Hit List: An In-Depth Investigation into the Mysterious Deaths of Witnesses to the JFK Assassination

Richard Charnin
April 18, 2013

Hit List: An In-Depth Investigation into the Mysterious Deaths of Witnesses to the JFK Assassination by Richard Belzer and David Wayne, is a unique and welcome addition to the massive trove of JFK Assassination literature.

There is no conjecture here, just the facts surrounding fifty mysterious witness deaths presented in an easy-to-read format. Warren Commission apologists are reduced to irrelevancy; the proof of conspiracy is overwhelming and beyond any doubt. The authors cite my probability analysis as background information.

The 1973 film Executive Action depicted a conspiracy to assassinate JFK and revealed that an actuary engaged by the London Sunday Times calculated the probability of 18 material witnesses dying within three years of the JFK assassination as 1 in 100,000 TRILLION.

In this video, Mark Lane, famous author/investigator of several books on the assassination, interviews Penn Jones, an independent researcher of JFK witness deaths.

Assuming the data and calculation methodology were essentially correct, then it was clear proof of a conspiracy and refuted the Warren Commission’s conclusion that Oswald was the lone assassin. A comprehensive probability analysis shows that the actuary’s odds were conservative. There were many more than 18 suspicious deaths.

The proof is in the post Executive Action: JFK Witness Deaths and the London Times Actuary which links to the JFK Witness Database Spreadsheet Model.

The probability analysis is straightforward; it is not a theoretical exercise. It is a mathematical proof of conspiracy based on factual data (552 Warren Commission witnesses, at least 14 unnatural deaths, corresponding mortality rates) and the Poisson probability formula. The numbers and probabilities speak for themselves. This is a challenge to those who still claim that the deaths do not prove a conspiracy: To substantiate your claim, you must refute the data (i.e., the Warren Commission witness list), the unnatural mortality rates and the use of the Poisson formula.

There were approximately 1400 JFK-related witnesses. In 1964-1977, at least 70 died unnaturally (homicide, suicide, accidental, unknown) and 34 deaths were suspiciously timed heart attacks, cancers, etc. Normally 11 unnatural deaths would be expected.

Some have questioned the relevance of the unnatural and suspicious witness deaths related to the assassination. There are 71 unnatural deaths out of the 107 deaths in the spreadsheet database. Of the 107, 24 testified at the Warren Commission, 12 were sought or testified at the Clay Shaw trial by prosecutor Jim Garrison, 4 by the Church Senate Committee, 17 by the House Select Committee on Assassinations (HSCA). Of the 57, 9 testified or were sought to testify by two of the four groups. Therefore, at least 48 witnesses in the database of 107 are indisputably relevant.

What are the odds that 48 witnesses called to testify (out of 1400 material witnesses) would meet unnatural deaths- before OR after testifying?
P= 2.67E-39 (less than 1 in a TRILLION TRILLION TRILLION).

The probability of exactly n deaths among N witnesses over T years given mortality rate R is calculated using the Poisson function: P (n) = Poisson (n, N*T*R, false)

The probability of at least n deaths is P (n) = 1- Poisson (n-1, N*T*R, true)

The following table displays the unnatural cause of death and corresponding mortality rate, expected number of deaths among the 1400 JFK witnesses, the actual number of deaths, and the probability.

Cause……..rate; expected; actual; probability
suicide……. 0.000107; 2.1; 7; 1 in 170
homicide…. 0.000062; 1.2; 40; 1 in 1 BILLION TRILLION TRILLION TRILLION
accidental.. 0.000359; 7.0; 23; 1 in 2.3 MILLION
unknown… 0.000014; 0.3; 5; 1 in 5 THOUSAND

TOTAL UNNATURAL..0.000542; 10.6; 70; 1 in 700 MILLION TRILLION TRILLION

To those who say there were many more than 1400 material witnesses, which means the probabilities are too low, consider 10,000 witnesses and 72 unnatural deaths from 1964-77. The probability is 1.3E-16 or 1 in 8,000 TRILLION, very close to the Sunday London Times actuary. For 3 years and 34 unnatural deaths, it is 7E-18 or 1 in 40,000 TRILLION.

This graph shows the long-term trend in U.S. homicide rate. Note that in 1963 the rate was approximately 6 per 100,000 (0.000062 is used in the homicide probability calculation).

Assuming 1400 JFK-related witnesses and the mortality rates above, the probability of at least
- 15 UNNATURAL deaths within ONE year of the assassination: 1 in 167 TRILLION.
- 33 UNNATURAL deaths within THREE years: less than 1 in 100 TRILLION TRILLION.
- 70 UNNATURAL deaths from 1964-77: 1 in 700 MILLION TRILLION TRILLION.
- 40 HOMICIDES from 1964-77: 1 in a BILLION TRILLION TRILLION TRILLION.

Of the 552 Warren Commission witnesses, there were at least fourteen unnatural deaths: 3 suicides, 5 homicides and 6 accidents. Nine others were suspicious. The probability of at least 22 UNNATURAL/SUSPICIOUS deaths and 1 attempted murder is 1 in 7 BILLION. If the “suicides” and “accidents” were actually HOMICIDES, then the probability of at least 14 HOMICIDES among the 552 witnesses is 1 in 2 THOUSAND TRILLION.

 

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)

 

The True Vote Model: A Mathematical Formulation

The True Vote Model: A Mathematical Formulation

http://richardcharnin.com/

Richard Charnin
Feb.5, 2013

A matrix is a rectangular array of numbers. The 1968-2012 National True Vote Model (TVM) is an application based on Matrix Algebra. The key to understanding the theory is mathematical subscript notation. The actual mathematics is really nothing more than simple arithmetic.

The model is easy to use. Just two inputs are required: the election year and calculation method (1-5). Each method uses the final adjusted National Exit Poll vote shares, but methods 2-5 change the returning voter mix to a feasible one (calculation method):
Method 1 displays the adjusted National Exit Poll which is always forced to match the recorded vote.
Method 2 assumes returning voters based on the previous election recorded vote.
Method 3 assumes returning voters based on the previous election votes cast (allocates uncounted votes).
Method 4 assumes returning voters based on the previous election unadjusted national exit poll.
Method 5 calculates the True Vote based on the previous election True Vote.

The True Vote (TV) is a function of the number of previous election returning and new voters in the current election and each candidate’s share of these voters.
TV = f(turnout, vote shares)

The US Vote Census estimates the number of votes cast in each election. Total votes cast include uncounted ballots, as opposed to the official recorded vote.

The True Vote Model is based on total votes cast – as it should be. There were approximately 40 million uncounted votes in the 6 elections from 1988-2008. Uncounted ballots are strongly Democratic.

Let TVP = total votes cast in previous election
Let TVC = total votes cast in the current election

The number of returning voters (RV) is estimated based on previous election voter mortality (5%) and an estimated turnout rate (TR).

For example, in 2004 there was an estimated 98% turnout (TR) of living 2000 voters. Voter mortality (VM) is 5% over four years (1.25% per year). We calculate returning 2000 voters as:
RV = TVP * (1- VM) * TR
RV = 103.2 = 110.8 * .95 * .98

There were 125.7 million votes cast in 2004. Therefore, we calculate the number of new voters TVN as:
TVN = TVC – RV
TVN = 24.5 = 125.7 – 103.2

In the base case we assume an equal turnout rate of previous election Democratic, Republican and other (third-party) voters.
V (1) = returning Democratic voters
V (2) = returning Republican voters
V (3) = returning other (third-party) voters
RV = V (1) + V (2) + V (3) = total returning voters
V (4) = TVC – RV = number of new voters.

Calculate m (i) as the percentage mix of total votes cast (TVC) for returning and new voters V(i):
m (i) = V (i) / TVC, i=1, 4

Let a (i, j) = candidates (j=1,3) vote shares of returning and new voters (i=1,4).

True Vote calculation matrix
Vote Mix Dem Rep Other
Dem m1 a11 a12 a13
Rep m2 a21 a22 a23
Oth m3 a31 a32 a33
Dnv m4 a41 a42 a43

The total Democratic share is:
VS(1) = ∑ m(i) * a(i, 1), i=1,4
This is mathematical notation for the sum of the products:
VS(1)= m(1) * a(1,1) + m(2) * a(2,1) + m(3) * a(3,1) + m(4) * a(4,1)

Republican share: VS(2) = ∑ m(i) * a(i,2), i=1,4
Third-party share:VS(3) = ∑ m(i) * a(i,3), i=1,4

Mathematical vote share constraints
Returning and new voter Mix sum to 100%
∑m (i) =100%, i= 1, 4

Candidate shares of returning and new voters sum to 100%
∑a (1, j) =100%, j=1, 3
∑a (2, j) =100%, j=1, 3
∑a (3, j) =100%, j=1, 3
∑a (4, j) =100%, j=1, 3

Total (Democratic+ Republican + Other) vote shares sum to 100%
∑ VS (i) = 100%, i=1,3

Adjusted Exit Poll: Matrix of Deceit
It is obvious that there must be fewer returning voters than voted in the prior election for each of the Democrat, Republican and third-party candidates. Approximately 5% die in the four years between elections.

But according to the adjusted, published National Exit Poll, there were millions more returning Bush voters from the previous election than were living in 1988, 1992, 2004 and 2008 – a mathematical impossibility and proof of election fraud beyond any doubt.

Sensitivity Matrix: alternative scenarios
These tables gauge the sensitivity of the total candidate vote shares to changes in their shares of returning and new voters.

In 2004,Bush won the recorded vote by 3 million (50.7-48.3%). However, at the 12:22am National Exit Poll timeline (13047 respondents), Kerry had 91% of returning Gore voters, 10% of returning Bush voters and 57% of New voters. In this base case scenario, Kerry had a 53.6% True Vote share and 10.7 million vote margin.

Adjusting the base case vote shares to view worst case scenarios:
1) Kerry has 91% (no change) of returning Gore voters, just 8% of returning Bush voters and 53% of New voters. Kerry’s total vote share is reduced to 52.1% and a 7.2 million winning margin.

2) Kerry has just 89% of returning Gore voters, 8% of returning Bush voters and 57% of New voters (no change). Kerry’s total vote share is reduced to 52.0% and a 6.9 million margin.

3) Assume the base case vote shares, but change the 98% returning 2000 voter turnout rate to 94% for Gore and 100% for Bush. Kerry’s total vote share is reduced to 52.7% and a 8.5 million margin.

4) Assume the base case 98% turnout of returning Gore and Bush voters and 91% Kerry share of returning Gore voters. To match the fraudulent recorded vote, Bush needed 61% of New voters compared to his 41% exit poll share. He also needed 96% of returning Bush voters compared to his 90% exit poll share. Both shares far exceeded the 2% margin of error. The probabilities are infinitesimal.

The sensitivity analysis confirms that the election was stolen. Kerry won all plausible (and implausible) scenarios. Bush needed an impossible 110% turnout of Bush 2000 voters to win the fraudulent recorded vote.

Adjusted 2004 National Exit Poll (match recorded vote)
2000 Votes Mix Kerry Bush Other Turnout
Gore 45.25 37% 90% 10% 0.0% 93.4%
Bush 52.59 43. 9.0 91. 0.0 109.7 (impossible)
Other 3.67 3.0 64. 14. 22. 97.7
DNV. 20.79 17. 54. 44. 2.0 -

Total 122.3 100% 48.3% 50.7% 1.0% 101.4%

............2004 True Vote Model
2000 Votes Mix Kerry Bush Other Turnout
Gore 52.13 41.5% 91% 9.0% 0% 98%
Bush 47.36 37.7 10.0 90.0 0.0 98
Other 3.82 3.00 64.0 14.0 22. 98
DNV. 22.42 17.8 57.0 41.0 2.0 -

Total 125.7 100% 53.5% 45.4% 1.0% 98%

........Kerry share of New voters (DNV)
Pct 39.% 55.% 57.% 59.% 61.%
of Bush.........Kerry % Vote Share
12% 51.1 54.0 54.3 54.7 55.1
11% 50.7 53.6 54.0 54.3 54.7
10% 50.4 53.2 53.6 53.9 54.3
9.% 50.0 52.9 53.2 53.6 53.9
4.% 48.1 51.0 51.3 51.7 52.1
Margin
12% 4.6 11.8 12.8 13.6 14.6
11% 3.7 10.9 11.8 12.7 13.6
10% 2.7 10.0 10.9 11.8 12.7
9.% 1.8 9.0 9.91 10.8 11.7
4% -2.9 4.3 5.18 6.08 7.00

........Returning Gore Voter Turnout
Bush 94.% 95.% 96.% 97.% 98.%
Turnout..... Kerry % Vote Share
96% 53.4 53.5 53.7 53.8 53.9
97% 53.2 53.3 53.5 53.6 53.8
98% 53.0 53.2 53.3 53.4 53.6
99% 52.8 53.0 53.1 53.3 53.4
100% 52.7 52.8 52.9 53.1 53.2
Margin
96% 10.3 10.7 11.0 11.4 11.8
97% 9.86 10.3 10.6 10.9 11.3
98% 9.42 9.78 10.1 10.5 10.9
99% 8.97 9.33 9.69 10.1 10.4
100% 8.52 8.88 9.24 9.60 9.96

 

1968-2012 Presidential Election Fraud: An Interactive True Vote Model Proof

1968-2012 Presidential Election Fraud: An Interactive True Vote Model Proof

http://richardcharnin.com/

Richard Charnin
Jan. 22,2013

The 1968-2012 National True Vote Model (TVM) has been updated to include the 2012 election. Anyone can run the model and calculate the True Vote for every presidential election since 1968. Only two inputs are required: the election year and the calculation method (1-5). These deceptively simple inputs produce a wealth of information and insight.

In the 1968-2012 elections, the Republicans led the average recorded vote 48.7-45.8%. The Democrats led the True Vote by 49.6-45.1%, a 7.4% margin discrepancy.

The calculation methods are straightforward. Method 1 reproduces the Final National Exit Poll which is always adjusted to match the official recorded vote. It is a mathematical matrix of deceit. Consider the impossible turnout of previous election Republican voters required to match the recorded vote in 1972 (113%), 1988 (103%), 1992 (119%), 2004 (110%) and 2008 (103%). This recurring anomaly is a major smoking gun of massive election fraud.

Methods 2-5 calculate the vote shares based on feasible returning voter assumptions. There are no arbitrary adjustments. Method 2 assumes returning voters based on the previous election recorded vote; method 3 on total votes cast (includes uncounted votes); method 4 on the unadjusted exit poll; method 5 on the previous (calculated) True Vote.

In the 12 elections since 1968, there have been over 80 million net (of stuffed) uncounted ballots, of which the vast majority were Democratic. And of course, the advent of unverifiable voting machines provides a mechanism for switching votes electronically.

Final election vote shares are dependent on just two factors: voter turnout (measured as a percentage of previous living election voters) and voter preference (measured as percentage of new and returning voters).

The TVM uses best estimates of returning voter turnout (“mix”). The vote shares are the adjusted National Exit Poll shares that were applied to match the recorded vote.

It turns out that the Final Exit Poll match to the recorded vote is primarily accomplished by changing the returning voter mix to overweight Republicans.

In 2004, the adjusted National Exit Poll indicated that 43% of voters were returning Bush 2000 voters (implying an impossible 110% Bush 2000 voter turnout in 2004) and 37% were returning Gore voters. But just changing the returning voter mix was not sufficient to force a match to the recorded vote; the Bush shares of returning and new voters had to be inflated as well. Kerry won the unadjusted NEP (13660 respondents) by 51.0-47.5%.

In 2008, the adjusted NEP indicated that 46% of voters were returning Bush voters (an impossible 103% turnout) and 37% returning Kerry voters. Obama won the unadjusted NEP (17836 respondents) by 61.0-37.5%.

Sensitivity Analysis

The final NEP shares of new and returning voters are best estimates based on total votes cast in the prior and current elections and a 1.25% annual mortality rate. But we need to gauge the effect of incremental changes in the vote shares on the bottom line Total Vote. The TVM does this automatically by calculating a True Vote Matrix of Plausibility (25 scenarios of alternative vote shares and corresponding vote margins).

The base case turnout percentage of prior election voters is assumed to be equal for the Democrat and Republican. The turnout sensitivity analysis table displays vote shares for 25 combinations of returning Democratic and Republican turnout rates using the base case vote shares.

The National Election Pool consists of six media giants and funds the exit polls. In 2012 the NEP decided to poll in just 31 states, claiming that it would save them money in these “tough” times. It would have cost perhaps $5 million to poll the other 19 states. Split it six ways and it’s less than the salary of a media pundit.

The published 2012 National Exit Poll does not include the “Voted in 2008” crosstab. It would have been helpful, but we don’t really need it. We calculated the vote shares required to match the recorded vote by trial and error, given the 2008 recorded vote as a basis. After all, that’s what they always do anyway.

 

Track Record: 2004-2012 Election Forecast and True Vote Models

Track Record: 2004-2012 Election Forecast and True Vote Models

Richard Charnin
Jan. 19, 2013

This is a summary of 2004-2012 pre-election projections and corresponding recorded votes, exit polls and True Vote Models.

Note that the Election Model forecasts are based on final state pre-election Likely Voter (LV) polls, a subset of the total Registered Voters (RV) polled. The LVs always understate Democratic voter turnout; many new (mostly Democratic) voters are rejected by the Likely Voter Cutoff Model (LVCM). In addition, pre-election polls utilize previous election recorded votes in sampling design, rather than total votes cast. Total votes cast include net uncounted votes which are 70-80% Democratic. The combination of the LVCM and uncounted votes results in pre-election polls understating Democratic turnout – and their projected vote share.

2004 Election Model
Kerry Projected 51.8% (2-party), 337 EV (simulation mean), 322 EV snapshot
Adjusted National Exit Poll (recorded vote): 48.3-50.7%, 252 EV
Unadjusted State exit poll aggregate: 51.1-47.6%, 349 EV snapshot, 336 EV expected Theoretical)
Unadjusted National Exit Poll: 51.7-47.0%
True Vote Model: 53.6-45.1%, 364 EV

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

2006 Midterms
Democratic Generic 120-Poll Trend Projection Model: 56.4-41.6%
Adjusted Final National Exit Poll (recorded vote): 52.2-45.9%
Unadjusted National Exit Poll: 56.4-41.6%
Wikipedia recorded vote: 57.7-41.8%

2008 Election Model
Obama Projected: 53.1-44.9%, 365.3 expected EV; 365.8 EV simulation mean; 367 EV snapshot
Adjusted National Exit Poll (recorded vote): 52.9-45.6%, 365 EV
Unadjusted State exit poll aggregate: 58.1-40.3%, 419 EV snapshot, 419 expected EV
Unadjusted National Exit Poll: 61.0-37.5%
True Vote Model: 58.0-40.4%, 420 EV

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

2010 Midterms Overview
True Vote Model Analysis

2012 Election Model
Obama Projected: 51.6% (2-party), 332 EV snapshot; 320.7 EV expected; 321.6 EV simulation 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

2012 Model Overview
Electoral Vote Trend
Monte Carlo Simulation Electoral Vote Frequency Distribution

 

Walker Recall: County Cumulative Vote Shares by Increasing Unit/Ward Size

Walker Recall: County Cumulative Vote Shares by Increasing Unit/Ward Size

Richard Charnin
Dec.18,2012

This is a cumulative vote trend analysis of the Walker Recall by increasing unit/ward vote counts. The data had already been included in The Walker Recall True Vote Database Model. Each county was sorted by size of Unit/Ward. Cumulative vote shares for Walker and Barrett were calculated and the graphs were generated.

The cumulative vote trend graphics is similar to Francois Choquette et al. analysis of the GOP Primaries and Prop. 37.

Note the upward sloped lines for Walker in Milwaukee, Racine, Winnebago, Waukesha counties. The Law of Large numbers is violated; we would expect flat or slightly upward sloping lines for Barrett since Democratic shares are usually higher in larger urban wards than in smaller rural ones.

If the lines are flat or upward sloping for Walker, this is an indicator of vote miscount favoring Walker.

The Law of Large Numbers

As the vote count increases, the cumulative vote shares should hardly change (the lines should be nearly flat). But if they diverge, there must be some external factor causing it. It could very well be the FRAUD FACTOR.

Consider this baseball analogy. Why do batting averages fluctuate so greatly in the spring, but less and less as the season progresses? The Law of Large Numbers. Batting average= Total base hits/Total At Bats

Vote share for Walker= Walker Votes/Total Votes (but the Law of Large numbers was violated in the election)

The following counties appear to be the most anomalous: Brown, Milwaukee, Ozaukee, Racine, Richland, Shawano, Sheboygan, Walworth, Waukesha and Winnebago.

Why would Barrett’s vote shares in Milwaukee County decline with increasing ward size? Presumably, larger wards are more Democratic than smaller wards. If anything, one would expect the lines to DIVERGE OR AT LEAST REMAIN PARALLEL – NOT CONVERGE. The Wisconsin True Vote Model indicated that Barrett had 66.0% in Milwaukee compared to his 63.6% recorded share. In Brown, 52.2% vs. 40.0%, Racine 51.5% vs. 46.9%, Sheboygan 47.4% vs. 35.3%; Winnebago 53.5% vs. 43.6%.

 

A Model for Estimating Presidential Election Day Fraud

A Model for Estimating Presidential Election Day Fraud

Richard Charnin
Jan. 1, 2013

Given 1) early voting (mail-in or hand-delivered paper ballots) and 2) late vote (absentees, provisional ballots) and 3) the total recorded vote, what is the Election Day vote share required to match the recorded vote?

This 2012 election fraud analysis shows that Obama’s Election Day vote share is approximately 3% lower than his total recorded share (a 6% discrepancy in margin) – and a strong indicator that votes were mostly stolen on Election Day. His late vote share is 10% higher than his Election Day share.

In 2012, there were 11.677 million late recorded votes (9.04% of the total). The late vote for each state is the difference between the current and Election Day votes. Obama had 60.2% of the two-party late vote and 51.96% of the total two-party vote.

In 2008, Obama had 59% of 10.2 million late votes compared to 52.4% of votes cast early or on Election Day. Is it just a coincidence that he also won the 2008 unadjusted state aggregate exit polls by a nearly identical 58.0-40.5% and the National Exit Poll by 61.0-37.5%? In 2012, there were just 31 adjusted state polls; the unadjusted state and national poll results have not been released.

But is the late vote a legitimate proxy of the True Vote? To find out, we need to weight (multiply) each state’s late vote share by its total vote. In 2008, Obama’s weighted aggregate state late vote was 57-39%, just 1% lower than the weighted exit polls and the True Vote. In 2012, it was 54-42%, closely matching the 56% two-party True Vote model share.

Estimates of the National popular vote and selected battleground states. In 2008, approximately 30% of total votes were cast early. Early vote rates for each state were set to the 2008 rate. Early vote shares were based on information supplied to the media. If the early vote estimate was not available, the assumption is that Obama did 2-3% lower in early voting than late.

Uncounted votes are assumed to be 2% of votes cast (75% to Obama). This has the effect of adding 1.3 million to his 4.74 million recorded vote (3.8%) margin. But Obama’s True Vote margin is estimated to be 15.7 million (56.1-43.9%).

Total Votes Cast = Early Vote + Election Day Vote + Late Vote + Uncounted Vote
Total Votes Recorded = Early Vote + Election Day Vote + Late Vote
In order to determine the Election Day vote, a simple trial and error (goal-seeking) procedure was used by adjusting the Election Day share until the total share matched the recorded vote. This is analogous to the exit pollsters stated procedure of adjusting the exit poll to match the recorded vote in each demographic cross tab by by changing weights and/or vote shares. The National Exit Poll forced a match to the recorded vote in a number of elections by adjusting actual exit poll results using mathematically impossible weightings (i.e., millions more returning voters from the previous election than were alive to vote in the current election).

In this analysis, we use actual early and late recorded vote data to determine the Election Day 2-party share required to match the total recorded vote. Unlike the media, the “goal-seek” is to determine the fraud component, not ignore it.

On Election Day, Votes cast on optical scanners and DREs are vulnerable to miscounts on the central tabulators.

Florida
Percent of total vote: Early 52%; Late 2%
To match his 2-party share (49.3%), Romney needed 51% on Election Day.

Ohio
Percent of total vote: Early 25%; Late 4%
To match his 2-party share (48.4%), Romney needed 51% on Election Day.

Iowa
Percent of total vote: Early 36%; Late 2%
To match his 2-party share (51.1%), Romney needed 70% on Election Day.

North Carolina (zero late vote?)
Percent of total vote: Early 60%; Late 0%
To match his 2-party share (47.3%), Romney needed 51% on Election Day.

California
Percent of total vote: Early 45%; Late 27%
To match his 2-party share (38.1%), Romney needed 46% on Election Day.

Arizona
Percent of total vote: Early 53%; Late 29%
To match his 2-party share (54.9%), Romney needed 60% on Election Day.

Virginia
Percent of total vote: Early 14%; Late 4%
To match his 2-party share (48.0%), Romney needed 51% on Election Day.

New Mexico
Percent of total vote: Early 62%; Late 2%
To match his 2-party share (45.1%), Romney needed 48% on Election Day.

Georgia
Percent of total vote: Early 53%; Late 1%
To match his 2-party share (53.1%), Romney needed 58% on Election Day.

National Vote – forced to match the recorded share
How Voted (2-party)………….Votes Pct Obama Romney
Early voting (paper)…………40.6 32.0% 55.0% 45.0%
Election Day…………………75.0 59.1% 49.0% 51.0%
Late Votes (paper)…………..11.2 8.9% 60.2% 39.8%

Recorded Share……….126.8 100.0% 51.9% 48.1%
Total Votes (mil)………………………… 65.85 60.98

…….. Obama Election Day %
…….. 49.0% 52.0% 56.0%
Early Obama Share
56.0% 52.2% 54.0% 56.4%
55.0% 51.9% 53.7% 56.1%
49.0% 50.0% 51.8% 54.1%
Margin
56.0% 5.7 10.2 16.2
55.0% 4.9 9.4 15.4
49.0% 0.0 4.5 10.5

 

Matrix of Deceit: Election Myths, Logic and Probability of Fraud

Election Fraud: Uncertainty, Logic and Probability

Oct. 29, 2012

Everyone thinks about problems every day. But how sure are they that their conclusions on how to solve them are valid? My new book Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts deals with uncertainty in our election systems. How do we know that the votes are counted as cast? If the information we are given is tainted, how do we know? We must distinguish between intuitive and logical reasoning. Yet decisions must be made everyday where there are multiple choices.

Which make the most sense? Which is the most probable? If you flip a coin and it comes up heads five times in a row, is the next flip more likely to be tails? Is a baseball player with a .300 batting average who has not had a base hit in his last 10 at bats due to get one his next time up? In decision making, we always need to consider probabilities.

In mathematics we need unambiguous definitions and rules. In other words, we need logical thinking. Logic is defined as a systematic study of the conditions and procedures required to make valid inferences.

We start with a statement and infer other statements are valid and justified as a consequence of the initial statement. It is important to note that logical inference does not mean the statement is true, only that it is valid. If the starting statement is true, then a logically derived result must also be true.

For example, it is a statement of fact that Bush had 50.5 million recorded votes in 2000. Approximately 2.5 million Bush 2000 voters died prior to the 2004 election, so there could not have been more than 48 million returning Bush voters. But according to the 2004 National Exit Poll, there were 52.6 million returning Bush voters. This is clearly impossible.

Furthermore, since the 2004 National Exit Poll was impossible and adjusted to match the recorded vote, then the recorded vote must also have been impossible. This simple deductive reasoning proves 2004 Election Fraud. But the recorded 2000 vote was also fraudulent – as were all elections before that. None reflected true voter intent. The simple proof: there were 6-10 million uncounted votes in every election prior to 2004. Votes cast exceeded votes recorded by 6-10 million. And 70-80% of the uncounted votes were Democratic.

Each National Exit poll is forced to match the bogus recorded vote based on bogus returning voters from the prior bogus election. It’s a recursive process. The polls assume all elections are fair and accurate. The same returning voter logic applied to the 1988, 1992 and 2008 elections shows that they were also fraudulent; the National Exit Polls were forced to match the recorded vote by indicating there were more returning Bush voters than were alive to vote. The corporate media has never seen fit to explain these recurring impossibilities.

Science is “cumulative”. New developments may refine or extend past knowledge. There is no such thing as a foolproof system. What is needed is a probability-based system for many types of problems. It is the only rational way of thinking.

There is no way to eliminate all risk (error) in a system model (or election poll). The problem is to evaluate risk and measure it based on a probability analysis. Every important problem requires a comparison of the odds. Probability analysis supplements classical logical thinking but does not replace it. In fact, classical logic is required in every step in the development of probability theory.

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

 

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

 

The Gallup Battleground Poll: an 8% Discrepancy from the state polls

The Gallup Battleground Poll: an 8% Discrepancy from the State polls

Richard Charnin
Oct. 16, 2012

The corporate media has been very busy today claiming that the Gallup poll shows Romney winning the battleground states by 4%. But according to the latest state polls, it’s exactly the opposite.

The latest state polls are in the 2012 Presidential True Vote/ Election Fraud Forecast Model.

Obama leads by 49.3-46.3% in 13 (131 EV) of 17 (198 EV) Battleground state polls weighted by state voting population.

View the numbers in this sheet (scroll down to row 119).

The Monte Carlo simulation gives Obama a 98.2% win probability if the election were held today. He has 306 expected electoral votes.