Is this intended to be a joke (perhaps poking fun at Dr. Shiva's downward sloping lines)? This is essentially 1-dimensional data (since Biden Percentage + Trump Percentage = 100 if 3rd parties get very few votes, so all data must lie on a line with slope of -1) plotted on a 2-dimensional plot with a red line thrown in for no apparently reason. This doesn't provide any evidence at all for who won.
Its proves it 100%. The evidence is HUGE. Really, what I aimed at showing was the top 200 Bell Weather Counties and their margins, pointing to Trump winning. Its a descriptive statistic, so no assumptions about data, just a plot. Make of it as you will. The red line shows the cutoff point. Red line is y = x, duh!
I will agree with that. As it seems that you do some research into this, I was curious if you may know a good way of detecting fraud at the county level. For instance, I was going to take a look at the historical correlations from past elections across lots of the counties and see if these correlations break down in this election. For instance, it seems there are a lot of interesting correlations that break down in this election. Florida and Georgia correlation is one of them. Upstate NY going more dem and Ohio/Iowa going more rep is another.
Just thinking out loud (may not work), one approach you might try to quantify the correlations in a useful way is to take a random subset of counties and use them as the explanatory variables in a logistic regression to make a prediction for a single target county. Repeat several times for the same target county with different random sets of counties used as explanatory variables so you get a whole bunch of predictions for the target county. If the actual vote for the target county is far outside of any of the predictions for that county, the target county is suspicious.
Just a follow-up on the regression: One would have to think a bit about how best to define the explanatory variables. For example, should they be raw percentages, or should they be the change in percentage compared to the prior year?
First, we know that Bell Weathers went for Trump, but that is not what I am showing. I show more than that; I show the top 200 most accurate counties. I won't debate the usefulness of the results. I have all the county level data from 1976 to 2020 and am analyzing it right now. What questions do you think are interesting to ask?
Is this intended to be a joke (perhaps poking fun at Dr. Shiva's downward sloping lines)? This is essentially 1-dimensional data (since Biden Percentage + Trump Percentage = 100 if 3rd parties get very few votes, so all data must lie on a line with slope of -1) plotted on a 2-dimensional plot with a red line thrown in for no apparently reason. This doesn't provide any evidence at all for who won.
Its proves it 100%. The evidence is HUGE. Really, what I aimed at showing was the top 200 Bell Weather Counties and their margins, pointing to Trump winning. Its a descriptive statistic, so no assumptions about data, just a plot. Make of it as you will. The red line shows the cutoff point. Red line is y = x, duh!
Sorry, I misinterpreted the title of the graph. There is some validity to the idea, but there is no reason to make a 2-D graph out of 1-D data.
I will agree with that. As it seems that you do some research into this, I was curious if you may know a good way of detecting fraud at the county level. For instance, I was going to take a look at the historical correlations from past elections across lots of the counties and see if these correlations break down in this election. For instance, it seems there are a lot of interesting correlations that break down in this election. Florida and Georgia correlation is one of them. Upstate NY going more dem and Ohio/Iowa going more rep is another.
Just thinking out loud (may not work), one approach you might try to quantify the correlations in a useful way is to take a random subset of counties and use them as the explanatory variables in a logistic regression to make a prediction for a single target county. Repeat several times for the same target county with different random sets of counties used as explanatory variables so you get a whole bunch of predictions for the target county. If the actual vote for the target county is far outside of any of the predictions for that county, the target county is suspicious.
Just a follow-up on the regression: One would have to think a bit about how best to define the explanatory variables. For example, should they be raw percentages, or should they be the change in percentage compared to the prior year?
We already knew the bellwethers went for Trump. And it was easier to see in a table.
First, we know that Bell Weathers went for Trump, but that is not what I am showing. I show more than that; I show the top 200 most accurate counties. I won't debate the usefulness of the results. I have all the county level data from 1976 to 2020 and am analyzing it right now. What questions do you think are interesting to ask?
Those are bellwethers. Good data visualization doesn’t overwhelm with non-critical variables. Margin is non-critical to bellwether analysis.