Linear Regression Analysis of Bundeslands Booster Rate and Excess Mortality
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This article continues my “excess mortality series,” where I look at the causes of excess mortality worldwide. One of my articles compared excess mortality in many countries with vaccination rates for those countries - and found a very strong relationship.
Now we have comparable data for German “Federal Lands” called Bundeslands. It was found by a very interesting substacker, Fabian Spieker.
His article is outstanding and sets a gold standard of what “substack anti-Covid-vax research” should be. If you like analytical research and factual “just the numbers” statistical presentations, consider subscribing to Fabian’s substack.
Here are Fabian’s notes:
Sachsen has the lowest booster rate (50.6%) and the lowest excess mortality rate (5.9%)
Thüringen has the second-lowest booster rate (54.2%) and the second-lowest excess mortality rate (8.1%)
Saarland has the second-highest booster rate (69.5%) and the second-highest excess mortality rate (12.9%)
Bremen has the third-highest booster rate (67.5%) and the third-highest excess mortality rate (11.9%)
Linear Regression
I decided to do what I am good at: I saved Fabian’s numbers and ran a linear regression to see the dependence of mortality on boosters. Here’s the data that I put in a spreadsheet:
I analyzed it with the GraphPad linear regression calculator:
It turns out that the regression finds a strong association between excess mortality and booster rates!
So, Germany provided data showing a SIGNIFICANT association of booster rates with excess mortality when broken down by region! What is interesting is that we have a same-ethnicity analysis for Germany. Unlike in country-by-country analyses, here we are not comparing Swedes with Bulgarians. We are comparing regions with ethnically the same people who speak the same language and follow the same German federal laws. And yet, much of the variation in mortality among German lands is explained by their booster rate!
Germany vs. The World - Same Relationship!
A while ago, I performed the same linear regression analysis for the entire world by country (where booster and excess mortality data is available).
I used somewhat different time periods. And yet, the linear fit for the whole world is remarkably similar to that for the Bundeslands in Germany! Reproducing the fits from my world analysis:
(Chile really had 125 boosters per 100 persons, and yes, it has the highest excess mortality)
Looks quite similar? Here are the equations of the straight lines:
World: Excess Mortality = 0.1675 * Booster Rate + 0.02387
Germany: Excess Mortality = 0.1732 * Booster Rate - 0.047
The equations are extremely similar, even though they are based on completely different data sets and somewhat different time periods!
Why are the lines for Germany and the entire world so similar? The reason is that Covid vaccines affect people similarly, wherever they are given. The similarity of the linear dependence between boosters and mortality, obtained from completely different data sets, proves that the relationship is real!
Is this not proof that Covid vaccines and boosters cause death?
I am sure that the good citizens of Germany, and citizens of the entire world, did not expect that boosters would cause excess mortality. We all were told that the vaccines were “safe and effective.” Everyone agreed! The newspapers told us. The politicians told us. And yet, now boosters cause excess mortality.
I am very sorry about this outcome. It was not my idea to administer these shots. Perfectly good people believed in science, their press, and their politicians. Unfortunately, that belief was misplaced: charlatans hijacked the science, and crooks bought the press.
Will the excess mortality end soon? What do you think?
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