RSS

Tag Archives: probability calculations

JFK Probability Analysis: Suspicious Deaths of Dealey Plaza Witnesses

JFK Probability Analysis: Suspicious Deaths of Dealey Plaza Witnesses

Richard Charnin
June 4, 2014
Updated: Oct. 30, 2017

Click Reclaiming Science:The JFK Conspiracy to look inside the book.

JFK Blog Posts
JFK Calc Spreadsheet Database
Tables and Graphs

The 1977 House Select Committee on Assassination (HSCA) claimed that the London Sunday Times actuary’s 1 in 100,000 trillion probability calculation that 18 material witnesses would die (13 unnaturally) in the three years following the assassination was invalid. The HSCA claimed that the witness universe was unknown. But the HSCA did not consider Dealey Plaza witnesses or other knowable witness groups (Warren Commission, Garrison/Shaw trial, Church Senate hearings – and the HSCA itself).

It is an interesting exercise to calculate the probabilities of suspicious deaths of 28 Dealey Plaza witnesses. Of the suspicious deaths, 14 were officially ruled unnatural (5 homicides, 7 accidents, 2 suicides).

Assuming there were 600 Dealey Plaza witnesses, the probability of 14 ruled unnatural deaths during the period 1963-1978 is 1 in 230 million. But the nine accidents and suicides were likely homicides.

The probability of 14 homicides for 400, 600 and 1000 witnesses:
400: 1 in 900 trillion
600: 1 in 4 trillion
1000: 1 in 5 billion

Sixteen Dealey Plaza witnesses testified at the Warren Commission, 3 were sought to testify at the Garrison trial, 3 at the Church Senate hearings and 3 at the House Select Committee on Assassinations (HSCA).

The probabilities of JFK witness deaths for various groups have been previously posted: Warren Commission, London Times actuary,Garrison/ Shaw, Church, HSCA, Simkin Educational Forum, JFK-related 1400+ witness reference “Who’s Who in the JFK Assassination”.

1 6311 Lee Harvey Oswald
2 6311 J.D. Tippit
3 6512 William Whaley
4 6606 Frank Martin
5 6608 Lee Bowers
6 6611 James Worrell
7 6701 Jack Ruby
8 6901 Charles Mentesana
9 6901 Buddy Walthers
10 7001 Merriman Smith
11 7008 Bill Decker
12 7101 Mac Wallace
13 7109 Roscoe White
14 7109 Cliff Carter
15 7309 Thomas E. Davis
16 7402 J.A. Milteer
17 7501 Allen Sweatt
18 7502 Ira (Jack) Beers
19 7505 Roger Craig
20 7509 Earl Cabell
21 7604 James Chaney
22 7608 Johnny Roselli
23 7703 Charles Nicoletti
24 7707 Ken O’Donnell
25 7801 Clint “Lummie” Lewis
26 7805 David Morales
27 7901 Billy Lovelady
28 8403 Roy Kellerman

Dealey Plaza witness deaths: https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdDFSU3NVd29xWWNyekd2X1ZJYllKTnc#gid=79

Quick JFK Witness death Calculator:
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdDFSU3NVd29xWWNyekd2X1ZJYllKTnc#gid=78

Advertisements
 
Leave a comment

Posted by on June 4, 2014 in Uncategorized

 

Tags: , , , , , , , , , , , ,

The Election Fraud Quiz II

The Election Fraud Quiz II

Richard Charnin
Sept. 23, 2013

1 The exit poll margin of error is not a function of
a) sample-size, b) 2-party poll share, c) national population size

2 In the 1988-2008 presidential elections, the Democrats won the recorded vote 48-46%. They won both the average unadjusted state and national exit polls by
a) 50-46%, b) 51-45%, c) 52-41%

3 In 2004 the percentage of living Bush 2000 voters required to match the recorded vote was
a) 96%, b) 98%, c) 110%

4 In 2000 the approximate number of uncounted votes was
a) 2, b) 4, c) 6 million

5 In 2008, Obama won by 52.9-45.6%. He led the unadjusted National Exit Poll (17,836 respondents) by
a) 53-45%, b) 58-40%, c) 61-37%

6 In 1988 Bush beat Dukakis by 7 million votes (53.4-45.6%). Dukakis won the National Exit Poll by
a) 49.9-49.1%, b) 50.7-48.3%, c) 51.0-48.0%

7 In 1988 the approximate number of uncounted votes was
a) 6, b) 9, c) 11 million

8 Of 274 state exit polls from 1988-2008, 135 exceeded the margin of error (14 expected). How many moved in favor of the GOP?
a) 85, b) 105, c) 131

9 Gore won the popular vote in 2000. In 2004, returning Nader voters were 5-1 for Kerry, new voters 3-2 for Kerry. In order for Bush to win, he must have won
a) 30% of returning Gore voters, b) 90% of returning Bush voters, c) both (a) and (b).

10 In 2008 Obama won 58% of the state exit poll aggregate. Given it was his True Vote, he had how many Electoral Votes?
a) 365, b) 395, c) 420

11 What is the probability that 131 of 274 state exit polls from 1988-2008 would red-shift to the GOP beyond the margin of error?
a) 1 in 1 million, b) 1 in 1 trillion, c) 1 in 1 trillion trillion trillion trillion trillion trillion trillion trillion trillion (E-116)

12 In 2000 12 states flipped from Gore in the exit polls to Bush in the recorded vote. Gore would have won the election if he had won
a) 1, b) 2, c) 3 of the 12 states

13 In 1988 24 states had exit polls (2/3 of the total recorded vote). Dukakis won the state polls by
a) 50-49%, b) 51-48%, c) 52-47%

14 Exit polls are always adjusted to conform to the recorded vote. It is standard operating procedure and
a) reported by the corporate media, b) noted by academia, c) statistical proof of election fraud

15 Bush had 50.5 million votes in 2000. Approximately 2.5 million died and 1 million did not return to vote in 2004. Therefore, there could not have been more than 47 million returning Bush 2000 voters. But the 2004 National Exit Poll indicated 52.6 million returning Bush voters. This is proof that
a) Bush stole the 2004 election, b) it was a clerical error, c) 6 million Bush votes were not recorded in 2000.

16 In 2000 Gore won the popular vote by 540,000 votes (48.4-47.9%). He won the unadjusted state exit poll aggregate by 50.8-44.4% and the unadjusted National Exit Poll by 48.5-46.3%, indicating that
a) the state exit poll aggregate was outside the margin of error, b) the National poll was within the margin of error, c) the election was stolen, d) all

17 Corporate media websites show that Bush won the 2004 National Exit Poll (13660 respondents) by 51-48%, matching the recorded vote. But the unadjusted National Exit Poll indicates that Kerry won by 51.0-47.6% (7064-6414 respondents). The discrepancy is proof that
a) the poll was adjusted to match the recorded vote, b) Bush stole the election, c) both, d) neither

18 The pervasive difference between the exit polls and the recorded vote in every election is due to
a) inexperienced pollsters, b) Republican reluctance to be polled, c) systemic election fraud

19 In 1992 Clinton defeated Bush by 43-37.5% (Perot had 19.5%). Clinton won the unadjusted National exit poll by 48-32-20%. Bush needed 119% turnout of returning 1988 Bush voters to match the recorded vote. These anomalies were due to
a) bad polling, b) Bush voters refused to be polled, c) Bush tried but failed to steal the election.

20 Sensitivity analysis is a useful tool for gauging the effects of
a) various turnout assumptions, b) various vote share assumptions, c) both, d) neither

21 Monte Carlo simulation is a useful tool for
a) predicting the recorded vote, b) electoral vote, c) probability of winning the electoral vote.

22 The expected electoral vote is based on
a) state win probabilities, b) state electoral votes, c) both, d) neither

23 To match the recorded vote, which exit poll crosstab weights and shares are adjusted?
a) when decided, b) how voted in prior election, c) party ID, d) gender, e) education, f) income, g) all

24 In 2004 Bush’s final pre-election approval rating was 48%, but it was 53% in the adjusted National Exit Poll. The discrepancy was due to
a) late change in approval, b) different polls, c) forcing the exit poll to match the recorded vote

25 The True Vote Model is designed to calculate the fraud-free vote. The TVM utilizes final exit poll shares but estimates returning voters based on the prior election
a) recorded vote, b) votes cast, c) unadjusted exit poll, d) true vote, e) all

https://richardcharnin.wordpress.com/category/true-vote-models/

1c 2c 3c 4c 5c 6a 7c 8c 9c 10c 11c 12a 13c 14c 15a 16c 17c 18c 19c 20c 21c 22c 23g 24c 25e

 

Tags: , , , , , , , , , , , , , , , ,

Debunking a JFK Mysterious Witness Death Lone Nutter

Debunking a JFK Mysterious Witness Death Lone Nutter

Richard Charnin
Aug. 6, 2013
Updated: March 8, 2014

Click Reclaiming Science:The JFK Conspiracy to look inside the book.

JFK Blog Posts
JFK Calc Spreadsheet Database

This post will debunk the following article on JFK witness deaths: http://www.vectorsite.net/twjfk_32.html

The author writes:
As something of a footnote, conspiracists have long played up the number of “mysterious deaths” associated with the JFK assassination, though an inspection of the list makes it seem substantially less mysterious. In any case, the bottom line of a half-century’s investigation of the assassination is that we are left with the conclusion that was apparent from the start: JFK was killed by a lone assassin named Lee Harvey Oswald.

One of the most preposterous claims of the conspiracists is that there have been large numbers of “mysterious deaths” of witnesses relevant to the JFK assassination. Jim Marrs, in his 1993 book (sic 1989) CROSSFIRE, cited 103 “mysterious deaths” up to 1984.

Conspiracists assert that odds of such groupings of deaths are so low that it is impossible to believe they couldn’t have been part of a plan, in particular citing a 1967 LONDON SUNDAY TIMES article with an early “mysterious deaths” list accompanied by a claim that an actuary had calculated the odds to be “a hundred thousand trillion to one”.

The HSCA found this citation of odds a little hard to believe. The list of people who could be connected to the JFK assassination was long, easily running to thousands, and the idea that a portion of them might have died over some period of years hardly seemed that improbable. The HSCA contacted the TIMES and got back a sheepish answer. It turned out that the question the paper had asked of an actuary was effectively:

Name 15 specific adults selected at random from the population of the USA. What are the odds that all 15 of these named people will be dead within a few years?

The odds are not at all good. Assume that adults in a population have, on the average, a 1 in 10 probability of dying in some given number of years. If 15 adults are selected at random from that population, the odds of all 15 dying to that time would be 1 in 10^15, a thousand trillion to one.

However, anybody with even a simple understanding of probability would know that was asking the wrong question. The right question was obviously: Given a group of several thousand people, what are the odds that at least 15 unspecified members of that group will be dead in a few years?

The answer was that one could bet on it and easily win. Given 1 in 10 odds of an adult in a population dying in some given number of years, we would expect in that time that, duh, roughly a tenth of the population would be dead. The TIMES apologized to the HSCA for the blunder.

——————————————————

The article is a total fiasco

The Lone Nutter’s ignorance and naivete is confirmed by this utterly false statement: “However, anybody with even a simple understanding of probability would know that was asking the wrong question. The right question was obviously: Given a group of several thousand people, what are the odds that at least 15 unspecified members of that group will be dead in a few years? The answer was that one could bet on it and easily win. Given 1 in 10 odds of an adult in a population dying in some given number of years, we would expect in that time that, duh, roughly a tenth of the population would be dead.”

The author calls legitimate seekers of the truth “conspiracists”. But he does not understand the problem, much less the math. This is the correct definition: Given a group of N people, what is the probability that at least n members of the group will die unnaturally (homicide, accident or suicide) within T years?

He just repeats the usual Warren Commission apologist talking points. The mathematical proof of a conspiracy relegates his screed as pure propaganda. Lone Nutters are shameless and have no regard for the truth.

The author’s lack of specificity is the “tell”. He fails to consider any of the following critical factors: the total number of material witnesses in the group, the number and cause of unnatural deaths, the time interval, unnatural mortality rates. All are necessary input for the probability calculation.

This LN does not even qualify as an amateur. But that’s understandable. After all, he’s a Lone Nutter, who by definition is incapable of rational analysis.

His logical errors and omission of key factors in his “analysis” include:
1) not assuming a specific number of witnesses in the target group,
2) invention of a 1 in 10 probability of dying,
3) not assuming a definitive time period,
4) failure to consider unnatural deaths and related mortality stats,
5) use of a pathetically, unscientific probability calculation,
6) naively states that “roughly a tenth of the population would be dead”.
7) failure to refute the relevance of 100 “convenient” deaths
8) failure to consider the more than 60 deaths of witnesses sought to testify.
9) failure to correctly calculate the expected number of unnatural deaths:
E=N*T*R, where N =total witnesses in the group, T= time period, R= weighted average mortality rate.
10) failure to consider the “paradigm shift”: why the witnesses died is a non-factor. The only relevant factors are how many died unnaturally, the time interval and the universe of material witnesses or the number of witnesses called to testify.

There are 120 dead material witnesses in the JFK Calc spreadsheet database based on a 1400+ total universe. Of the 120, 63 were sought to testify at the Warren Commission, Garrison trial, Church Senate and HSCA.

The author does not consider that the number of UNNATURAL deaths among the 1400+ witnesses is the key factor – not total deaths. There was a STATISTICALLY IMPOSSIBLE 77 OFFICIALLY RULED UNNATURAL DEATHS (34 homicides, 24 accidents, 16 suicides, 3 unknown).

In fact, 25 of the 40 accidents and suicides were actually HOMICIDES – based on the STATISTICAL EXPECTATION of 12 accidents and 3 suicides – so we are up to 59 homicides among the 77 unnatural deaths. But by the same reasoning, there was a statistically impossible number of “natural deaths”: HEART ATTACKS and SUDDEN CANCERS. Therefore, the 34 OFFICIAL homicides UNDERSTATES the true number (estimated as 90+) based on STATISTICAL EXPECTATION.

These graphs are mathematical proof of a conspiracy.

The Paradigm Shift
But his most fundamental flaw was focusing on the relevance of individual witnesses in Marrs’ list without considering the paradigm shift: WHY the witnesses died is IRRELEVANT.

The relevant factors are how many witnesses were called to testify, how many died, their cause of death and the time period. In fact, from 1964-78, approximately 1100 witnesses were called to testify in four investigations. At least 63 died (38 unnaturally, including 27 homicides).

The author claims there were thousands of witnesses. In fact, the FBI claimed to have interviewed 25,000 (only about 1400 were material). But let’s assume there were 25,000.

There were at least 25 homicides of material witnesses from 1964-66. The probability of at least 25 homicides among the 25,000 is 1 in 38 BILLION (2.6E-11). The average homicide rate for 1964-66 was 0.000061.

There were at least 83 homicides from 1964-78. The probability of 83 homicides among the 25,000 is 1 in 43 TRILLION (2.32E-14). Only 32 homicides would normally be expected. The average homicide rate for 1964-78 was 0.000084.

The data and probabilities are displayed in JFK Calc: A Spreadsheet/Database of Mysterious Witness Deaths.

Statistically expected number of unnatural deaths
Expected unnatural deaths: E = N*T*R, where
N = 1400 material witnesses
T = 15 years (1964-78)
R = .000818 average unweighted unnatural mortality rate

JFK Material witnesses unnatural mortality
Among 1400 material witnesses from 1964-78, 77 deaths were officially ruled as unnatural – but only 17 were statistically expected: 34 homicides (2 expected); 24 accidents (12 expected); 16 suicides (3 expected) and 3 unknown (0.2 expected). There were 40 deaths officially ruled as accidental or suicide – but only 15 were expected. Therefore it is likely that approximately 25 (40-15) accidents and suicides were actually homicides.

Expected vs. Official Unnatural Death (1964-78)

Cause Expected Official Mortality Rate
homicide 1.76 34 0.000084 44%
suicide. 2.91 16 0.000130 21%
accident 12.47 24 0.000594 31%
unknown. 0.21 3 0.000010 4%
Total 17.35 77 0.000818 100%

Warren Commission
According to the CIA, N= 418 witnesses testified, but the total was 552 including affidavits and depositions.

There were at least n= 18 unnatural deaths over T= 15 years (1964-78). The probability of at least 10 unnatural deaths among the witnesses in 3 years is:
P = 1 – poisson(9, 1.06, true) = 1.81E-07 (1 in 5,509,693)
(based on the 0.000842 national unnatural rate)
P = 1 – poisson(9, 0.31, true) = 1.53E-12 (1 in 652,270,204,558)
(based on the 0.000245 JFK witness-weighted unnatural rate)

The London Sunday Times Actuary
The actuary’s 100,000 trillion to one odds of 18 material witness deaths in three years (13 were unnatural) is matched by assuming 459 witnesses and the 0.000207 weighted unnatural mortality rate. Only one unnatural death would normally be expected among 459 witnesses in the three year period.
The probability is:
P= POISSON (13, 0.29, false) = 9.96E-18 (1 in 100,000 TRILLION)

This is a sensitivity analysis of unnatural witness deaths.

Convenient deaths spiked in 1964 (Warren Commission) and 1977 (House Select Committee).

 
23 Comments

Posted by on August 6, 2013 in JFK

 

Tags: , , , , , , , , , ,

Latin American Leaders and Cancer: A Probability Analysis

Latin American Leaders and Cancer: A Probability Analysis

Richard Charnin

Mar. 14, 2013

Hugo Chavez was one of seven (six leftist) Latin-American leaders recently diagnosed with cancer. Columbia’s conservative President Juan Manuel Santos was struck with prostate cancer after beginning peace talks with left wing FARC. The six leftists: Brazilian President Dilma Rousseff, Paraguay’s Fernando Lugo, former Brazilian leader Luiz Inácio Lula da Silva, Argentina’s former President Nestor Kirchner. Argentina’s current President Cristina Fernández de Kirchner was diagnosed with thyroid cancer in December 2012, although later analysis proved she had never actually suffered from the illness. In 2006, it was reported that retired Cuban leader Fidel Castro was also diagnosed with cancer, so there were at least EIGHT in total.

To estimate the probability that a given number of n individuals in a group of size N would be diagnosed with cancer, the following information is required:
1) Average age of the group and associated 10 year cancer rate
2) Size of the group (N)
3) Number (n) diagnosed with cancer

The calculations are estimates based on the BINOMIAL DISTRIBUTION.
P (at least n) = 1- binomdist (n-1, N, rate, 1)

The following spreadsheet contains two probability tables:
1 – For a Given Average Age: Group size vs. number diagnosed with cancer
2 – For a Given Group Size: Average age vs. number diagnosed with cancer

For the following probabilities, the assumed average age of Latin American leaders is 60 (10.13% cancer rate).
Note that Castro was diagnosed in 2006.

-The probability is 0.26% (1 in 389) that at least SEVEN of ALL 20 Latin American leaders would be diagnosed with cancer. Including Castro, the probability is 0.05% (1 in 2203) that 8 would be diagnosed.

-Assuming 10 leftist leaders, the probability that AT LEAST 5 would be diagnosed with cancer is approximately 0.17% (1 in 577). The probability that AT LEAST 6 would be diagnosed is approximately 0.02% (1 in 6330).

-Assuming 14 leftist leaders, the probability that AT LEAST 5 would be diagnosed with cancer is approximately 0.97% (1 in 103). The probability that AT LEAST 6 would be diagnosed is approximately 0.16% (1 in 634).

-Assuming 18 leftist leaders, the probability that AT LEAST 5 would be diagnosed with cancer is approximately 2.96% (1 in 34). The probability that AT LEAST 6 would be diagnosed is approximately 0.68% (1 in 146).

Data Source: National Cancer Institute (SEER)

 
Leave a comment

Posted by on March 14, 2013 in Uncategorized

 

Tags: , , , ,

Historical Overview and Analysis of Election Fraud

Richard Charnin
Jan.31, 2013
Updated: Jan. 22, 2017

Historical Overview and Analysis of Election Fraud

In the 1968-2012 Presidential elections, the Republicans won the average recorded vote by 48.7-45.8%. The 1968-2012 National True Vote Model (TVM) indicates the Democrats won the True Vote by 49.6-45.0% – a 7.5% margin discrepancy.

In the 1988-2008 elections, the Democrats won the unadjusted state exit poll aggregate by 52-42%, yet won the recorded vote by just 48-46%, an 8% margin discrepancy. 

Probabilities of the state and national exit poll discrepancies 

The state exit poll margin of error was exceeded in 135 of 274 state presidential elections from 1988-2008. The probability of the occurrence is ZERO. Only 14 (5%) would be expected to exceed the MoE at the 95% confidence level. Of the 135 which exceeded the MoE, 131 red-shifted to the Republican. The probability P of that anomaly is ABSOLUTE ZERO (E-116). That is scientific notation for

P= .000000000 000000000 000000000 000000000 000000000 000000000 000000000 000000000 000000000 000000000 000000000 0000001.

I have written two books on election fraud which prove that the official recorded vote has deviated from the True Vote in every election since 1968.  Except for 2016,  the deviations have  always favored the Republicans. Voting machine “glitches” are not due to machine failures; they are caused by malicious programming.

The proof is in the 1988-2008 Unadjusted State Exit Polls Statistical Reference. Not one political scientist, pollster, statistician, mathematician or media pundit has ever rebutted the data or the calculation itself. They have chosen not to discuss the topic. And who can blame them? Job security is everything.

Election forecasters, academics, political scientists and main stream media pundits never discuss or analyze the statistical evidence that proves election fraud is systemic – beyond a reasonable doubt. This site contains a compilation of presidential, congressional and senate election analyses based on pre-election polls, unadjusted exit polls and associated True Vote Models. Those who never discuss or analyze Election Fraud should focus on the factual statistical data and run the models. If anyone wants to refute the analytic evidence, they are encouraged to do so in a response. Election forecasters, academics and political scientists are welcome to peer review the content.

The bedrock of the evidence derives from this undisputed fact: National and state actual exit poll results are always adjusted in order to force a match to the recorded vote – even if doing so requires an impossible turnout of prior election voters and implausible vote shares.

All demographic categories are adjusted to conform to the recorded vote. To use these forced final exit polls as the basis for election research is unscientific and irresponsible. The research is based on the bogus premise that the recorded vote is sacrosanct and represents how people actually voted. Nothing can be further from the truth.

It is often stated that exit polls were very accurate in elections prior to 2004 but have deviated sharply from the recorded vote since. That is a misconception. UNADJUSTED exit polls have ALWAYS been accurate; they closely matched the True Vote Model in the 1988-2008 presidential elections. The adjusted, published exit polls have always matched the fraudulent RECORDED vote because they have been forced to. That’s why they APPEAR to have been accurate.

The Census Bureau indicates that since 1968 approximately 80 million more votes were cast than recorded. And these were just the uncounted votes. What about the votes switched on unverifiable voting machines and central tabulators? But vote miscounts are only part of the story. The True Vote analysis does not include the millions of potential voters who were illegally disenfranchised and never got to vote.

In 1988, Bush defeated Dukakis by 7 million recorded votes. But approximately 11 million ballots (75% Democratic) were uncounted. Dukakis won the unadjusted exit polls in 24 battleground states by 51-47% and the unadjusted National Exit Poll by 50-49%. The Collier brothers classic book Votescam provided evidence that the voting machines were rigged for Bush.

In 1992, Clinton defeated Bush by 5.8 million recorded votes (43.0-37.5%). Approximately 9 million were uncounted. The National Exit Poll was forced to match the recorded vote with an impossible 119% turnout of living 1988 Bush voters in 1992. The unadjusted state exit polls had Clinton winning a 16 million vote landslide (47.6-31.7%). The True Vote Model indicates that he won by 51-30% with 19% voting for third party candidate Ross Perot.

In 1996, Clinton defeated Dole by 8.6 million recorded votes (49.3-40.7%); 9 million were uncounted. The unadjusted state exit polls (70,000 respondents) had Clinton winning a 16 million vote landslide (52.6-37.1%). The True Vote Model indicates that he had 53.6%.

In 2000, Al Gore won by 540,000 recorded votes (48.4-47.9%). But the unadjusted state exit polls (58,000 respondents) indicated that he won by 50.8-44.4%, a 6 million vote margin. There were nearly 6 million uncounted votes. The True Vote Model had Gore by 51.5-44.7%. The Supreme Court awarded the election to Bush (271-267 EV). In Florida, 185,000 ballots were uncounted. Twelve states flipped from Gore in the exit poll to Bush in the recorded vote: AL AR AZ CO FL GA MO NC NV TN TX VA. Gore would have won the election if he captured just one of the states. Democracy died in 2000.

In July 2004 I began posting weekly Election Model projections based on the state and national polls. The model was the first to use Monte Carlo Simulation and sensitivity analysis to calculate the probability of winning the electoral vote. The final projection had Kerry winning 337 electoral votes with 51.8% of the two-party vote, closely matching the unadjusted exit polls.

The adjusted 2004 National Exit Poll was mathematically impossible; it was forced to match Kerry’s 48.3% recorded vote (the unadjusted NEP indicated that Kerry had 51.7%). The adjusted poll indicated that there were 52.6 million returning Bush 2000 voters (43% of the 122.3 million recorded). But Bush had just 50.5 million votes in 2000; only 48 million were alive in 2004. Assuming a 96% turnout, 46 million voted. Therefore, simple arithmetic shows that the adjusted NEP overstated the number of returning Bush voters by 6.6 (52.6-46) million. In order to match the recorded vote, there had to be an impossible 110% turnout of living Bush 2000 voters.

THE ULTIMATE PROOF THAT THE ELECTION WAS STOLEN IS CONFIRMED BY A) KERRY’S 4 MILLION NEW VOTER MARGIN (22 MILLION NEW VOTERS, NEARLY 60% FOR KERRY), B) 4 MILLION RETURNING GORE MARGIN AND C) 2 MILLION RETURNING NADER MARGIN. KERRY WON BY 10 MILLION VOTES.

The post-election True Vote Model calculated a feasible turnout of living 2000 voters based on Census total votes cast (recorded plus net uncounted), a 1.25% annual mortality rate and 98% Gore/Bush voter turnout. It determined that Kerry won by 67-57 million and had 379 EV. Kerry’s unadjusted state exit poll aggregate 51.0% share was close to his 51.7% unadjusted National Exit Poll share. He had 53.5% in the True Vote Model. There was further confirmation of a Kerry landslide.

Consider the adjustments made to the 2004 National Exit Poll crosstabs to force a match to the recorded vote.

Bush had a 48% national approval rating in the final 11 pre-election polls. The Final adjusted National Exit Poll was forced to indicate that he had a 53% approval rating. He had just a 50% rating in the unadjusted state exit poll weighted aggregate. Given the 3% differential, we can assume that the 48% pre-election approval rating was also inflated by 3% and was really 45% – a virtual match to the True Vote Model. The exit pollsters had to inflate Bush’s 48% pre-election average rating by 5% in the NEP in order to match the recorded vote. There was a 0.99 correlation ratio between Bush‘s state approval and his unadjusted exit poll share.

Similarly, the unadjusted state exit poll Democratic/Republican Party ID split was 38.8-35.1%. In order to force the National Exit Poll to match the recorded vote, it required a bogus 37-37% split. The correlation between state Republican Party ID and the Bush unadjusted shares was a near-perfect 0.93. This chart displays the state unadjusted Bush exit poll share, approval ratings and Party-ID.

The Final 2006 National Exit Poll indicated that the Democrats had a 52-46% vote share. The Generic Poll Trend Forecasting Model projected that the Democrats would capture 56.43% of the vote. It was within 0.06% of the unadjusted exit poll.

In the 2008 Primaries, Obama did significantly better than his recorded vote.

The 2008 Election Model projection exactly matched Obama’s 365 electoral votes and was within 0.2% of his 52.9% share (a 9.5 million margin). But the model understated his True Vote. The forecast was based on final likely voter (LV) polls that had Obama leading by 7%. The registered voter (RV) polls had him up by 13% – before undecided voter allocation. The landslide was denied.

The Final 2008 National Exit Poll was forced to match the recorded vote by indicating an impossible 103% turnout of living Bush 2004 voters and 12 million more returning Bush than Kerry voters. Given Kerry’s 5% unadjusted 2004 exit poll and 8% True Vote margin, one would expect 7 million more returning Kerry than Bush voters – a 19 million discrepancy from the Final 2008 NEP. Another anomaly: The Final 2008 NEP indicated there were 5 million returning third party voters – but only 1.2 million were recorded in 2004. Either the 2008 NEP or the 2004 recorded third-party vote share (or both) was wrong. The True Vote Model determined that Obama won by over 22 million votes with 420 EV. His 58% share was within 0.1% of the unadjusted state exit poll aggregate (83,000 respondents).

In the 2010 Midterms the statistical evidence indicates that many elections for House, Senate, and Governor, were stolen. The Wisconsin True Vote Model contains worksheets for Supreme Court and Recall elections. A serious analyst can run them and see why it is likely that they were stolen.

The 2012 Presidential True Vote and Election Fraud Simulation Model exactly forecast Obama’s 332 electoral vote based on the state pre-election polls.  Obama won the recorded vote by 51.0-47.2% (5.0 million vote margin) and once again overcame the built-in 5% fraud factor. The built-in True Vote Model projected that Obama would win by 56-42% with 391 electoral votes. But just 31 states were exit polled, therefore a comparison between the True Vote Model and the (still unreleased) state and national unadjusted exit polls (i.e. the red-shift) is not possible. Obama won the 11.7 million Late votes recorded after Election Day by 58-38%. In 2008, he won the 10.2 million late votes by 59-37%. The slight 2% margin difference is a powerful indicator that if a full set of 2012 unajusted state and national exit polls were available, they would most likely show that Obama had 55-56% True Vote share.

In 2016,  Clinton won the Recorded vote by 48.3-46.2%. But Trump won the The 2016 Election Model recorded vote forecast by 44.4-42.9% and exactly matched the 306-232 EV. 

Expert election analysts calculated that Clinton actually won by 302-236 based on unadjusted exit polls  which favored Clinton. They focused on four states that Trump won: WI, NC, MI and PA.  The analysts assumed that the exit polls were fairly conducted – just like they had been in the past.  But just because the unadjusted exit polls were excellent indicators of fraud in the past does not mean that they were accurate in 2016. The media was in the tank for Clinton, the establishment candidate. In both the pre-election and exit polls, the Democratic Party-ID affiliation and corresponding vote share was inflated at the expense of Independents. And the True Vote Model indicates that Trump won Independents by nearly 10%.

The unadjusted polls were the impetus for recounting MI, WI and PA. But why recount only states that Trump narrowly won? What about the states that he narrowly lost: NV, MN, NM, CO, NH

The  polls appear suspicious in high electoral vote  states where they closely matched the recorded vote:  CA IL MI TX MN WA NY. Clinton’s CA margin exceeded Obama’s in 2012 by an implausible 7%. An unknown number of illegals were encouraged to vote by Obama.

 https://richardcharnin.wordpress.com/2016/12/01/the-2016-presidential-recounts-why-not-add-these-six-states/

TRACK RECORD
Election Model Forecast; Post-election True Vote Model

1988-2008 State and National Presidential True Vote Model

1968-2012 National Presidential True Vote Model

US Count Votes National Election Data Archive Project
Analysis of the 2004 Presidential Election Exit Poll Discrepancies

2004 True Vote Model
Kerry 51.8%, 337 EV (snapshot)
State exit poll aggregate: 51.7%, 337 EV
Recorded Vote: 48.3%, 255 EV
True Vote Model: 53.6%, 364 EV

2008 Election Model
Obama 53.1%, 365.3 EV (simulation mean)
Recorded: 52.9%, 365EV
State exit poll aggregate: 58.0%, 420 EV
True Vote Model: 58.0%, 420 EV

2012 Forecast and True Vote Model
Obama 51.6%, 332 EV (Snapshot)
Recorded : 51.6%, 332 EV
True Vote Model: 55.2%, 380 EV

2016 Election Model Forecast
Recorded Vote: Clinton 48.3-46.2%, Trump 306-232 EV
Recorded Forecast: Trump 44.4-42.9% with 306-232 EV
True Vote: Trump 48.5-44.3% with 351-187 EV

Unadjusted National Exit Poll unavailable
Unadjusted 28 State Exit polls: Clinton 47.9-44.7%

 

Tags: , , , , , , , , , , , , , , ,

Calculating the Projected Electoral Vote

Calculating the Projected Electoral Vote

Oct. 26, 2012

The 2012 Election Forecast Simulation Model calculates the projected electoral vote in three ways.

1. Snapshot EV: The state electoral vote goes to the projected leader based on the pre-election poll. This is a crude estimate in close races in which the projected margin is within 1-3%.

2. Expected EV: The probability of winning each state is calculated using the poll-based projection. The theoretical forecast electoral vote is the weighted sum of the state win probabilities and corresponding electoral votes. EV= ∑ P(i) * EV (i), i =1,51 states. This is the best estimate for the projected Electoral Vote.

3. Simulation Mean EV: The mean electoral vote is a simple average of the simulated trial elections. It calculated mean approaches the theoretical expected EV as the number of trials increase (500 is sufficient), illustrating the Law of Large Numbers. A Monte Carlo simulation is needed to calculate the probability of winning the election. It is simply the number of winning trials divided by 500.

The Final Nov.6 model forecast that Obama would have a 332 Snapshot EV (exactly matching his actual EV), a 320.7 Expected EV and 320.8 Simulation Mean EV. But the Expected EV is a superior forecast tool since it eliminates the need for stating that “the states are too close to call”.

Published 10/27/12:
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Election Model Forecast; Post-election True Vote Model

2004 Election Model (2-party shares)
Kerry 51.8%, 337 EV (snapshot)
State exit poll aggregate: 51.7%, 337 EV
Recorded Vote: 48.3%, 255 EV
True Vote Model: 53.6%, 364 EV

2008 Election Model
Obama 53.1%, 365.3 EV (simulation mean);
Recorded: 52.9%, 365 EV
State exit poll aggregate: 58.0%, 420 EV
True Vote Model: 58.0%, 420 EV

2012 Election Model
Obama Projected: 51.6% (2-party), 332 EV snapshot; 320.7 expected; 321.6 mean
Adjusted National Exit Poll (recorded): 51.0-47.2%, 332 EV
True Vote Model 56.1%, 391 EV (snapshot); 385 EV (expected)
Unadjusted State Exit Polls: not released
Unadjusted National Exit Poll: not released

 
Leave a comment

Posted by on October 27, 2012 in 2012 Election, Uncategorized

 

Tags: , , ,

Final Forecast: 2012 Presidential True Vote/Election Fraud Model

Final Forecast: 2012 Presidential True Vote/Election Fraud Model

Richard Charnin
Nov 5, 2012

The final 2012 Presidential True Vote/Election Fraud Model exactly forecast Obama’s 332 electoral vote. His projected 51.6% two-party recorded share was close to the actual 51.9%. Obama actually did much better in the True Vote Model forecast (391 EV, 56% two-party). As usual, the systematic fraud factor caused the red-shift. But Obama overcame the fraud, just as he did in 2008.

The 2008 Election Model was also right on the money. It forecast that Obama would have a 53.1% recorded share and 365.3 expected EV. He had 52.9% and 365 EV. But he had 58.0% in the True Vote Model and 420 EV. His 58.0% weighted aggregate share of the unadjusted state exit polls (82,000 respondents) confirmed the True Vote Model. He won the unadjusted National Exit Poll (17,836 respondents) by an astounding 61-37%.

The Presidential True Vote and Monte Carlo Simulation Forecast Model is updated on a daily basis. The election is assumed to be held on the latest poll date.

Final Forecast: 11/06/2012 9am
Obama: 320.7 expected electoral votes; 99.6% win probability (498 of 500 trials).
He had a 332 snapshot EV (actual total).
He led the state poll weighted average by 49.3-46.2% (51.6% 2-party share).
He led 50.4-47.0% in 16 of 18 Battleground states with 184 of 205 EV.

Obama led Romney in the RCP National average: 48.8-48.1%.
Rasmussen and Gallup are Likely Voter (LV) polls which lean to the GOP.
Rasmussen: Romney led 49-48%.
Gallup: Romney led 50-49%. It was 51-46% a week ago.

Obama led in the Rand poll 49.5-46.2% (closely matching the state polls). Unlike the national LV polls, the Rand poll doesn’t eliminate respondents but weights them on a scale of 1-10 (based on voter preference and intention to vote).

The 3% Obama margin increase in the Rand poll over the national LV polls illustrates why the LVs understate Obama’s margin by using the Likely Voter Cutoff Model (LVCM). LV polls are a subset of the registered voter (RV) sample. They always understate the Democratic share. The majority of voters eliminated by the Likely Voter Cutoff Model (LVCM) are Democrats.

The True Vote Model indicates that Obama would have 55.2% of the two-party vote with 371 expected EV in a fraud-free election. Will he be able to overcome the systemic fraud factor?

2012 Presidential True Vote and Monte Carlo Simulation Forecast Model (html)
– The Monte Carlo Electoral Vote Simulation is based on the latest state polls and currently assumes an equal split of undecided voters. The expected electoral vote is the sum of the products of the state win probabilities and corresponding electoral votes.

– The True Vote Model is based on plausible turnout estimates of new and returning 2008 voters and corresponding vote shares.

The model calculates an estimated True Vote forecast for the National aggregate or any state. The calculation is displayed below the input data section. State poll-based national vote shares, electoral vote and probabilities are displayed on the right side of the screen.

2008 True Vote 2012 Vote Pct Obama Romney
Obama 76.2 58.0% 72.4 68.8 54.2% 90% 10%
McCain 53.0 40.3% 50.3 47.8 37.7% 7% 93%
Other. 2.20 1.66% 2.10 1.97 1.6% 50% 50%
DNV ...................8.27 6.5% 59% 41%
Total 131.4 100% 124.8 126.8 100% 56.1% 43.9%
..............True Vote........... 71.1 55.7
............. Recorded Vote....... 51.0% 47.2%
............. Projected 2-party... 51.6% 48.4%
............. Electoral Vote
............. Projected Snapshot.. 332 206
............. 500 Simulation Mean. 321 217
............. Expected True EV.... 385 153
............. EV Win Probability.. 99.8%

This worksheet contains the weekly polling trend analysis.

The polling data is from the Real Clear Politics (RCP) and Electoral-vote.com websites. The simulation uses the latest state polls.

View this 500 election trial simulation electoral vote frequency graph.

1988-2008: 274 State exit polls. An 8% Discrepancy

In the six presidential elections from 1988-2008, the Democrats won the average recorded vote by 48-46%. But they led both state and national exit polls by 52-42%. There were approximately 375,000 respondents in the 274 state polls and 90,000 respondents in the six national polls. Overall, an extremely low margin of error.

1988-2008 Unadjusted State and National Exit Poll Database

The Ultimate Smoking Gun that proves Systemic Election Fraud:
1) The Likely Voter Cutoff Model eliminates newly registered Democrats from the LV sub-sample. Kerry had 57-61% of new voters; Obama had 72%.
2) Exit poll precincts are partially selected based on the previous election recorded vote. 
3) In the 1988-2008 presidential elections, 226 of 274 exit polls red-shifted to the Republicans. Only about 137 would normally be expected to red-shift. The probability is zero.
4) 135 of 274 exit polls exceeded the margin of error. Only 14 (5%) would normally be expected. The probability is ZERO.
5) 131 of the 135 exit polls that exceeded the margin of error red-shifted to the Republicans. The probability is ZERO.
 

No exit polls in 19 states

The National Election Pool (NEP) is a consortium of six corporate media giants which funds the pollster Edison Research to do exit polling in the U.S and abroad. The NEP announced that they would not exit poll in 19 states, 16 of which are universally thought of as being solid RED states. Or are they? 

In 2008, Obama won exit polls in AK, AL, AZ, GA, NE, SD. He came close to winning in TX, KY, SC, TN, MS. These former RED states may have turned PURPLE. View this worksheet in the model. 

The bad news is that the NEP decision to eliminate the polls makes it easier for vote margins to be padded and electoral votes flipped. Without the polls, it is much more difficult to calculate the statistical probabilities of fraud based on exit poll discrepancies. In the 1988-2008 elections, the Democrats led the unadjusted state exit polls by 52-42%, but by just 48-46% in the official recorded vote. This is a mathematically impossible result which proves systemic election fraud.

The good news is that the post-election True Vote Model should find implausible discrepancies in the recorded state and national votes. After all, that is what it was designed to do.

Sensitivity Analysis

The pre-election TVM built in the 2012 Election Model uses alternative scenarios of 2008 voter turnout and defection rates to derive a plausible estimate of the total final share. The returning voter assumptions are based on Obama’s 58% True Vote (a plausible estimate) and his 53% recorded share. The latter scenario results in vote shares that are close to the LV polls.

The sensitivity analysis of alternative turnout and vote share scenarios is an important feature in the model. The model displays the effects of effects of incremental changes in turnout rates and shares of returning voters. The tables display nine scenario combinations of a) Obama and McCain turnout rates and b) Obama/Romney shares of returning Obama and McCain voters. Obama’s vote share, winning margin and popular vote win probability are displayed for each scenario.

Registered and Likely Voters

Historically, RV polls have closely matched the unadjusted exit polls after undecided voters are allocated and have been confirmed by the True Vote Model.

Likely Voter (LV) polls are a subset of Registered Voter polls and are excellent predictors of the recorded vote – which always understate the Democratic True Vote. One month prior to the election, the RV polls are replaced by LVs. An artificial “horse race” develops as the polls invariably tighten.

The Likely Voter Cutoff Model (LVCM) understates the voter turnout of millions of new Democrats, thereby increasing the projected Republican share. Democrats always do better in RV polls than in the LVs. Based on the historical record, the Democratic True Vote share is 4-5% higher than the LV polls indicate. The LVs anticipate the inevitable election fraud reduction in Obama’s estimated 55% True Vote share.

Media pundits and pollsters are paid to project the recorded vote – not the True Vote. The closer they are, the better they look. They never mention the fraud factor which gets them there, but they prepare for it by switching to LV polls.

The disinformation loop is closed when the unadjusted, pristine state and national exit polls are adjusted to match the LV recorded vote prediction.

2004 and 2008 Election Models

The 2004 model matched the unadjusted exit polls. Kerry had 51.7% and 337 electoral votes. But the election was stolen. Kerry had 48.3% recorded. View the 2004 Electoral and popular vote trend

The 2008 model exactly matched Obama’s 365 EV. The National model exactly matched his official recorded 52.9% share; the State model projected 53.1%. His official margin was 9.5 million votes.

Obama had 58.0% in the unadjusted, weighted state exit poll aggregate (83,000 respondents) which exactly matched the post-election True Vote Model. Obama’s 23 million True Vote margin was too big to steal.

The National Exit Poll displayed on mainstream media websites (Fox, CNN, ABC, CBS, NYT, etc.) indicates that Obama had 52.9% – his recorded vote. Unadjusted state and national exit polls are always forced to match the recorded share.

But the media never discussed the fact that Obama had 61% in the unadjusted National Exit Poll (17,836 respondents). View the 2008 Electoral and popular vote trend

This graph summarizes the discrepancies between the 1988-2008 State Exit Polls and the corresponding Recorded Votes.

The True Vote Model

The 2008 True Vote Model (TVM) determined that Obama won in a landslide by 58-40.3%. Based on the historical red-shift, he needs at least a 55% True Vote share to overcome the systemic 5% fraud factor. The TVM was confirmed by the unadjusted state exit poll aggregate: Obama had an identical 58-40.5% margin (83,000 respondents). He won unadjusted National Exit Poll (17,836 respondents) by an even bigger 61-37% margin.

In projecting the national and state vote, a 1.25% annual voter mortality rate is assumed. The TVM uses estimated 2008 voter turnout in 2012 and corresponding 2012 vote shares. The rates are applied to each state in order to derive the national aggregate result.

There are two basic options for estimating returning voters. The default option assumes the unadjusted 2008 exit poll as a basis. The second assumes the recorded vote. It is important to note that the True Vote is never the same as the recorded vote. The 1988-2008 True Vote Model utilizes estimates of previous election returning and new voters and and adjusted state and national exit poll vote shares.

Monte Carlo Simulation

The simulation consists of 500 election trials. The electoral vote win probability is the number of winning election trials divided by 500.

There are two forecast options in the model. The default option uses projections based on the latest pre-election state polls. The second is based on the state True Vote. The fraud factor is the difference between the two.

The projected vote share is the sum of the poll and the undecided voter allocation (UVA). The model uses state vote share projections as input to the Normal Distribution function to determine the state win probability.

In each election trial, a random number (RND) between 0 and 1 is generated for each state and compared to Obama’s state win probability. If RND is greater than the win probability, the Republican wins the state. If RND is less than the win probability, Obama wins the state. The winner of the election trial is the candidate who has at least 270 electoral votes. The process is repeated in 500 election trials.

Electoral Votes and Win Probabilities

The Electoral Vote is calculated in three ways.
1. The Snapshot EV is a simple summation of the electoral votes. It could be misleading if close state elections favor one candidate.
2. The Mean EV is the average of the 500 simulated election trials.
3. The Theoretical EV is the product sum of the state electoral votes and corresponding win probabilities. A simulation or meta-analysis is not required to calculate the expected EV.

The Mean EV approaches the Theoretical EV as the number of election trials increase. This is an illustration of the Law of Large Numbers.

Obama’s electoral vote win probability is his winning percentage of 500 simulated election trials.

The national popular vote win probability is calculated using the national aggregate of the the projected vote shares. The national margin of error is 1-2% lower than the MoE of the individual states. That is, if you believe the Law of Large Numbers and convergence to the mean.

The Fraud Factor

The combination of True Vote Model and state poll-based Monte Carlo Simulation enables an analyst to determine if the forecast electoral and popular vote share estimates are plausible. The aggregate state poll shares can be compared to the default TVM.

The TVM can be forced to match the aggregate poll projection by…
– An incremental change in vote shares. A red flag would be raised if the match required that Obama captured 85% of returning Obama voters and Romney had 95% of returning McCain voters (a 10% net defection).

– Adjusting 2008 voter turnout in 2012. For example, if McCain voter turnout is required to be 10-15% higher than Obama’s, that would raise a red flag.

– Setting the returning voter option to the 2008 recorded vote. The implicit assumption is that the 2008 recorded vote was the True Vote. But the 2008 election was highly fraudulent. Therefore, the model vote shares will closely match the likely voter polls.

Check the simulated, theoretical and snapshot electoral vote projections and corresponding win probabilities.

In 2004, Election Model forecasts were posted weekly using the latest state and national polls. The model was the first to use Monte Carlo simulation and sensitivity analysis to calculate the probability of winning the electoral vote. The final Nov.1 forecast had Kerry winning 337 electoral votes with 51.8% of the two-party vote, closely matching the unadjusted exit polls.

2004 Election Model Graphs

State aggregate poll trend
Electoral vote and win probability
Electoral and popular vote
Undecided voter allocation impact on electoral vote and win probability
National poll trend
Monte Carlo Simulation
Monte Carlo Electoral Vote Histogram

In the 2006 midterms, the adjusted National Exit Poll was forced to match the House 52-46% Democratic margin. But the 120 Generic Poll Trend Model forecast that the Democrats would have a 56.4% share – exactly matching the unadjusted exit poll.

The 2008 Election Model projection exactly matched Obama’s 365 electoral votes and was within 0.2% of his 52.9% recorded share. He won by 9.5 million votes. But the model understated his True Vote. The forecast was based on final likely voter (LV) polls that had Obama leading by 7%. Registered voter (RV) polls had him up by 13% – even before undecided voters were allocated. The landslide was denied.

The post-election True Vote Model determined that Obama won by 23 million votes with 420 EV. His 58% share matched the unadjusted state exit poll aggregate (83,000 respondents).

Exit pollsters and media pundits have never explained the massive 11% state exit poll margin discrepancy or the impossible 17% National Exit Poll discrepancy. If they did, they would surely claim that the discrepancies were due to reluctant Republican responders. But they will not even try to explain the impossible returning voter adjustments required to force the polls to match the recorded vote in the 1988, 1992, 2004 and 2008 elections.

2008 Election Model Graphs
Aggregate state polls and projections (2-party vote shares)
Undecided vote allocation effects on projected vote share and win probability
Obama’s projected electoral vote and win probability
Monte Carlo Simulation Electoral Vote Histogram

Published 10/27/12:
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Track Record: Election Model Forecast; Post-election True Vote Model
https://docs.google.com/document/d/1zRZkaZQuKTmmd_H0xMAnpvSJlsr3DieqBdwMoztgHJA/edit

2004 Election Model (2-party shares)
Kerry 51.8%, 337 EV (snapshot)
State exit poll aggregate: 51.7%, 337 EV
Recorded Vote: 48.3%, 255 EV
True Vote Model: 53.6%, 364 EV

2008 Election Model
Obama 53.1%, 365.3 EV (simulation mean);
Recorded: 52.9%, 365 EV
State exit poll aggregate: 58.0%, 420 EV
True Vote Model: 58.0%, 420 EV

2012 Election Model
Obama Projected: 51.6% (2-party), 332 EV snapshot; 320.7 expected; 321.6 mean
Adjusted National Exit Poll (recorded): 51.0-47.2%, 332 EV
True Vote Model 56.1%, 391 EV (snapshot); 385 EV (expected)
Unadjusted State Exit Polls: not released
Unadjusted National Exit Poll: not released

 
28 Comments

Posted by on October 17, 2012 in 2012 Election

 

Tags: , , , , , , , , , ,

 
Richard Charnin's Blog

JFK Conspiracy and Systemic Election Fraud Analysis