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The 2016 True Vote Model (TVM)

Richard Charnin
Aug. 20, 2017

In 2012, National and state exit polls stopped asking the question: “Who did you vote for in the last election”. Exit polls are always forced a match to the recorded vote assuming there is zero fraud.

In the 2016 TVM, vote shares required to match the recorded vote are calculated.
The True Vote is estimated by adjusting prior election voter turnout while using the same vote shares used in the recorded vote match.

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=664338739

MICHIGAN

There are two sets of voter turnout assumptions. Vote shares are the same in each.

Case 1. Equal 95% turnout of returning Obama and Romney voters. Vote shares are calculated to automatically match the RECORDED vote.
Trump wins by 47.50-47.27% (10,821 votes)

Case 2. Base case TRUE VOTE
Estimate: 88% turnout of Obama, 95% turnout of Romney voters.
Trump wins by 48.75-45.97% (133,000 votes)
Assumption:  1 in 7 Bernie Sanders voters in the primary who voted for Obama did not return to vote in the presidential election as they were cheated in the primaries.

True Vote Sensitivity Analysis:
View a 25 scenario matrix for 5 Trump shares of returning Obama and 5 Trump shares of returning Romney voters. Trump wins 20 of 25 scenarios.

Worst case: Clinton wins by 48.6-46.1% (117,000 votes)
Base case: Trump wins by 48.7-46.0% (133,000 votes)
Best case: Trump wins by 50.5-44.3% (297,000 votes)

NATIONAL
US 2016 True Vote Model
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1768941212

 
 

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2016 Election Model- 9 pre-election polls: 5 Non-MSM and 4 MSM pollsters

Richard Charnin
Aug, 4, 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

The following are the basic steps used to estimate 2016 National True Vote shares.  The True Vote Model utilizes nine  pre-election polls.  Party-ID varies greatly among the polls. Therefore, Gallup’s dedicated voter affiliation (Party-ID) survey is used to adjust the national poll shares.

The 2016 Gallup national survey is used to approximate state Party-IDs by calculating the change from 2012 National Party-ID to 2016 Gallup Party-ID.  The projected state vote share is calculated by applying the average of the 9 national pre-election Party-ID poll shares to the 2016 state Party-ID. The electoral vote is then calculated. View the full set of calculations in this spreadsheet: https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1036175945

National True Vote Model: Basic Methodology

1) Compare MSM vs. non-MSM polls (Party-ID and vote shares).
2) Adjust pollsters Party-ID to Gallup voter affiliation
3) Allocate undecided voters.
4) View the effect of these adjustments to the pre-election vote shares.

  • MSM pollsters overweighted Democrats Party-ID and underweighted Independents compared to non-MSM pollsters. Clinton wins the polls by 45.8-43.6%, matching her 2.1% recorded vote margin.
  • 2 Apply Gallup voter affiliation survey of National Party-ID (40I-32D-28R)  to each of the nine polls, Trump is a 44.1-43.3% winner.
  • 3 Note: the polls did not allocate undecided voters (approximately 6%), which typically break 3-1 for the challenger. Trump was the de-facto challenger.
  • 4 Effect: Allocating  undecided voters (4.5% to Trump and 1.5% to Clinton) to the Gallup-adjusted vote shares, Trump is the winner by 48.6-44.8%.

Non-MSM………….Party-ID…………..Pre-election……….Gallup (40I-32D-28R)
Polls………………Ind Dem Rep…….. Clinton..Trump…..Clinton Trump
IBD………………..37% 34% 29%…….. 43%….45%……..41.9% 45.3%
Rasmussen……..32% 40% 28%………45%….43%……..40.6% 45.3%
Quinnipiac………26% 40% 34%………47%….40%……..44.7% 40.8%
Gravis……………27% 40% 33%………47%….45%……..43.6% 45.5%
USC/Dormsite… 30% 38% 32%………44%….47%……..41.7% 48.2%
Average………..30.4% 38.4% 31.2%…45.2%.44.0%…..42.5% 45.0%

MSM……………..Party-ID……………..Pre-election…….Gallup Adj
Polls…………….Ind Dem Rep………..Clinton Trump..Clinton Trump
Reuters…………16% 45% 38%………42% ….39%…….36.0% 36.8%
Fox News………19% 43% 38%………48%…..44%…….45.8% 43.9%
CNN……………..43% 31% 26%………49%……44%…..48.6% 44.4%
ABC ……………..29% 37% 29%………47%…..45%……46.8% 47.0%
Average………26.8% 39.0% 32.8%…46.5% 43.0%……44.3% 43.0%

Summary…………….Party-ID…………Pre-election……Gallup Adj
…………………Ind…..Dem….Rep……Clinton.Trump..Clinton Trump
9 polls……….28.8% 38.7% 31.9%…..45.8% 43.6%…..43.3% 44.1%
5 nonMSM….30.4% 38.4% 31.2%…..45.2% 44.0%….42.5% 45.0%
4 MSM………26.8% 39.0% 32.8%…..46.5% 43.0%……44.3% 43.0%

Allocating  undecided voters (4.5% to Trump and 1.5% to Clinton) to the Gallup-adjusted vote shares, Trump is the winner by 48.6-44.8%.

 
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Posted by on August 5, 2017 in 2016 election, True Vote Models

 

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Adjusted Pre-election polls in the True Vote Model indicate Trump won by 5 million votes

Richard Charnin
Aug.2, 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 analysis shows that although Clinton won the Recorded Vote by 48.3-46.2% (2.8 million votes), Trump won the True Vote.

Plausible adjustments made to nine pre-election polls in the True Vote Model are the core of the analysis. These polls had Clinton winning by 45.8-43.6% with 298-240 electoral votes: Ipsos/Reuters, IBD/Tipp, Rasmussen, Quinnipiac, Fox News, CNN, ABC, Gravis, LA Times.

Adjusting  Party-ID to the Gallup voter affiliation survey, Trump won by 43.4-43.1% with 306-232 Electoral votes. After allocating 75% of undecided voters to Trump, the de-facto challenger, the True Vote Model indicates that Trump won  by 48.2-44.5% (5.1 million votes) with 336 electoral votes . Historically, challengers won a solid majority (65-90%) of undecided voters when the incumbent was unpopular. Clinton and the Democrats were unpopular, especially after she stole the primary from Bernie Sanders.

View the Model
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1036175945

Model Results
Pre-election poll averages based on:
Party-ID: Clinton 45.8-43.6
Gallup voter affiliation: Trump 43.4-43.1

Forecast Model (post-UVA)
Party-ID (9 Pre-election poll average):  Clinton 46.7-46.2
Gallup Party-ID: Trump 48.2-44.5

Trump Electoral votes: Pre and post UVA
Snapshot EV: pre-UVA: 306 (exact forecast); post-UVA EV: 336

Expected EV based on state win probabilities
Pre UVA: 289; post UVA: 351

Method
Calculate the average of 9 final pre-election polls (Party-ID and vote shares).
Calculate National Vote shares using Party-ID from

  • 9-poll average: 28.9I-38.7D-31.9R
  • Gallup voter affiliation survey: 40I-32D-28R

Gallup Voter Affiliation
1- Nov. 1-6: 36I, 31D, 27R (6 other)
2- Nov. 9-13: 40I, 30D, 27R (3 other)
Average (Election Day) : 38I, 30.5D, 27R (4.5 other)

Calculate State Vote shares
State Party-ID based on proportional change in National Party-ID from 2012 to 2016 Gallup survey applied to 2012 State Party-ID.
2012: 40.3D, 35.4R, 24.7I  
2016: 32D, 28R, 40I

Undecided voter allocation (UVA): 75% to Trump

Sensitivity Analysis
15 vote share/margin scenarios (pre-UVA) based on Trump % of Rep and Ind
Best Case: Trump 45.0-42.7
Base Case: Trump 43.4-43.1
Worst Case: Clinton 43.5-41.9

Electoral Vote Scenarios
Recorded EV = 306
Forecast  EV (pre-UVA) = 306
Forecast True EV (post-UVA) = 336
Difference between 306 EV and 336 EV due to MI (16) and NJ (14)

Expected EV
(based on state win probabilities post UVA)
EV = 351
Exp EV = sum [(P(i) * EV(i)], i= 1, 51
(P(i) = probability of winning state, EV(i) =  State Electoral vote
Margin of Error (MoE) = 2.5%

 
 

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Why isn’t congress asking these intelligence officers to testify?

Richard Charnin
July 24,2017

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

Forensic studies of “Russian hacking” into Democratic National Committee computers last year reveal that on July 5, 2016, data was leaked (not hacked) by a person with physical access to DNC computers, and then doctored to incriminate Russia.

After examining metadata from the “Guccifer 2.0” July 5, 2016 intrusion into the DNC server, independent cyber investigators have concluded that an insider copied DNC data onto an external storage device, and that “telltale signs” implicating Russia were then inserted”.

https://consortiumnews.com/2017/07/24/intel-vets-challenge-russia-hack-evidence/

FOR THE STEERING GROUP, VETERAN INTELLIGENCE PROFESSIONALS FOR SANITY

1 William Binney, former NSA Technical Director for World Geopolitical & Military Analysis; Co-founder of NSA’s Signals Intelligence Automation Research Center
2 Skip Folden, independent analyst, retired IBM Program Manager for Information Technology US (Associate VIPS)
3 Matthew Hoh, former Capt., USMC, Iraq & Foreign Service Officer, Afghanistan (associate VIPS)
4 Michael S. Kearns, Air Force Intelligence Officer (Ret.), Master SERE Resistance to Interrogation Instructor
5 John Kiriakou, Former CIA Counterterrorism Officer and former Senior Investigator, Senate Foreign Relations Committee
6 Linda Lewis, WMD preparedness policy analyst, USDA (ret.)
7 Lisa Ling, TSgt USAF (ret.) (associate VIPS)
8 Edward Loomis, Jr., former NSA Technical Director for the Office of Signals Processing
9 David MacMichael, National Intelligence Council (ret.)
10 Ray McGovern, former U.S. Army Infantry/Intelligence officer and CIA analyst
11 Elizabeth Murray, former Deputy National Intelligence Officer for Middle East, CIA
12 Coleen Rowley, FBI Special Agent and former Minneapolis Division Legal Counsel (ret.)
13 Cian Westmoreland, former USAF Radio Frequency Transmission Systems Technician and Unmanned Aircraft Systems whistleblower (Associate VIPS)
14 Kirk Wiebe, former Senior Analyst, SIGINT Automation Research Center, NSA
15 Sarah G. Wilton, Intelligence Officer, DIA (ret.); Commander, US Naval Reserve (ret.)
16 Ann Wright, U.S. Army Reserve Colonel (ret) and former U.S. Diplomat

 

 
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Posted by on July 24, 2017 in Uncategorized

 

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HOLISTIC DOCTORS DEATH PROBABILITIES AND MEDIA SHILLS

Richard Charnin
July 17, 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

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.
Assume
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)

https://www.healthnutnews.com/recap-on-my-unintended-series-the-holistic-doctor-deaths/
https://richardcharnin.wordpress.com/2015/05/24/a-probability-analysis-of-the-mysterious-deaths-of-125-scientists-and-75-bankers/
http://www.snopes.com/holistic-doctor-death-conspiracy/
http://reason.com/blog/2015/09/13/the-feds-murdering-alternative-doctors

 
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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
7/12/2017

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

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

Conspiracy Theories and Mathematical probabilities
https://richardcharnin.wordpress.com/2012/05/26/conspiracy-theories-and-mathematical-probabilities/

Unnatural Deaths of at least 78 JFK-related witnesses from 1963-1978.
https://richardcharnin.wordpress.com/2013/05/15/jfk-witness-deaths-calculating-the-probabilities/

Seth Rich and 8 other DNC-related suspicious deaths
https://richardcharnin.wordpress.com/2017/05/20/quick-mortality-probability-calculator/

Election Fraud: True Vote vs. Recorded
https://richardcharnin.wordpress.com/2012/06/25/election-fraud-an-introduction-to-exit-poll-probability-analysis/

Suspicious Deaths of 75 Bankers and 125 Scientists
Suspicious Deaths of 11 Holistic Doctors in 3 months
https://richardcharnin.wordpress.com/2015/05/24/a-probability-analysis-of-the-mysterious-deaths-of-125-scientists-and-75-bankers/
https://docs.google.com/spreadsheets/d/1VdwJE_g5z3St3h2NbbXpau0DH7-g1y_98IKXRrt_9ao/edit#gid=496654748

Suspicious Deaths of 16 Microbiologists in 4 months
http://www.rigorousintuition.ca/board2/viewtopic.php?f=18&t=34755#p462796
http://911research.wtc7.net/post911/attacks/killings.html

10 Terrorist Attacks and Concurrent Drills
https://richardcharnin.wordpress.com/2015/12/13/14991/

Cancer Deaths of 7 Latin American Leaders
https://richardcharnin.wordpress.com/2013/03/14/latin-american-leaders-and-cancer-a-probability-analysis/

Mystery Deaths for various group size assumptions https://docs.google.com/spreadsheets/d/1VdwJE_g5z3St3h2NbbXpau0DH7-g1y_98IKXRrt_9ao/edit#gid=252025167

No automatic alt text available.

 
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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
LINKS TO  POSTS

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

TRUE VOTE
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:
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1768941212

 
 

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Richard Charnin's Blog

JFK Conspiracy and Systemic Election Fraud Analysis