You will need to explain what your issue is. What is your level of academic expertise (you are challenging an MIT professor on math / data analysis).
I am a retired scientist who worked at a major university and I watched the whole video. It is well done. I did not see any significant problem. It shows signatures of an algorithm for vote transferring at play here. It's similar to the Benfords law analysis, though different. This allows us to identify the states, districts, and precincts where investigations need to occur first. It also identifies the candidates that benefited from the likely fraud.
Follow his math on the slide at ~20 minute mark and plot the Y values yourself. You will conclude that there is a good reason his plot goes downward.
EDIT: Shiva is saying that as you get deeper into Republican precincts the percentage of mixed voter ballots should go up for Trump. But mixed ballots are a mix of Republicans, Democrats and Independents. Why should the percentage of them go from ~0-100% voting for Trump just because they live in a more Republican district? They should not. In fact the data shows that at about 20% Republican district mixed voters level off their support for trump at ~35% and it becomes essentially constant. If you subtract the x-value from that constant - as Shiva is doing - then Y will go negative linearly. Shiva doesn't know what the fuck he's talking about.
if so then you would expect the same results for Biden in Dem strongholds? You think Dr. Shiva, the MIT "quak" hasn't done any controls on the Biden side? He did show one county in Michigan where the downward slope did not occur. That is one control.
I guess you can also give us an easy answer to explain the Biden Benford Law violations?
You will need to explain what your issue is. What is your level of academic expertise (you are challenging an MIT professor on math / data analysis).
I am a retired scientist who worked at a major university and I watched the whole video. It is well done. I did not see any significant problem. It shows signatures of an algorithm for vote transferring at play here. It's similar to the Benfords law analysis, though different. This allows us to identify the states, districts, and precincts where investigations need to occur first. It also identifies the candidates that benefited from the likely fraud.
I'm a mechanical engineer.
Follow his math on the slide at ~20 minute mark and plot the Y values yourself. You will conclude that there is a good reason his plot goes downward.
EDIT: Shiva is saying that as you get deeper into Republican precincts the percentage of mixed voter ballots should go up for Trump. But mixed ballots are a mix of Republicans, Democrats and Independents. Why should the percentage of them go from ~0-100% voting for Trump just because they live in a more Republican district? They should not. In fact the data shows that at about 20% Republican district mixed voters level off their support for trump at ~35% and it becomes essentially constant. If you subtract the x-value from that constant - as Shiva is doing - then Y will go negative linearly. Shiva doesn't know what the fuck he's talking about.
if so then you would expect the same results for Biden in Dem strongholds? You think Dr. Shiva, the MIT "quak" hasn't done any controls on the Biden side? He did show one county in Michigan where the downward slope did not occur. That is one control.
I guess you can also give us an easy answer to explain the Biden Benford Law violations?