Richard Charnin's Blog

2016 True Vote Model- California

Richard Charnin
Aug. 22, 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
Reclaiming Science: The JFK Conspiracy
LINKS TO POSTS
Last 3 Elections: Exact Forecast of Electoral Vote

2016 True Vote Model- California

Clinton won the recorded vote: 61.7-31.6% (4.27 million votes). But election fraud was rampant.

Consider that in 2012, Obama beat Romney by 60.2-37.1% (3.0 million votes) in California. Do you believe that Clinton beat Obama’s margin by 1.2 million? If you do, there’s a bridge in Brooklyn you may be interested in.

1) To match the recorded vote, Clinton needed to win new voters by 90-2%.
2) Nearly 450,000 angry Sanders voters did not turn out for Hillary.

Clinton won the True Vote by 55.0-37.8% (2.44 million votes).
The 1.8 million True Vote discrepancy from the recorded vote comprised nearly 2/3 of her bogus 2.8 million vote national margin.

Media shills insist that Clinton won by 3 million votes. It has become their Mantra, along with the debunked Russian “collusion”. Show them the numbers and tell them: THE RECORDED VOTE IS NEVER EQUAL TO THE TRUE VOTE. THERE IS NO SUCH THING AS A FRAUD-FREE ELECTION.

California True Vote Model
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=633901715

There are eleven counties in California with more registered voters than voting age adults in the county. The counties include San Francisco, Los Angeles and San Diego. Judicial Watch, a conservative watchdog organization, has sent a letter to California Secretary of State Alex Padilla on behalf of the Election Integrity Project, noting that there are 11 counties in the state with more registered voters, and alleging that the state may be out of compliance with Section 8 of the National Voter Registration Act (NVRA).
http://www.thegatewaypundit.com/2017/08/10-11-california-counties-registered-voters-voting-age-adults-democrat/

Leave a comment

Posted by Richard Charnin on August 22, 2017 in 2016 election

Tags: Bernie Sanders, California 2016 True Vote Model, election fraud, Hillary Clinton, Open Source

The 2016 True Vote Model (TVM)

20 Aug

Richard Charnin
Aug. 20, 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

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 to match the recorded vote and assume zero fraud.

In the 2016 True Vote Model, returning 2012 election voter turnout is estimated. Vote shares required to match the recorded vote are calculated. The True Vote is estimated by adjusting 2012 voter turnout. Recorded vote shares are unchanged.

2016 STATE TRUE VOTE MODEL: MICHIGAN
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1824904286

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: 89% turnout of Obama, 95% turnout of Romney voters.
Trump wins by 48.7-45.7% (142,000 votes)
Assumption: Approximately 147,000 of Sanders MI primary voters who voted for Obama did not return to vote in the presidential election.

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 24 of 25 scenarios.

Worst case: Clinton wins by 47.4-46.9% (22,000 votes)
Base case: Trump wins by 48.7-45.7% (142,000 votes)
Best case: Trump wins by 50.4-44.0% (307,000 votes)

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

Leave a comment

Posted by Richard Charnin on August 20, 2017 in 2016 election, True Vote Models

Tags: 2016 True Vote Model, returning voters

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

05 Aug

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.

1 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%.

1 Comment

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

Tags: 2016 pre-election polls, Gallup voter affiliation survey, richard charnin, true vote model

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

02 Aug

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%

Leave a comment

Posted by Richard Charnin on August 2, 2017 in 2016 election, Uncategorized

Tags: 2016 pre-election polls, 2016 True Vote Model, richard charnin, Trump won the true vote

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

24 Jul

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

1 Comment

Posted by Richard Charnin on July 24, 2017 in Uncategorized

Tags: Democratic National Committee, Guccifer 2.0, Russian hacking, VETERAN INTELLIGENCE PROFESSIONALS FOR SANITY

HOLISTIC DOCTORS DEATH PROBABILITIES AND MEDIA SHILLS

17 Jul

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

Leave a comment

Posted by Richard Charnin on July 17, 2017 in Media, Uncategorized

Tags: expected holistic deaths, holistic deaths, poisson probability, snopes, weighted unnatural mortality rates