Thank you. I'm also an engineer and came to the same conclusion, but felt like I must be missing something.
TLDR: The guy set his y-axis as y = -x + t, where x is the number of straight ticket voters and t is the number of independent Trump voters. He himself said t should be constant, therefore he is plotting a linear function with a negative slope.
He is finding the difference between two percentages.
Y the percentage of Straight Republican Vote (SRV)
X being the percentage of Trump Individual Vote (TIV) - Y
He argues that in a normal case, the percentage of TIV would be close to percentage of SRV (+/- 7%). So when a precinct has a SRV of 65%, the TIV would be between 60-70%.
There's no reason to assume that the percentage of TIV would be a constant (say 50%), which would be what is required for this to be a linear negative slope.
Even in the case where the SRV approaches 99%, the TIV should also be corespondent - because ultimately it's tracking the same population.
Thank you. I'm also an engineer and came to the same conclusion, but felt like I must be missing something.
TLDR: The guy set his y-axis as y = -x + t, where x is the number of straight ticket voters and t is the number of independent Trump voters. He himself said t should be constant, therefore he is plotting a linear function with a negative slope.
That's not quite right.
He is finding the difference between two percentages.
Y the percentage of Straight Republican Vote (SRV) X being the percentage of Trump Individual Vote (TIV) - Y
He argues that in a normal case, the percentage of TIV would be close to percentage of SRV (+/- 7%). So when a precinct has a SRV of 65%, the TIV would be between 60-70%.
There's no reason to assume that the percentage of TIV would be a constant (say 50%), which would be what is required for this to be a linear negative slope.
Even in the case where the SRV approaches 99%, the TIV should also be corespondent - because ultimately it's tracking the same population.