Author Archives: Richard Charnin

About Richard Charnin

In 1965, I graduated from Queens College (NY) with a BA in Mathematics. I later obtained an MS in Applied Mathematics from Adelphi University and an MS in Operations Research from the Polytechnic Institute of NY. I started out as a numerical control engineer/programmer for a major defense/aerospace manufacturer and then moved to Wall Street as a manager/developer of corporate finance quantitative applications for several major investment banks. I consulted in quantitative applications development for major domestic and foreign financial institutions, investment firms and industrial corporations. In 2004 l began posting weekly "Election Model" projections based on state and national polls. As "TruthIsAll", I have been posting election analysis to determine the True Vote ever since.


Richard Charnin
July 17, 2017

There were approximately 50 UNNATURAL deaths (murders, accidents, suicides) of HOLISTIC doctors in one year. Was this reported in the mainstream media? What is the probability?

Media shills like Snopes make the same mistakes as Warren Commission defenders. They don’t understand that calculating probabilities requires…
1) the number of UNNATURAL deaths – NOT total deaths.
2) UNNATURAL MORTALITY RATES: homicides (0.00005), accidents (0.00038) and suicides (0.00012). They are much lower than rates of “natural” causes.
3) the NUMBER of HOLISTIC doctors- NOT the total of ALL doctors.
4) knowing that MOTIVE for the deaths is NOT a factor in the calculation.

The Poisson probability function is based on the number of ACTUAL UNNATURAL deaths (n) and the EXPECTED number (E) in the group (N).
Probability = poisson (n, E, false)
E=N*R*T is based on the WEIGHTED unnatural mortality rate (R), population size (N) and the time period (T).

Since we do not know N, the number of holistic doctors, we calculate the probability assuming N=50,000, 75,000 and 100,000. The probability decreases as N increases.

Given: n= 50 unnatural deaths, R= 0.0002, T= 1 year, N is unknown.
N= 100,000: P = 7.63E-09 or P= 1 in 131,058,359
(E=20 deaths would be expected)

N= 75,000: P= 6.41E-13 = 1 in 1,559,298,094,732 (1 in 1.6 trillion)
(E=15 deaths would be expected)

N= 50,000: P= 1.49E-19 = 1 in 6,699,149,835,876,030,000
(1 in 6.7 million trllion)
(E=10 deaths would be expected)

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Posted by on July 17, 2017 in Media, Uncategorized


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Probability Analysis of Unlikely Historical Events

Richard Charnin

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

Did you ever view a discussion, much less a probability analysis of  these events in the mainstream media?

Conspiracy Theories and Mathematical probabilities

Unnatural Deaths of at least 78 JFK-related witnesses from 1963-1978.

Seth Rich and 8 other DNC-related suspicious deaths

Election Fraud: True Vote vs. Recorded

Suspicious Deaths of 75 Bankers and 125 Scientists
Suspicious Deaths of 11 Holistic Doctors in 3 months

Suspicious Deaths of 16 Microbiologists in 4 months

10 Terrorist Attacks and Concurrent Drills

Cancer Deaths of 7 Latin American Leaders

Mystery Deaths for various group size assumptions

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Posted by on July 14, 2017 in JFK, Uncategorized


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2016 National Exit Poll vs. True Vote Model: How did you vote in the 2012 election?

Richard Charnin
July 9, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

The 2008 presidential election was the last one in which the National (NEP) and state exit polls asked “How Did You Vote in the Last Election?”. A plausible reason is that the question provided clear proof of fraud in all elections from 1988-2008. The How Voted crosstab matrix required more returning Bush voters than were still alive in order to match the bogus recorded vote in 1992 (119% turnout), 2004 (110%) and 2008 (103%). Conversely, the True Vote Model, which used a feasible estimate of returning voters, confirmed the unadjusted, pristine state and national exit polls.

Since the “How Voted” question was not asked, we can derive a crosstab to match the 2016 recorded vote using assumptions for 2012 returning voter turnout and 2016 vote shares.

General Assumption: 1% Annual voter mortality

2016 Estimated National Exit Poll assumptions
Equal 96% turnout of living 2012 Obama and Romney voters.
Clinton wins 87% of returning Obama and 7% of returning Romney voters.
Trump wins 7% of returning Obama and 88% of returning Romney voters.
Trump wins new voters by 48-47%.
Clinton wins by 2.9 million recorded votes, 48.3-46.2%.

2016 True Vote Model assumptions
Voter turnout: 92% of living Obama voters and 96% of Romney voters
Clinton wins 82% of returning Obama and 7% of returning Romney voters
Trump wins 10% of returning Obama and 88% of returning Romney voters
New voters: Trump and Clinton 45% tie
Trump wins the base case scenario by 3.6 million votes, 47.8-45.1%.

2016 TVM rationale
– 96% Romney voter turnout vs. 92% for Obama: approximately 2.5 million living Obama voters were angry Sanders voters who did not vote.
– Clinton’s 82% share of returning Obama voters: approximately 2.6 million Obama voters were angry Sanders voters who defected to Jill Stein, Trump and Johnson.

NATIONAL EXIT POLL – is always forced to match the recorded vote
“HOW VOTED IN 2012” was not asked in the 2016 NEP.
It would have looked something like this…
2016….. Mix Clinton Trump Other
Obama…. 44.6% 87% 7% 6%
Romney… 41.2% 7% 88% 5%
Other…… 1.5% 45% 45% 10%
DNV….. 12.6% 47% 48% 5.4%

Total…. 100% 48.3% 46.2% 5.5%
Vote…. 136.2 65.7 62.9 7.6

2012….. Mix Clinton Trump Other
Obama…. 42.7% 82% 10% 8%
Romney… 41.2% 7% 88% 5%
Other…… 1.5% 45% 45% 10%
DNV…… 14.5% 45% 45% 10%

Total…. 100% 45.1% 47.8% 7.1%
Vote…. 136.2 61.5 65.1 9.7

Sensitivity analysis
The tables display Trump’s total vote share and margin over a range of 25 scenarios of his  shares of returning Obama (8-12%) and Romney voters (86-90%). He wins 24 of the 25 scenarios. In the worst case scenario, Trump loses by 1 million votes (46.9-46.1%). In the best case, he wins by 8 million (49.5-43.5%). Trump wins the base case scenario by 3.6 million votes, 47.8-45.1%.

View the spreadsheet:


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Trump won the True vote; Clinton won the Fraudulent Recorded vote

Richard Charnin
June 24, 2017
Updated: July 10,2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

Hillary Clinton’s 2.9 million recorded vote margin is a myth. The simple proof: ALL elections are fraudulent. THE RECORDED VOTE IS NEVER EQUAL TO THE TRUE VOTE. Mainstream media pre-election and exit polls were rigged for Clinton.  

She won the Recorded Vote 48.3-46.2% . Trump had 306 EV. The True Vote Model indicates that Trump won by 48-44% (5 million votes) with 351 EV.

1988-2012: Democrats won the True Vote and the unadjusted exit polls 52-42%. They won the recorded vote by 48-46%. They won the True Vote in every election. The exit polls and the True Vote Model indicated that the 1988,2000 and 2004 elections were stolen.

So what changed in 2016? The establishment was in the tank for Clinton. The pre-election and exit polls were biased in her favor. Trump won the primaries easily; Clinton had to cheat Bernie. Trump and Bernie drew big crowds, Clinton drew small crowds. Trump and Bernie won (non-scientific) online debate polls by large margins.

2016 Democratic primary: 11 of 26 unadjusted exit polls exceeded the MoE for Sanders. Odds against: 79 billion to one.

2016 Election: Clinton led 9 pre-election polls by 2.5% – exactly matching the recorded vote.
Pre-election polls were rigged for Clinton. Democratic Party ID was inflated.
True National Party ID was 40-I-32D-28R

Unadjusted exit polls were also rigged for Clinton. Large exit poll discrepancies favored Clinton in the Rust belt and Red states.  Exit polls matched the recorded vote in large states (i.e. CA). If the recorded vote was bogus, then the unadjusted exit polls must have also overstated Clinton shares. In NY the 5% discrepancy actually favored Trump.

True Vote Sensitivity Analysis – returning 2012 voters. Trump wins all 25 scenarios.

Ohio unadjusted exit poll indicated a implausible 47% tie .  Trump won Ohio by 51.7-43.6%. But the unadjusted poll indicates that he won by just 47.1-47.0%. To match the unadjusted poll, Clinton needed to win Independents by 50-35%, an implausible margin.  However, the final Ohio exit poll (which is always matched to the recorded vote) indicated that Trump won Independents by 51-38%.

Humboldt County, CA is only US county with an Open Source foolproof vote count/audit. Bernie had his highest CA share in Humboldt (71%). Jill Stein had her highest share there(6%) compared to 1% elsewhere.

Voter turnout: millions of Sanders voters a) did not turnout, b) voted for Stein, c) voted for Trump,

Trump and Bernie each won Independents by 10%. Trump had a higher percentage of Republicans than Clinton had of Democrats.

“Crosscheck”: It is estimated that one million votes were suppressed, costing Hillary.

Illegal voters: Estimated at 1-5 million. Obama encouraged illegals to vote.

Fraction Magic: votes were flipped to Clinton on Central tabulators (Bev Harris)

Hillary supporter George Soros had an interest in voting machines in 16 states.

Recounts in MI and WI showed that Trump did better than reported. Wayne County, MI had more votes than registered voters.

National Exit Poll- When Decided:  The NEP is ALWAYS adjusted to match the recorded vote. The 2016 NEP indicates that 26% of voters decided after Oct.1;  48%  voted for Trump and 40% for Clinton. Of the 74% who decided before Oct.1, Clinton led 51-45%.

The 2016 NEP indicates that 40% of voters decided after Sept.1. Trump won these voters by 48.0-42.0%. Clinton won voters who decided before Sept.1 by 52.5-45.0%. Since the poll was forced to match Clinton’s 48.3-46.2% recorded vote, it appears that her pre-Sept. vote share was inflated.

The third-party Recorded vote is another clue that Clinton’s vote was rigged.
According to the National Exit Poll, 4% of voters who decided before Oct.1 voted for a third party candidate; 12% voted third party after Oct.1. Jill Stein had just 1% of the total recorded vote. Could it be that Jill really had at least 3% of which 2% or more were shifted to Clinton?

Decided Pct Clinton Trump Other
Post Oct. 1 26% 40.0% 48.0% 12.0%
Pre Oct. 1.. 74% 51.0% 45.0% 4.0%
Total……… 100% 48.3% 46.2% 5.5%

Decided Pct Clinton Trump Other
Post Sept. 1 40% 42.0% 48.0% 10.0%
Pre Sept 1.. 60% 52.5% 45.0% 2.5%
Total……… 100% 48.3% 46.2% 5.5%

Were Clinton’s pre-Oct.  poll shares rigged to match the recorded vote? Clinton won the national recorded vote by 2.9 million. She won IL, CA and NY by a combined 7 million votes. So Trump won the recorded vote by at least 4 million everywhere else. But Trump’s True Vote margin had to be higher than 4 million. Here’s why: As many as 3 million of Clinton’s 7 million margin in IL, CA and NY may have been fraudulent- matching her national 3 million margin. Were Clinton’s votes inflated (rigged) in these and other states?

State exit poll………….. IL…….. CA……. NY
Total Recorded %…… 56-39-5.. 62-32-4. 60-37-3
Before Oct.1………….66-32-2.. 67-29-4. 67-31-2 < Rigged?
After Oct.1………….. 33-55-12. 51-42-7. 38-53-9 < shift to Trump & 3rd party
Votes (mil)…………….. 5.5……. 14.2……. 7.5
Margin (mil)…………… 0.95……. 4.3…….. 1.7 Total 6.95 million

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Posted by on June 24, 2017 in 2016 election


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Seth Rich/JFK Mortality Probability Calculator

Richard Charnin
Updated: 7/15/17

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

It’s not just about Seth Rich. Applied Mathematics indicates a virtual 100% probability of a cover-up.

Assume a random group of 10,000 DNC/Wikileaks related individuals:
-There were 8 suspicious deaths (5 homicides) in 3 months from April 2016.
The probability of at least 5 homicides in 3 months is 1 in 6.5 million.
– There were 12 suspicious deaths (8 homicides) in 15 months since April 2016.
The probability of at least 8 homicides in 15 months is 1 in 3.4 million.

4/18: John Jones, lawyer who defended Assange, run over by train.
May : Michael Ratner (Wikileaks NY lawyer), cancer.
6/22: John Ashe, ex-UN official, barbell fell on neck. He was going to testify on DNC and Clinton.
6/23: Mike Flynn,48, died day he reported on Clinton Foundation (unknown).
7/10: Seth Rich, DNC staffer, shot twice in back.
7/25: Joe Montano,47, DNC, heart attack day before the DNC convention.
8/01: Victor Thorn, gunshot wound, author of books on Clintons.
8/02: Shawn Lucas, DNC process server, lethal combination of drugs.
Oct : Gavin McFayden (Wikileaks founder), cancer.
May : Peter Smith, GOP operative, found dead from asphyxiation in a Minnesota hotel room just days after talking to the Wall Street Journal about his efforts to obtain Hillary’s Clinton’s missing emails. Suicide?
May : Beranton Whisenant, prosecutor investigating DNC, found dead on Hollywood, FL beach.
July: Klaus Eberwein, former Haiti Government official found dead in a motel room with a gunshot wound to the head. Was to testify on Clinton Foundation connection to Haitian earthquake charity.

How many DNC voter data admins were there? How many DNC process servers? How many HRC biographers? How many Assange lawyers? How many Wikileaks founders? How many UN officials preparing to testify? How many DNC officials? How many investigative reporters on the Clintons? Are any of these deaths being investigated? Any suspects?

What is the probability that in a random group of N DNC/Wikileaks related individuals, n would die unnaturally in T years given group mortality rate R? Three (R, n, T) of the 4 parameters are known constants. The only unknown is N, the number of individuals in the study.
The expected number of unnatural deaths: E = N*R*T

The  Poisson distribution function calculates the probability of rare events. The probability of n homicides when E are expected is P = poisson (n,E,false).

There were 7 suspicious DNC/Wikileaks deaths in 3 months:
n = 7
R = 0.0002 (DC homicide rate; 135 homicides/681170 pop.)
T = 3 months (0.25 Year).
N = relevant DNC/Wikileaks population.
E = N*R*T =N*0.0002*0.25 (expected number of homicides).

Assume N = 1,000 DNC/Wikileaks related  persons, then for
n=3 homicides: P= 1 in 52 thousand
n=4 homicides: P= 1 in 4.2 million
n=5 homicides: P= 1 in 422 million
n=6 homicides: P= 1 in 51 billion
n=7 homicides: P= 1 in 7.2 trillion

Assume: n=7, T= 0.25 (3 months), R=0.0002 and
N= 500, P = 1 in 902.1 trillion
N= 1,000, P = 1 in 7.2 trillion
N= 3,000, P = 1 in 3.6 billion
N= 10,000, P = 1 in 1.1 million

Since N is unknown, let’s view a SENSITIVITY ANALYSIS table over a range of N for n=5,6,7,8,9:

Probability of n homicides in a random group of
n 10,000 20,000 30,000 40,000
5 0.02% 0.31% 1.41% 3.61%
6 0.00% 0.05% 0.35% 1.20%
7 0.00% 0.01% 0.08% 0.34%
8 0.00% 0.00% 0.01% 0.09%
9 0.00% 0.00% 0.00% 0.02%

The analysis assumes the 7 DNC/Wikileaksdeaths were all homicides. If they were a combination of  homicides,  accidents,  suicides and heart attacks, we need to use a weighted mortality rate. This is conservative since “accidents” and “suicides” were likely homicides. The heart attack was also highly suspicious.

………………..National Weighted for T=.25 (3 months)
COD………. n Rate……… Rate
Accident.. 2 0.00038 0.00076
Suicide…. 1 0.00012 0.00012
Homicide. 3 0.00005 0.00015
Natural?.. 1 0.00173 0.00173 heart attack/cancer
Total…….7 0.00228 0.00039

For n=7, N= 1000, R = 0.00039, T = 0.25 (3 months)
Probability: P = 1 in 60 billion.

For n=5 homicides, N=1000, T= 0.27 (14 weeks), R = 0.00005
P = 1 in 275 billion

For n =7 (5 homicides, 2 heart attacks), N=1000, T= 0.25, R = 0.00052
P = 1 in 8 billion.

For n=9 (5 homicides, 2 heart attacks and 2 cancers):
R=0.0008, N=1000, T=0.5 (6 months)
P = 1 in 2.5 billion.

There were n=6 suspicious DNC/Wikileaks deaths in T=5 weeks (0.10 years). Mortality rate R=0.0002. Assuming a random group of N individuals, the probability that it was just a coincidence is
N Probability
500  1 in 900 trillion
1000 1 in 14 trillion
3000 1 in 20 billion
30000 1 in 32000

You can run the spreadsheet calculator for any combination of N, n, R and T.

Probability of 0-7 homicides in a random group of 40,000 over 3 months

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In 1964-78, there were an estimated 1500 JFK-related material witnesses, of whom 122 died suspiciously. Seventy-eight(78) of the 122 were officially ruled unnatural. Of the 78, 34 were homicides, 24 accidents, 16 suicides and 4 unknown. The probability of 78 unnatural deaths: 2.7E-31 (1 in a million trillion trillion).

Just 12 accidents and 3 suicides were expected statistically, therefore approximately 60 of the 78 unnatural deaths were likely homicides.

Of the remaining 44 “natural” deaths (heart attacks, sudden cancers, other), approximately 25-30 were homicides based on the total number of expected deaths. Therefore, there were 85-90 homicides among the 122 suspicious deaths. For 10,000 witnesses, Probability: 5.5E-47


Simkin JFK Index of 656 key individuals: 44 homicides, Probability = 4.7 E-60


Posted by on May 20, 2017 in 2016 election, JFK, Uncategorized


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Election Justice USA: Evidence of Massive Election Fraud in the Primaries

Richard Charnin
April 29, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

An excellent article from “Project Censored 2017”.

On July 25th, 2016, Election Justice USA (EJUSA) released a hundred-page report compiling evidence of massive election fraud during the 2016 Democratic primaries. Election Justice USA is a non-partisan organization that consists of attorneys, technologists, journalists, statisticians, and activists.

Essentially, EJUSA concludes that Bernie Sanders may have lost an upper estimate of 184 pledged delegates due to specific irregularities and instances of fraud. Their conclusions? The combination of voter suppression, registration tampering, voter purging, and the manipulation of computerized voting machines, likely cost Bernie Sanders the election.
Additionally, Election Justice USA found that the computer counts differed widely from the exit poll projections, but only for the Democratic Party primaries. According to election analyst Richard Charnin, Bernie Sanders’ exit poll share exceeded his recorded vote share by greater than the margin of error in 11 of 26 primaries: Alabama, Arizona, Georgia, Massachusetts, New York, Ohio, Mississippi, South Carolina, Texas, Wisconsin, and West Virginia.

Charnin reported that the probability of this occurring is 1 in 77 billion, which raises the strong possibility of election fraud. Yet, almost no discrepancies were found in the data for the Republican Party primaries. This is particularly remarkable, because the exit polls were conducted on the same day, in the same precincts, with the same interviewers, and used the same methodologies for both the parties. So, this evidence suggests that the computer counts were only accurate for the Republican Party, while the computer counts for the Democratic Party primaries remain largely unverified.



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Posted by on April 29, 2017 in 2016 election


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University of Virginia Study: 20% of Trump Voters were former Obama Voters

Richard Charnin
April 29, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

Larry Sabato is the founder and director of the Center for Politics at the University of Virginia.

Sabato said: “This is the largest study of just Trump voters… The first thing that is perfectly clear is that Trump has not lost almost none of his backers, which includes the soft Trump voters. He’s still got 92-93% of them supporting him. It’s also true he hasn’t gained many people from the other side. We live in a very polarized era… What I found fascinating, nobody else has identified this, 20% of Trump voters actually voted for Obama either in 2008 or in 2012 or in both years. In other words a fifth of his vote came from Obama voters”.

I calculated Trump’s vote share based on the above: If 20% of Trump voters were former Obama voters, then the vote share calculation indicates that Trump won by an estimated 48.3-42.9% (7 million votes), confirming the True Vote Model: Trump by 48.5-44.3% (351-187 EV).

2012….. Pct……Trump Clinton Other……..Trump share
Obama…. 51.1%…. 19%….75%….. 6%……….9.7% 20% <<<<
Romney… 47.2%…. 80%…..8%…..12%……..37.8% 78%
Other…….. 1.7%….. 48%….46%……6%……….0.8% 2%

Total….. 100%…..48.3%. 42.9%…8.8%…..48.3% 100%


Includes estimated 2012 voter turnout in 2016 and new voters.
Assumption: 18.3% of Trump voters were returning Obama voters
True Vote share: Clinton 42.8%, Trump 47.8%, Other 9.4%
True Vote: Clinton 58.3 million, Trump 65.1, Other 12.8
Recorded share: Clinton 48.3%, Trump 46.2%, Other 5.5%
Recorded Vote: Clinton 65.7 million, Trump 62.9, Other 7.6
(9.7 million flip in margin (7.1%) from the Recorded to True vote)
Returning and new voters
Clinton Trump Other Trump%
Obama 44.6 11.9 3.0 18.3%
Romney 4.6 44.1 8.6 67.8%
Other 0.9 0.9 0.2 1.4%
DNV (new) 8.1 8.1 1.0 12.5%
Total 58.3 65.1 12.8 100.0%
2012 Mix Clinton Trump Other Turnout
Obama 43.66% 75% 20% 5% 94%
Romney 42.09% 8% 77% 15% 98%
Other 1.54% 45% 45% 10% 95%
DNV (new) 12.70% 47% 47% 6%
True Share 100% 42.8% 47.8% 9.4%
 True Vote 136.2 58.3 65.1 12.8
Recorded 136.2 65.7 62.9 7.6
Change -7.4 2.2 5.2
Trump% Obama 18% 19% 20% 21% 22%
Trump% Romney Trump share
79% 47.8% 48.2% 48.7% 49.1% 49.5%
78% 47.4% 47.8% 48.2% 48.7% 49.1%
77% 46.9% 47.4% 47.8% 48.2% 48.7%
76% 46.5% 47.0% 47.4% 47.8% 48.3%
75% 46.1% 46.5% 47.0% 47.4% 47.8%
Clinton share
79% 42.8% 42.4% 41.9% 41.5% 41.1%
78% 43.2% 42.8% 42.4% 41.9% 41.5%
77% 43.7% 43.2% 42.8% 42.3% 41.9%
76% 44.1% 43.6% 43.2% 42.8% 42.3%
75% 44.5% 44.1% 43.6% 43.2% 42.7%
Trump % margin
79% 5.0% 5.8% 6.7% 7.6% 8.5%
78% 4.1% 5.0% 5.9% 6.7% 7.6%
77% 3.3% 4.2% 5.0% 5.9% 6.8%
76% 2.4% 3.3% 4.2% 5.1% 5.9%
75% 1.6% 2.5% 3.3% 4.2% 5.1%
Trump vote margin
79% 6.77 7.96 9.15 10.33 11.52
78% 5.62 6.81 8.00 9.19 10.38
77% 4.47 5.66 6.85 8.04 9.23
76% 3.33 4.52 5.71 6.89 8.08
75% 2.18 3.37 4.56 5.75 6.94

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Posted by on April 29, 2017 in 2016 election


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Richard Charnin's Blog

JFK Conspiracy and Systemic Election Fraud Analysis