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Investigation Finds Significant Evidence of Cheating by Ticket Counting Software
Wang Tianjiao, Senior Financial Analyst in New York
Dr. Shiva and his team used the Michigan state ballot results as a data source. There are more than 80 counties in Michigan. Dr. Shiva’s team analyzed the four largest counties. Among them, the Oakland, Macomb and Kent votes showed an abnormal distribution of votes, with obvious signs of software modification. . And the higher the rate of support for the Republican Party, the greater the proportion of software manipulation.
After posting the results of her cheating analysis in vote counting software yesterday, Shvia issued a public challenge to Trump (Trump) and Biden today (11th):
Mr. President @realDonaldTrump
& Biden teacher @JoeBiden
Our analysis in Michigan indicated that the computer likely used an algorithm to transfer 69,000 tickets. We are willing to work with your representative to conduct a strict and transparent review of our results. Are you open to this?
Mr. president @realDonaldTrump
And Mr. Biden @Joe BidenOur analysis in Michigan indicates that a computer algorithm was probably used to transfer 69,000 votes. We are ready to submit our results to a critical and transparent review with your representatives. Are you open?
-Dr. Shiva
– Dr. SHIVA Ayyadurai, PhD from MIT. Email Inventor (@va_shiva) November 11, 2020
At least 69,000 votes have been modified in favor of Trump to support Biden
Analysis by Dr. Shiva’s team showed that at least 69,000 votes for Trump were altered by Dominion software to support Biden, that is, Trump’s votes were reduced by 69,000 and Biden’s votes were increased by an additional 69,000 by the software. . This is equivalent to Trump’s original margin of 138,000 votes that was trimmed by the software.
The Oakland County with the highest number of voters had 30,000 ballots rigged, including 20,000 votes cast in advance and 10,000 votes cast on Election Day. Additionally, around 16,000 votes in Macomb County were manipulated by software, including 14,000 votes cast in advance and 2,000 votes cast on Election Day. In addition, 22,500 votes were rigged in Kent County.
What is even more shocking is that the vote counting software that is implanted with the tampering algorithm is not just Dominion’s software, it is ubiquitous in various commonly used vote counting programs. This algorithm for calculating votes from candidate A to candidate B has been developed and used as early as 2001. Diebold, the first counting software product, has such an algorithm design.
| Early voting
Early voting |
Vote on Election Day
Election day |
total
Total |
Impact on ticket difference
Impact on margin |
|
| Oakland County | 20,000 | 10,000 | 30,000 | 60,000 |
| Macomb County | 14,000 | 2500 | 16,500 | 33,000 |
| Kent County | 22,500 | 45,000 | ||
| Total | 69,000 | 138,000 |
Districts with a low rate of Republican support had no obvious changes
In all the following graphs, the x-axis of the abscissa represents the proportion of voters who support the Republican Party, and the y-axis of the ordinate represents the difference between the votes that support Trump and the votes that support the Republican Party.
For example, the small blue box in the following figure represents a district, whose proportion supports Republican candidates is 60%, that is, the X coordinate is 60%. The percentage of votes in this constituency that supports Trump’s reelection is 65%, so the difference between Trump’s voting rate and the Republican Party’s voting rate is 5%, that is, the Y coordinate is 5% (as shown below)
Under normal circumstances, voters who support a certain party often vote for the party’s presidential candidate, so Trump’s vote rate should be closer to the Republican Party’s approval rate. So the difference between the two (Y coordinate) should be close to 0%, that is, the voting results of all electoral districts should be distributed roughly close to a horizontal line in the coordinate system (as shown below ).
If Trump’s vote rate is slightly higher than other Republican candidates, then the difference (Y coordinate) between the two should be close to a positive percentage, such as 5%.
The following figure shows the distribution of votes for all electoral districts in Oakland County, the largest county in Michigan. These data come from the first votes.
It can be seen that the left half of the distribution diagram above basically conforms to the statistical law. This means that districts with little Republican support have no obvious signs of software modification. Additionally, Trump’s vote rate is about 7% higher than that of Republican members on average. This is also in line with our long-term observation that the rate of support for Trump is higher than that of Republican parties and party members.
However, the right half of the distribution map shows a downward trend as a whole, indicating that the higher the Republican support rate, the more obvious the traces of amendments to the votes that support Trump and more. votes are modified.
For example, in the constituency represented by the blue square in the lower right corner of the figure, voter support for Republican congressmen is as high as 65%, while Trump’s vote rate is roughly 25% higher. lower than that of other Republican congressional candidates.
If this is just an occasional individual situation, it might make sense. Yet statistics show that in constituencies where the GOP approval rating exceeds 20%, data changes have occurred almost entirely according to a consistent pattern. You can see that the selection areas represented by the blue squares in the figure below are very evenly distributed near the red dotted line. Only the data that has been manipulated will appear as a “perfect” linear arrangement.
This distribution can be understood as that the software algorithm will automatically reduce Trump’s votes as long as the approval rate of the Republican Party exceeds 20%. As a result, Trump’s approval rating quickly became lower than that of the Republican Party.
And the higher the Republican approval rating, the more Trump’s approval rating has been rigged. And there is a linear decline. For example, in the Crimson constituency where the GOP approval rate is 65%, Trump’s approval rate is only 40%, which is 25% lower than the GOP approval rate. For districts with higher Republican approval ratings, this difference has been revised to be larger.
Someone once argued that many GOP supporters are tired of Trump, so many people support the Republican candidate for Congress, but do not support Trump as president. If this is true, the data results should be scattered below the 0% horizontal line (as shown in the figure below), rather than the downward slope that is now present.
Wayne County, City of Detroit, Deep Blue District
Interestingly, the results of the data analysis show that the vote counting software does not appear to manipulate the votes of Wayne County, where the city of Detroit is located. Wayne County is recognized as a “dark blue” constituency and the majority of voters support the Democratic Party. As can be seen from the figure below, most constituencies (blue squares) are spread out on the left side of the coordinate system, that is, most constituencies in Wayne County have an overall support rate of less than 10% for Republican candidates. Trump’s approval rating outperforms other Republicans by roughly 10%. For example, among constituencies with 5% Republicans, Trump received an average of 15%.
In conclusion
Dr. Shiva concluded that there are still many gaps and shortcomings in the current vote counting process. Counting software must be open source and all code must be open and transparent. And the card reader handling the ballot should save the ballot photo file instead of destroying it immediately after reading the card. In fact, destroying ballot images is against federal law.
If each state uploads the images of the ballots obtained by the card reader on the Internet as a whole (hidden names and other personal information), the process of counting the ballots may be subject to public supervision and the statistics of various universities and institutions. academics can verify it personally.
Analysis of Dr. Shiva and his team
Dr. Shiva explained his analysis process clearly and clearly showing the scatter distribution map, and analyzed in detail the vote data (explanation and texture in English) for the three main counties (Oakland) in Michigan.
Ballot images are already destroyed in many states
Dr. Shiva explained his analysis process clearly and clearly by showing the scatter plot.
Detailed analysis of voting data for Michigan’s top three counties (Oakland)
Editor in Charge: Yang Yihui #
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