...This is very interesting...
I couldn't put them on the chart, but they're available at the sources I provided.

I think the point is that there is no correlation. Meaning that stricter gun laws don't make us any safer.
That's about what I saw after I plotted this. The thing is, I saw NO correlation at all, negative or positive, which tells me that there are other factors that must come into play.

(Just) a chart does not show correlation, nor cause and effect. It takes linear regression to isolate linear correlation between random variables (IIRC college statistics and quant methods from a wee yaer or two ago, LOL)
A simple chart DOES show correlation (or lack thereof) between two variables, which is what I did here. A chart showing monthly ice cream sales on one axis and monthly average temperature on the other would show a correlation between ice cream sales and temperature. I was only interested in these two variables for this purpose. It's not intended to be any sort of scientific study - I don't have the time for something like that.

As for cause and effect, I think we all know that correlation is not causation.
 

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Ok, math teacher here...

I agree that it would certainly appear that there is no correlation here. There is certainly no strong correlation. Linear regression should be done, but the only other possible outcome there seems to be that a higher Brady score increases the incidence of violence. Just glancing at this chart makes me wonder if there isn't a very weak positive (if any) correlation, but again, analysis should be done to identify any outliers in the data, etc.

As for the whole "correlation does not equal causation" argument; that is certainly true. This data would refute any claim that more restrictive states are safer, though.
 
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