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MY COMMENTS TO THE MSM ON THE RIGGING OF THE 2016 PRE-ELECTION POLLS

The MSM just interviewed the authors of a new book on the reasons for Clinton’s loss.  I commented to Chris Mathews and Brian Williams of MSNBC as well as FOX and CBS on how MSM pollsters rigged the pre-election polls for Clinton.

FYI: Your guests may have looked at my 2016 Election model. It was based adjustments to the final pre-election polls which were biased for Clinton. The Democratic Party-ID share was overstated at the expense of Independents who went solidly for Trump. In addition, there is strong evidence that votes were stolen from Jill Stein – by Clinton.

Note: I exactly forecast the RECORDED EV in the last three elections: 365, 332, 306. In each case the winner did better in the True Vote than the Recorded vote. Here is the proof: https://richardcharnin.wordpress.com/2014/09/14/summary-2004-2012-election-forecast-1968-2012-true-vote-model/

In the 2016 Election Model. Trump had 306 RECORDED EV but actually had approximately 350 TRUE EV: https://richardcharnin.wordpress.com/2016/11/07/2016-election-model-forecast/

 
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Posted by on April 24, 2017 in 2016 election

 

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2016 Voter Turnout and Vote share Sensitivity Analysis: Trump won the Popular Vote

Richard Charnin
Mar. 15, 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
LINKS TO  POSTS

Trump wins all 25 scenarios over various combinations of voter turnout

Assumption
Party ID (registration) 38I-31D-27R
(Gallup voter affiliation survey average Nov.1-13,  2016)

1. Base Case Voter Turnout: Dem 65%, Rep 70%, Ind 70%
Trump 48.3-45.2% (4.2 million vote margin)

2. Worst Case Turnout: Dem 67%, Rep 68%, Ind 70%
Trump 47.6-45.9% (2.3 million vote margin)

3. Best Case Turnout: Dem 63%, Rep 72%, Ind 70%
Trump 49.1-44.5% (6.2 million vote margin)

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=610568510

Reg Voter  Gallup Base Case
Turnout Voter Affil Clinton Trump Johnson Stein
70% Ind 38% 40% 50% 5% 5%
65% Dem 31% 88% 8% 1% 3%
70% Rep 27% 7% 89% 3% 1%
Vote share 100.0% 45.2% 48.3% 3.2% 3.2%
Votes 136.2 61.6 65.8 4.4 4.4
Trump %
Dem   Rep Turnout      
Turnout 68% 69% 70% 71% 72%
63% 48.3% 48.5% 48.7% 48.9% 49.1%
64% 48.2% 48.3% 48.5% 48.7% 48.9%
65% 48.0% 48.2% 48.3% 48.5% 48.7%
66% 47.8% 48.0% 48.2% 48.3% 48.5%
67% 47.6% 47.8% 48.0% 48.2% 48.3%
Trump Vote
Dem Rep Turnout
Turnout 68% 69% 70% 71% 72%
63% 65.9 66.1 66.3 66.6 66.8
64% 65.6 65.8 66.1 66.3 66.6
65% 65.4 65.6 65.8 66.1 66.3
66% 65.1 65.3 65.6 65.8 66.1
67% 64.9 65.1 65.3 65.6 65.8
Clinton %
Dem Rep Turnout
Turnout 68% 69% 70% 71% 72%
63% 45.2% 45.0% 44.9% 44.7% 44.5%
64% 45.4% 45.2% 45.1% 44.9% 44.7%
65% 45.6% 45.4% 45.2% 45.1% 44.9%
66% 45.8% 45.6% 45.4% 45.2% 45.1%
67% 45.9% 45.8% 45.6% 45.4% 45.2%
Trump %  Margin
Dem Rep Turnout
Turnout 68% 69% 70% 71% 72%
63% 3.1% 3.5% 3.8% 4.2% 4.5%
64% 2.8% 3.1% 3.5% 3.8% 4.2%
65% 2.4% 2.8% 3.1% 3.5% 3.8%
66% 2.0% 2.4% 2.7% 3.1% 3.4%
67% 1.7% 2.0% 2.4% 2.7% 3.1%
Trump  Vote  Margin
Dem Rep Turnout
Turnout 68% 69% 70% 71% 72%
63% 4.3 4.7 5.2 5.7 6.2
64% 3.8 4.2 4.7 5.2 5.7
65% 3.3 3.7 4.2 4.7 5.2
66% 2.8 3.3 3.7 4.2 4.7
67% 2.3 2.8 3.2 3.7 4.2
 
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Posted by on March 15, 2017 in 2016 election

 

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2016 True Vote Sensitivity analysis: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Feb. 25, 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
LINKS TO  POSTS

This is an analysis of the 2016 Presidential True Vote. Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016 (80% for Clinton). View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast,  over one million  Democratic minority voters were disenfranchised via  Crosscheck,  a system which eliminated voters with duplicate names from voter rolls.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count. The results confirm other analyses which show that Trump won the popular vote.

Let TV = True Vote
RV = Recorded vote
Then we have:
RV = TV + Fraud

Given:
Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6
Election fraud components:
F =Vote flipping on maliciously coded, proprietary voting machines and central tabulators
I = Illegal voters (non-citizens)
U = Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
I = 3  million: 2.4 million voted for Clinton,  0.6 million for Trump
U =7 million: 5.6 million voted for Clinton, 1.4 million for Trump
F= 4 million (net): 5.6% ( 1 in 18) of Trump’s votes flipped to Clinton on voting machines and central tabulators. 
Trump wins by 2.8 million: 67.7-64.9 (48.3-46.3%)

Sensitivity Analysis
Given: U=7 million (5.6 million to Clinton, 1.4 million to Trump)
Worst case: (I=4 million, F=3 million) Clinton wins by 0.83 million
Base case: (I=3 million, F=4 million) Trump wins by 2.77 million
Best case: (I=2 million, F= 5 million) Trump wins by 3.57 million

Assume the following changes to the base case assumptions:
I = 2  million: 1.6 million voted for Clinton,  0.4 million for Trump
U = 3 million: 2.7 million voted for Clinton, 0.3 million for Trump
F= 4 million (net): 5.6% ( 1 in 18) of Trump’s votes flipped to Clinton on voting machines and central tabulators. 
Trump wins by 4.0 million: 66.8-62.8 (48.7-45.8%)

View the spreadsheet: https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1672204415

http://www.pewhispanic.org/2016/01/19/millennials-make-up-almost-half-of-latino-eligible-voters-in-2016/ph_election-2016_chap1-chart-08/

Number of Latino Eligible Voters Is Increasing Faster Than the Number of Latino Voters in Presidential Election Years

 Base Case Total Clinton Trump Other
Recorded vote 136.22 65.72 62.89 7.61
    48.25% 46.17% 5.59%
Illegal -3.0 -2.4 -0.6 0
Uncounted +7.0 5.6 1.4 0
Vote Flip  – -4.0 4.0 0
True Vote 140.22 64.9 67.7 7.6
 Base Case   46.3% 48.3% 5.4%
Illegals  4.0 3.0  2.0
Flip  Trump
5.0 67.7 67.9 68.1
4.0 67.5 67.7 67.9
3.0 65.9 66.1 66.3
 
 Illegals  4.0 3.0 2.0
Flip Trump %
5.0 48.3% 48.4% 48.6%
4.0 48.1% 48.3% 48.4%
3.0 47.0% 47.1% 47.3%
 
 Illegals  4.0 3.0 2.0
Flip Clinton %
5.0 46.3% 46.2% 46.0%
4.0 46.4% 46.3% 46.2%
3.0 47.6% 47.4% 47.3%
 Illegals  4.0 3.0 2.0
Trump
Flip  Margin
5.0 2.77 3.17 3.57
4.0 2.37 2.77 3.17
3.0 -0.83 -0.43 -0.03
 
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Posted by on February 25, 2017 in 2016 election

 

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Probability of exactly forecasting the electoral vote in the last three elections

Richard Charnin
Feb. 11, 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
LINKS TO  POSTS

I was asked to calculate the probability of my exact forecast of the Electoral Vote in the last three elections (365,332,306). It was a combination of experience and luck. I do not expect to exactly forecast the EV in 2020.

My Track Record
https://richardcharnin.wordpress.com/2014/09/14/summary-2004-2012-election-forecast-1968-2012-true-vote-model/

Note that the following calculation is just an approximation.

Assume the following:
1) the probability of Obama winning in 2008 was 0.95; it was also 0.95 in 2012. The probability of Trump winning in 2016 was 0.05.
Therefore the probability of forecasting all three winners correctly is
P1 = 0.045 =.95*.95*.05

2) the winning EV is in the 270-370 range.
The probability of exactly forecasting the EV in a given election is 0.01. The probability of exactly forecasting the EV in all 3 elections is 1 in a million:
P2 =.000001 = 0.01*0.01*0.01

Therefore, the probability of forecasting the winner and the EV in the three elections is
P3 = P1*P2 = .045* 0.000001 or 1 in 22 million.

To put it another way, forecasting the electoral vote exactly in three successive elections would be expected to occur just once in 22 million elections (88 million years).

 

 
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Posted by on February 11, 2017 in 2016 election

 

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More clues on Election Fraud from Humboldt Cty, CA

Richard Charnin
Jan.1, 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
LINKS TO  POSTS

Humboldt is the gift that keeps on giving. It is the only county in the U.S. which uses an Open Source System (TEVS) to count and audit votes. The system was installed in 2006.

In the CA primary, Bernie Sanders had his highest margin (71%) in Humboldt. https://richardcharnin.wordpress.com/2016/07/02/bernie-landslide-in-ca-humboldt-cty-open-source-system/

In 2008-2012, Obama did 2.58% better in Humboldt than he did in the full state. This is to be expected. But in 2016, Clinton did 1.75% worse in Humboldt. Her 4.26% increase over Obama in CA represents a 1.2 million increase in margin. Was she really that popular? Or was her vote padded?

In the 2016 presidential election, Jill Stein’s 6.1% share in Humboldt was her highest in the state – just like it was for Bernie. Clinton’s 56% share in Humboldt ranked #20 of 58 California counties. Stein’s average in the 19 counties was 2.3%. Clinton averaged 68.0%. So how come Stein did so much better in Humboldt than she did in the other 19 liberal counties?

Could it be Humboldt’s nearly foolproof Open Source voting system? Could it be that fraud was prevented in Humboldt? Could it be that nearly 2/3 of Stein’s votes were blue-shifted to Clinton? Could it be that Clinton’s 61% CA share was inflated by at least 4%? Note that 4% of 14 million votes is 560,000.

Keep in mind that the recorded vote is never equal to the True Vote. There is always election fraud. But in Humboldt, we can assume that the recorded vote is the True Vote due to its near foolproof Open Source system. There is no reason to believe Clinton’s recorded CA vote is legitimate.

Humboldt Democratic 2-party share
1988-2004 Before TEVS: 57.2%
2008-2016 After TEVS: 64.6%

California Presidential share
……Dem… Rep…Other
2008 60.21% 36.46% 3.33%
2012 60.24% 37.12% 2.64%
2016 61.73% 31.62% 6.66% HRC margin 7% over Obama?

Humboldt Presidential share
……Dem… Rep…Other
2008 62.05% 33.95%.4.00%
2012 59.68% 32.61% 7.72%
2016 56.04% 31.01% 12.95% HRC loses 3.64% vs Trump 1.60%

Democratic 2-party Presidential share
……CA….Humboldt..Diff
2008 62.28% 64.64% 2.36%
2012 61.87% 64.67% 2.80%
2016 66.13% 64.37% -1.75% HRC gains 4.26% over Obama?

…………………. Stein Clinton
1 San Francisco.. 2.4% 85.0%
2 Alameda……… 2.7  78.7
3 Marin…………..2.2  78.1
4 San Mateo……..1.6  75.7
5 Santa Cruz……..3.5  73.9
6 Santa Clara…….1.8  72.7
7 Los Angeles……2.2  71.8
8 Sonoma……….. 3.2  69.4
9 Contra Costa…..1.9  68.5
10 Imperial……….1.6  67.9
11 Monterey………2.1  66.8
12 Yolo…………….2.2 66.7
13 Napa……………2.1  63.9
14 Solano………….1.7  61.6
15 Santa Barbara ..2.1  60.6
16 Mendocino…….5.6  58.9
17 Sacramento….. 1.8  58.3
18 San Benito……. 1.7 57.1
19 San Diego………1.8 56.3
20 Humboldt……..6.2 56.0

View this spreadsheet of 58 county votes. https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1462588532

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1010903783

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Posted by on January 1, 2017 in 2016 election

 

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Why the recorded vote and unadjusted exit polls are wrong

Richard Charnin
Dec.30, 2016

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
LINKS TO  POSTS

Some analysts claim that the 2016 unadjusted state exit polls prove that the election was rigged for Trump. I proved mathematically that in the 1988-2008 presidential elections, 274 unadjusted state and 6 national exit polls were accurate and reflected true voter intent. But just because the polls were excellent indicators of the True Vote in the past does not prove that they were accurate in 2016.

Basic analysis indicates Trump won the popular and electoral vote. Pre-election and exit polls were rigged for Clinton. Democratic Party-ID was inflated in the pre-election and exit polls.

The National Election Pool of six media giants funds exit pollster Edison Research. The published results are always forced to match the recorded vote which implies zero election fraud. But there is always election fraud.  Historically, unadjusted state and national exit polls always favored the Democratic candidate, but there was  a RED shift from the Democrat in the poll to the Republican in the recorded vote.

Exit pollsters at  Edison Research never reveal the location of precincts, votes and survey results. The only way to prove that the unadjusted exit polls are correct (and the published results bogus) is 1) to reveal the complete exit poll timeline and the data for all precincts polled and 2) a True Vote analysis based on historical and current independent data.

I used the True Vote Model analysis based on a plausible number of returning voters from the prior election to prove the unadjusted exit polls were correct in 1988-2008. I used a True Vote Model analysis based on Gallup Party-ID voter affiliation to prove that the unadjusted polls were bogus in 2016.

The True Vote Model indicates that the 1988-2008 unadjusted exit polls were accurate.
https://richardcharnin.wordpress.com/2014/09/14/summary-2004-2012-election-forecast-1968-2012-true-vote-model/

The 2016 election was different in kind from prior elections; the Democrat was the establishment candidate. It was established beyond a reasonable doubt that the primaries were stolen from Bernie Sanders by the DNC which colluded with the media.

As usual, state and national exit polls were forced to match the recorded vote. This was the first election in which the media discussed election fraud – but avoided the obvious U.S. suspects: the rigged voting machines, illegal and disenfranchised voters. No, it was the Russians!

And we are supposed to believe that the MSM would not rig the unadjusted exit polls to match the rigged  pre-election polls  to make it appear that Clinton was the winner?
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=0

Party-ID
Nine Pre-election polls (average): 28.8 Ind – 38.7 Dem- 31.9 Rep.
Final National Exit Poll (CNN): 31 Ind – 36 Dem – 33 Rep.
Gallup national voter affiliation survey: 40 Ind -32 Dem -28 Rep. https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=505041111

Nine Pre-election polls 
Clinton won the average: 45.8-43.3%
Trump won the average Gallup-adjusted poll: 44.4-42.9%
Trump won Independents: 43.6-33.8%

Final  National Exit Poll (forced to match the Recorded Vote)
Clinton won the reported vote: 48.2-46.2%.
Clinton won the National Exit Poll: 47.7-46.2%.
Trump won Independents by just 46-42% – a 5.8% discrepancy from the pre-election polls which he led by 9.8%. This anomaly is additional evidence that Trump won the True Vote.

Unadjusted exit polls (28 states)
Clinton won the polls: 49.6-43.6%
Clinton won the corresponding recorded vote: 49.3-45.2%

States not exit polled
Trump won: 50.4-43.7%

True Vote
Trump led the True Vote Model (three scenarios of his share of late undecided voters)
– Scenario I:  47.5-45.1%, 306 EV (50% undecided)
– Scenario II: 47.9-44.7%, 321 EV (60% undecided)
– Scenario III: 48.3-44.3%, 351 EV (70% undecided)

 
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Posted by on December 30, 2016 in 2016 election

 

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2016 Cumulative Vote Shares: Illinois, Michigan, California

2016 Cumulative Vote Shares: Illinois, Michigan, California

Richard Charnin
Dec. 22, 2016

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
LINKS TO  POSTS

102 Illinois Counties: Cumulative Vote Shares

Trump won 91 of 102 counties
Illinois: 5,095,677 Votes, Clinton 58.4%-41.6%, Margin: 859,319
Cook County: 1,968,795 Votes, Clinton 77.6%-22.4%, Margin:1,088,369
Other 101: 3,126,882 Votes, Trump 53.7-46.3%, Margin: 229,050

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1076651857

 

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83 Michigan Counties: Cumulative Vote Shares

Trump won 75 of 83 counties.
Clinton won Wayne County (Detroit) by 290,451 votes (69.4-30.1%)
Trump won the other 82 counties by 301,155 votes (54-46%)

NY Post Dec. 14, 2016:
“The Detroit News found voting scanning machines at 248 of the city’s 662 precincts — 37 percent — tabulated more ballots than the number of actual voters counted in the poll books.

“There’s always going to be small problems to some degree, but we didn’t expect the degree of problem we saw in Detroit. This isn’t normal,” Krista Haroutunian, chairwoman of the Wayne County Board of Canvassers, told the paper.”

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=65235688

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58 California Counties: Cumulative Vote Shares

Based on the following data, it is difficult to believe that Hillary won the state by 4.27 million votes. She won the national recorded vote by 2.8 million, so Trump won the other states by at least 1.5 million (and that is conservative).

– Clinton did 7.0% better in CA than Obama in 2012. Clinton won by 61.7-31.6%, a 30.1% margin (4.27 million votes). Obama won by 60.2-37.1%, a 23.1% margin (3.01 million votes). If Clinton’s margin was 23.1%, she would have won by 3.3 million votes.In the CNN final CA exit poll (matched to the reported vote), the Party-ID is 47D-23R-30I . Was Clinton more popular than Obama? Not plausible.

– When the CA final exit poll Party-ID is adjusted from 47D-23R-30I to 34.2D-22.3R-43.5I based on the change in national Party ID from 2014, Clinton wins CA by 56.1-36.5% (2.78 million votes).

– Humboldt County, CA is the only county in the U.S. which uses Open Source software to count and audit votes. In the CA primary, Sanders had his highest vote share (71%) in Humboldt. Jill Stein also had her highest share (6.2%) in Humboldt.

Is it just a coincidence that Bernie and Jill both had their highest vote shares in Humboldt? Or was it due to the foolproof Open Source voting system?

– Hillary had 56% in Humboldt, nearly 6% lower than her total CA share. It is a fact that Bernie was cheated in CA by massive fraud. Who is to say that Hillary did not also cheat in CA to pad her popular vote margin?

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1462588532

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Posted by on December 22, 2016 in 2016 election

 

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