Government Funded False Flag Attacks

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🧵⬇️1. Fb is LifeLog, LifeLog is Darpa, and DARPA is a Enterprise Run by CIA... Well... Past President... Big Tech, Big Pharma, MSM, HOLLYWOOD, DC...

Past Presidents....Zuckerberg, Gates...
All C_A... the Family business.... The company...


2. Past Presidents....Zuckerberg, Gates...
All C_A... the Family business.... The company...The Farm.... all C_A assets... most of them related by blood, business, or marriage...


3. "The individual is handicapped by coming face-to-face with a conspiracy so monstrous he cannot believe it exists. The American mind simply has not come to a realization of the evil which has been introduced into our midst." - J. Edgar Hoover


4. diff. names & faces.... Monsters that lurk in the Shadows. Swamp, Deep State, Establishment, Globalist Elite Cabal...

Shall we go back...How far back...


5. I know these monsters... it's when I try to explain them to others is when I run into a problem.This is why I'm better at retweeting and compiling. I never know where to start... Everytime I try to thread, i end up w/ a messy monstrous web.I'm better at helping others thread.
Which metric is a better predictor of the severity of the fall surge in US states?

1) Margin of Democrat victory in Nov 2020 election
or
2) % infected through Sep 1, 2020

Can you guess which plot is which?


The left plot is based on the % infected through Sep 1, 2020. You can see that there is very little correlation with the % infected since Sep 1.

However, there is a *strong* correlation when using the margin of Biden's victory (right).

Infections % from
https://t.co/WcXlfxv3Ah.


This is the strongest single variable I've seen in being able to explain the severity of this most recent wave in each state.

Not past infections / existing immunity, population density, racial makeup, latitude / weather / humidity, etc.

But political lean.

One can argue that states that lean Democrat are more likely to implement restrictions/mandates.

This is valid, so we test this by using the Government Stringency Index made by @UniofOxford.

We also see a correlation, but it's weaker (R^2=0.36 vs 0.50).

https://t.co/BxBBKwW6ta


To avoid look-ahead bias/confounding variables, here is the same analysis but using 2016 margin of victory as the predictor. Similar results.

This basically says that 2016 election results is a better predictor of the severity of the fall wave than intervention levels in 2020!

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