Richard Charnin

July 27, 2015

The purpose of the Monte Carlo Electoral Vote Simulation Model is to calculate the probability of a candidate winning at least 270 Electoral votes.

The model contains the following Obama 2-party vote shares:

2008- Unadjusted state exit polls and recorded votes

2012- True Vote Model shares (19 states were not exit polled) and recorded votes

The Electoral Vote Histogram shows the results of the 200 simulation trials.

There are four input methods. Enter 1,2,3,4

2008: 1- exit poll, 2- recorded votes;

2012: 3- True vote, 4- recorded votes

In order to see the effects of changes, a blank column is inserted so that vote shares can be overridden.

The Total Electoral Vote is calculated using individual state projections. But the probability of winning each state is required in order to calculate the total probability of winning 270 EV. The state win probability is calculated using the projected two-party vote share and the margin of error (MoE).

The Total EV is calculated as the sum of the products of the state win probabilities and corresponding electoral votes.

**Prob = NORMDIST (vote share, 0.5, MoE/1.96, true)**

1- The theoretical expected EV is the sum of the 51 state win probabilities multiplied by the corresponding EVs.

2- The snapshot EV is just the sum of the projected electoral votes. It cam be misleading if state elections are close.

3- The mean EV is the average of the 200 simulation trials.

The three methods yield similar EVs.

Likely Voter polls are used by political pundits to forecast the recorded vote which they assume to be fraud-free -but which are usually fraudulent. The recorded vote is forecast in the Election Model, but only for comparison to the True Vote. The True Vote Model is based on prior election returning and new voters and corresponding candidate vote shares. This method gives a close approximation to the True State Vote.

The 2012 Monte Carlo Simulation Forecast exactly matched Obama’s 332 electoral votes and 51.0% total vote share. In the True Vote Model he had 55.6% and 391 Electoral votes.

In the 2008 Election Model Obama’s 365.3 expected theoretical electoral vote was a near-perfect match to his recorded 365 EV. The simulation mean EV was 365.8 and the snapshot was 367. Obama’s won all 5000 election trials. His projected 53.1% share was a close match to the 52.9% recorded share.

But the forecast was based on Likely Voter (LV) polls, a subset of Registered Voter (RV) polls which projected 57% for Obama The RV polls were confirmed by the post-election True Vote Model (58%,420 EV) which matched the unadjusted state exit poll aggregate (58%,420 EV). Obama had 61% in the unadjusted National Exit Poll of 17836 respondents.

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July 25, 2015

This is an informative article and video from *We the People Dane County Blog* http://wethepeopledanecounty.blogspot.com/2015/07/the-media-scott-walkers-november-2014.html. It contains links to Election Fraud articles (including many of my blog posts) and related videos.

Analysis of Scott Walker’s 2012 recall and the November 2014 election results can be shown to be mathematically implausible and cannot represent voter intent. The chance that Scott Walker has, in 2 consecutive election cycles, “won” with vote totals that each violate the Law of Large Numbers is zero.

While Scott Walker bases his 2016 Presidential Campaign on the statement he has won 3 elections in 4 years, in fact, at least 2 of these elections can be demonstrated to have been stolen. The embedded video below explains and highlights the media’s role in election fraud.

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Richard Charnin

June 16, 2011

Updated May 6,2012 to include unadjusted exit polls

Updated July 21, 2015 to include Cumulative Vote share analysis

Charnin Website

Wisconsin blog posts

**2010 Wisconsin Senate True Vote Model
**

This is an updated analysis of the 2010 Wisconsin Senate race. The WI Exit Poll was forced to match the recorded vote (Johnson defeated Feingold by 52-47%). Forcing a match to the recorded vote is standard operating procedure. In order to force a match in the 2004 and 2008 presidential elections, the exit pollsters had to assume an impossible number of returning Bush voters from the previous election.

The returning voter mix should reflect the previous election True Vote, not the recorded vote. In the adjusted 2010 exit poll, 49% of the recorded votes were cast by returning Obama 2008 voters and 43% by returning McCain voters. The ratio is consistent with Obama’s 7.5% national recorded vote margin.

In Wisconsin, Obama had a 56.2% recorded share; Feingold just 47%. But Obama led the unadjusted Wisconsin exit poll by 63-36% (2,545 respondents; 2.4% margin of error). In Oregon, Obama had a 57% recorded share. Ron Wyden, a progressive Democratic senator running for re-election,had an identical 57%.

The probability is 97.5% that Obama’s true Wisconsin vote share exceeded 61%. Assuming Obama had 61%, how could Feingold have had just 47% two years later?

In the 2010 WI exit poll, Vote shares were not provided for returning third party (Other) voters and new (DNV) voters which represented 3% and 5% of the total recorded vote, respectively. In order to match the vote, Johnson must have won these voters by approximately 60-35%, which is highly unlikely. In 2008, Obama won returning third party voters by 66-20%.

A comparison of the demographic changes from 2004 to 2010 yields interesting results – but the 2010 numbers are suspect asthey are based on the the 2010 recorded vote:

– Johnson needed 70% of voters who decided in the final week to win.

**From 2004 > 2010:**

Females: 53% > 50% (is not plausible).

Voters over 45: 50% > 62% (seems high)

Party ID: 38R/35D > 37D/36R (more Democrats, so how did Feingold lose)

Independents for Feingold: 62% > 43% (implausible)

Labor for Feingold; 66% > 59% (why would he lose his base support?)

Milwaukee County for Feingold: 68% > 61% (10% of his base defected?

Suburban/Rural for Feingold: 51% > 43%

**The True Vote Model**

Using the unadjusted 2008 Wisconsin presidential exit poll as a basis, Feingold won by 52.6-45.5%, a 154,000 vote margin. The model assumes McCain returning voter turnout of 70% in 2010, compared to just 63% of Obama voters. It also assumes the adjusted exit poll shares that were required to match the recorded vote. The adjusted poll indicates that Feingold had an implausibly low 84% share of returning Obama voters. If Feingold had 89% (all else being equal), he would have won by 289,000 votes with a 56% total share.

**Sensitivity Analysis**

Vote shares are displayed for various scenarios of a) returning Obama and McCain voter turnout and b) Feingold’s share of returning and new voters. Although the exit poll was forced to match the recorded vote, the True Vote Model uses the adjusted vote shares as the base case. It is likely that the vote shares were also adjusted to force a match to the recorded vote.

The True Vote Base Case analysis assumes a 1.0% annual voter mortality rate, a 63% turnout of living Obama voters and a 70% turnout of McCain voters. The percentage mix of returning 2008 third-party (other) voters could not have been the 3% indicated in the WI exit poll. That would mean there were 65,000 third-party voters but there were just 44,000. Therefore, the model assigned the 1.5% excess of Other voters to New/DNV (first-time voters and others who did not vote in 2008).

Feingold was the winner in all scenarios of returning Obama and McCain voters. But it is important to keep in mind that the adjusted WI exit poll gave Feingold just 84% of returning Obama voters. It is difficult to accept the premise that nearly one of six Obama voters defected to Johnson.

**Cumulative Vote Shares**

The sharply increasing Johnson cumulative vote share in Milwaukee and other counties defies explanation. Democratic vote shares rise in large urban voting precincts.

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July 20 2015

Charnin Website

Look inside the book: Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Look inside the book:Reclaiming Science:The JFK Conspiracy

Edison Research conducts exit polls. In this report, ER once again fails to mention the Election Fraud factor, which has skewed the True Vote in national, state and local elections for decades. http://statistical-research.com/wp-content/uploads/2014/08/Probability-Based-Exit-Poll-Estimation.pdf

Frustrated voters who have seen their elections stolen need to know the facts. The corporate media never discusses Election Fraud – the third-rail of American politics. But it is no longer the dirty little secret it was before the 2000 election. This is an analytic overview of Historical Election Fraud: https://richardcharnin.wordpress.com/2013/01/31/historical-overview-of-election-fraud-analysis/

**My comments are in bold italics.**

Edison: Of the surveys there were 19 states where the sample size was too small for individual state demographic or other breakouts.

**That is absolute nonsense. In 2012, the National Election Pool (NEP) of six media giants which funds the exit polls said it did not want to incur the cost, so they would not run exit polls in 19 states. That was a canard. Could it be that the NEP and the pollsters did not want the full set of 50 state exit polls to be used in a True Vote analysis? The continued pattern of discrepancies would just further reveal built-in systematic fraud. **

That is also why the question “How Did You Vote in 2008” was not published along with the usual cross tabs. The “How Voted” crosstab is the Smoking Gun of Election Fraud. In every election since 1988, the crosstab illustrates how pollsters adjust the number of returning Republican and Democratic voters (as well as the current vote shares) to match the recorded vote.

https://richardcharnin.wordpress.com/2014/11/19/the-exit-poll-smoking-gun-how-did-you-vote-in-the-last-election/

Edison: The majority of interviews are conducted in-person on Election Day in a probability sample that is stratified based on geography and past vote.

*The past vote is the bogus recorded vote which favors the Republicans. Any stratification strategy is therefore biased and weighted to the Republicans.
*

Edison: The goal in this paper is not to provide a comprehensive and exhaustive discussion of the intricacies of the operational and statistical aspects of an exit poll but to provide additional discussion on various ways to incorporate probability distributions into an exit poll framework. The core of this discussion is based on discrete data in the exit poll. The examples used in this paper will be based on the data obtained from the 2012 presidential election and will specifically address the use of the Dirichlet and Normal distributions.

**How does Edison explain the massive exit poll discrepancies? **

– In 2008, Obama had 61% in the National Exit Poll (17836 respondents) and 58% in the weighted aggregate of the state exit polls. But he had a 52.9% recorded share. The probability of the discrepancy is ZERO.

– In 2004, John Kerry had 51.7% in the unadjusted National Exit Poll (13660 respondents)s. He led the state aggregate by 51.1-47.6%. But Kerry lost the recorded vote by 50.7-48.3%.

**– In 2000, Al Gore led the unadjusted National Exit Poll by 48.5- 46.3%. He led the state aggregate polls by 50.8-44.4%. But Gore was held to a 48% tie with Bush in the recorded vote.**

Edison: A useful characteristic relating to probability distributions is the ability to use known data and then simulate from the posterior distribution. Using the exit poll framework, the statewide candidate estimates can be used and applied using the Dirichlet distribution approach. This means that the estimates from each state can be used to determine the probability that a given candidate will win each state. With the probability of success established for each state we can incorporate these probabilities into a winner-take-all Binomial distribution for all 50 states and the District of Columbia.

* A simulation is not required to calculate the expected electoral vote if we already have calculated 51 state win probabilities, The expected EV is the product sum of the probabilities and corresponding EVs.*

EV = SUMPRODUCT[prob(i) * EV(i)], where i =1,51.

**In the 2012 True Vote Election Model, pre-election state win probabilities were calculated based on final Likely Voter (LV) polls. The model exactly projected Obama’s 332 EV. But Obama’s True Vote was much better than his recorded share. Note: LVs are a subset of Registered Voter (RV) polls which eliminate new, mostly Democratic, “unlikely” voters.
https://richardcharnin.wordpress.com/2012/10/17/update-daily-presidential-true-voteelection-fraud-forecast-model/ **

Edison: Clearly, ‘calling’ a national election based purely on sample data is not the most favorable strategy due to sampling variability. However, updating the probability that a candidate will win with additional known data in each of the given states will decrease the variability in the posterior distribution. This can be accomplished by using additional known prior data or, as is often the case in elections, by adding the final precinct election results provided shortly after the polling places close.

This is all good theoretically, but it assumes that the final precinct data has not been manipulated. In any case, a 10 million trial simulation is overkill. Only 500 Monte Carlo trials are necessary to calculate the probability of winning the electoral vote.

Edison: This can be accomplished by using additional known prior data or, as is often the case in elections, by adding the final precinct election results provided shortly after the polling places close. Due to the nature of elections, informed priors are often available and can be incorporated into the estimates to improve the probability distribution. In this way, specific models can be developed to handle states with more or less available prior data and improve the overall model.

*Again, no mention of the votes being flipped in the precincts.*

Edison: We can take the currently collected data and model the results using other quantities that are available. In some ways, due to the nature of linear regression, prior information is already implicitly included in exit poll regression models.

*But prior election data is based on vote-miscounts. Garbage in, garbage out.*

Edison: It is quite clear that the past Democrat vote from 2008 and the current exit poll vote from 2012 are very good predictors of the 2012 final precinct reported vote. Furthermore, using the classical linear regression, the R2 value is 0.95 indicating that a significant amount of variation in vote is explained by these two predictor variables.

* In 2008 and 2012, as in all prior elections, the allocation of returning voters was adjusted to match the recorded vote. EDISON RESEARCH MAKES THE INVALID ASSUMPTION THAT THE RECORDED VOTE IS THE TRUE VOTE. IT IS AN UNSCIENTIFIC MYTH WHICH ONLY SERVES TO PERPETUATE FRAUD IN FUTURE ELECTIONS AS A RECURSIVE BYPRODUCT OF FRAUDULENT PRIOR ELECTIONS.
*

Edison: There are two primary goals that are addressed by regression models in this paper:

1) general understanding of the data within a given state. In other words identifying variables that aid in a linear prediction of the candidate’s vote; and

2) predicting y, given x, for future observations.

*Which data? The adjusted demographic data or the actual pristine data?
If Y = f(X), then X should not be forced to fit the recorded result.
*

Edison: For the purposes of this paper the sample of polling locations using the final end of night results are used as the response variable. Generally for all states past data tends to be a very good predictor of current results. In some states there are other predictors (e.g. precinct boundary changes, current voter registration, weather, etc.) that work well while in other states those same predictors provide no additional information and make the model unnecessarily complex.

*But past data does not reflect the prior True Vote, so any regression analysis cannot predict the True Vote. It will however predict the bogus, recorded vote.*

Edison: Again, the regression model presented here is an example model used for demonstration purposes (i.e. no formal model selection procedure was used). Furthermore, for this same purpose the non-informative prior is used. It’s clear from the output of the regression summary that there is a strong effect for 2008 candidate vote percentage, precincts with high Democrat vote in 2008 tend to have a very predictable Democrat vote in 2012. As one would expect the 2012 exit poll results have a strong effect when predicting the final polling location results. This example regression model for Florida is provided in Equation 2.

E (CANDj |x,θ) = β0 +β1 ·CANDEP2012j + β2 ·CAND2008j

**All this is saying that a candidate’s vote share is predictable using regression analysis based on the 2008 recorded vote and 2012 adjusted precinct exit poll data. But if the precinct data is biased; the projection will reflect the bias. And the cycle continues in all elections that follow.
**

Edison: We can check to see if the observed data from the polling places are consistent with the fitted model. Based on the model and the predictive distribution, the model fits quite well without outliers in any of the precincts.

Of course the model will fit the bogus recorded vote quite well because it was forced to match the recorded vote.

*But what if the observed recorded precinct vote data is manipulated?*

Edison: Several important conclusions about the analysis of exit poll data can be drawn from this review of approaches using probability distributions. First, it is clear that there are many probability distribution components to an exit poll.

*But the prior information (recorded vote and adjusted exit polls) used in the probability analysis is bogus as long as there is no consideration of the Election Fraud Factor.
Recorded Vote = True Vote + Fraud
*

Edison: This research on exit polling serves as an exploration of ways to investigate and analyze data and to provide alternate, complementary approaches that may be more fully integrated into standard election (and non-election) exit polling. These procedures are only a few of the many ways that can be used to analyze exit poll data. These approaches provide an alternate way to summarize and report on these data. It also provides additional visualization and ways to view the data and how the data are distributed.

**But the core problem is not addressed here. All alternative models are useless if they are based on prior and current recorded vote data which has been corrupted.**

Edison: Further topics include small sample sizes, missing data, censored data, and a deeper investigation into absentee/early voting. Additionally, these approaches can be used to investigate various complex sample design techniques (e.g. stratified, cluster, multi-phase, etc.) and evaluate how the designs interact with probabilistic approaches in an exit polling context. Further hierarchical modeling may provide additional insight into the complexities of the exit poll data.

**These sample design techniques are all based on recorded vote data. Why are pristine exit polls always adjusted (forced) to match the Election Day recorded vote to within 0.1%? **

**Proof: Unadjusted Exit Polls are forced to match the Recorded vote:
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdFIzSTJtMTJZekNBWUdtbWp3bHlpWGc#gid=15**

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July 16, 2015

Charnin Website

Look inside the book: Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Look inside the book:Reclaiming Science:The JFK Conspiracy

2016 Presidential Election: Will voter turnout overwhelm the built-in fraud factor?

**Assumptions:**

Obama won the 2012 True Vote by 55-43%

In 2016, the Democrat wins

91% of returning Obama voters,

6% of Romney voters and

50% of New voters.

**To win the popular vote, the GOP would need 97% of Romney voters to return compared to 77% of Obama voters. But that is implausible since Obama won the 2012 True Vote by approximately 15 million. A 20% split in 2012 voter turnout is not feasible; the GOP cannot win a fair election.**

View the spreadsheet: https://docs.google.com/spreadsheets/d/1RbFw_0MHZk5bIj2439pponSrYirE20NN5PrJSlAD9Hw/edit#gid=1642225781

The Democrat would win easily if 90% of Obama 2012 voters turned out and the votes were counted fairly. But since the True Vote is never equal to the recorded vote, Democratic voters must come out in droves to overcome vote-switching and vote-dropping on proprietary voting machines which have been in place since 2002. The GOP realized that it could never win an honest election. HAVA look: https://richardcharnin.wordpress.com/2013/01/31/historical-overview-of-election-fraud-analysis/

**The published, official adjusted National Exit Poll is always forced to match the Election Day recorded vote. The NEP exactly matched Obama’s Election Day recorded share in 2008 and 2012. Was this just a coincidence?
**

The ADJUSTED National Exit Poll Gender cross tab matched the recorded vote exactly:

Obama 52.71%; McCain 45.35%.

Obama had 59.2% of 10.2 million Late Votes recorded after Election Day.

**Obama won the UNADJUSTED 2008 National Exit Poll by 61-37%.
The UNADJUSTED 2008 state exit poll aggregate matched the True Vote Model:
Obama led both by 58.0-40.5%.**

**In 2012, Obama had 50.34% and Romney 48.07% on Election Day.
In the Gender crosstab, it was a near perfect match:
**Obama led by 50.30-47.76%.

Obama had 60.23% of 11.7 million Late Votes.

**In 2012, the National Election Pool decided not to run exit polls in 19 states.**

The NEP claimed the polls were too expensive.

Or was it because the UNADJUSTED exit polls would be too revealing?

https://richardcharnin.wordpress.com/category/2004-election/

**2008-2012 Adjusted National Exit Poll
..........2012 ......... 2008......... 2016 Tie Vote scenario
Gender Pct Obama Romney Obama McCain Dem Repub**

Male....47.0 45.0 52.0 49.0 48.0 ... 43.4 53.7

Female..53.0 55.0 44.0 56.0 43.0 ... 54.0 45.0

```
```**2016 Tie Vote Scenario**

2012.........Pct Dem Repub Ind Turnout

Obama.... 39.4% 91% 6% 3% 77%

Romney... 38.8% 6% 94% 0% 97%

Other..... 1.8% 47% 48% 5% 95%

DNV.......20.0% 50% 47% 3%

**Votes......100% 66.2 66.4 2.5**

Share......100% 49.0% 49.1% 1.9%

**2012 True Vote**

2008.....Pct Obama Romney Other

Obama.. 53.8% 90% 07% 3%

McCain. 37.2% 07% 93% 0%

Other....1.5% 51% 45% 4%

DNV......7.5% 55% 42% 3%

**Vote.....100% 72.2 54.5 2.5**

Share........ 55.9% 42.2% 1.9%

Recorded..... 65.9 60.9 2.3

Share........ 51.0% 47.2% 1.8%

**Unadjusted 2008 National Exit Pool (17836 respondents)**

**Total....... Sample Obama McCain Other**

Respondents 17,836 10,873 6,641 322

**Vote Share. 100.0% 60.96% 37.23% 1.81%**

**Unadjusted 2008 National Exit Poll**

2004 Votes %Mix Obama McCain Other

DNV.....17.7 13.4 71 27 2

Kerry...57.1 43.4 89 09 2

Bush....50.8 38.6 17 82 1

Other....5.9 4.50 72 26 2

**Share..131.5 100.% 58.0% 40.4% 1.6%**

Vote...........131.5 76.3 53.0 2.2

**Final Adjusted 2008 National Exit Poll
(forced to match recorded vote with impossible returning Bush voters)
2004....Votes %Mix Obama McCain Other**

DNV.....17.1 13 71 27 2

Kerry.. 48.6 37 89 9 2

Bush... 60.5 46 17 82 1

Other... 5.3 04 72 26 2

Votes............... 69.50 59.95 2.02

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July 15, 2015

Charnin Website

Look inside the book: Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Look inside the book:Reclaiming Science:The JFK Conspiracy

The Isner–Mahut match at the 2010 Wimbledon Championships is the longest match in tennis history, measured both by time and number of games. In the Men’s Singles tournament first round, the American 23rd seed John Isner defeated the French qualifier Nicolas Mahut after 11 hours, 5 minutes of play over three days, with a final score of 6–4, 3–6, 6–7(7–9), 7–6(7–3), 70–68 for a total of 183 games.

What is the probability of a 70-68 set?

It’s the same as flipping a coin 70 times and always coming up heads

or a basketball player with a 50% average sinking 70 foul shots in a row

or a mediocre .500 baseball team winning 70 games in a row

or of 23 unnatural deaths among 1400 JFK witnesses in the first year following the assassination…

Assume the players are equally matched (each has a 50% chance of winning a game)

The probability P = .5^70 = 8.47E-22 = 1 in a BILLION TRILLION!

It never happened before and never will.

It is by far the most astounding result in sports history.

It defies explanation.

But it actually happened.

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Richard Charnin

July 2, 2015

My Website: Election Fraud and JFK

Look inside the book: Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Look inside the book:Reclaiming Science:The JFK Conspiracy

On Election Day 2012, 117.4 million votes were recorded. Obama led by 50.34-48.07%. The National Exit Poll was published the day after the election. It was adjusted to match Obama’s Election Day share: 50.30-47.76%. However, 11.7 million Late votes were recorded after Election Day. Obama won them by 60.2-39.8%. The surge in Obama’s late votes increased his final total margin to 51.03-47.19%. But he actually had a 55% True Vote share. The systematic red-shift struck again. https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdDQzLWJTdlppakNRNDlMakhhMGdGa0E#gid=29

It is way too early to make any predictions 16 months in advance. There is no reason to believe the 2016 election will be fraud-free. The Democratic True Vote is always greater than the recorded vote. But we can run True Vote Model scenarios to see what it would take for Clinton, Bush and Sanders to win.

There are two calculation methods:

Method 1: returning 2012 voters are based on the recorded vote- Obama had 51%.

This calculation assumes the election will be fraudulent since the prior recorded vote was fraudulent. Therefore, returning voter estimates are implausible. In any case, the model generates vote share scenarios based on various assumptions of Obama and Romney voter turnout.

Method 2: returning voters are based on the 2012 True Vote – Obama had 55%.

This calculation assumes that the election will be essentially fraud-free since the estimated number of returning voters is plausible.

Base case assumptions assume:

1) 2012 recorded vote shares.

2) 1.25% annual voter mortality (total 5%)

3) 95% turnout of living Obama and Romney voters.

**Sensitivity Analysis**

For Clinton to win, she needs at least 90% of returning Obama voters, 7% of returning Romney voters and 55% of new voters.

View four sensitivity analysis tables and graphs:

Clinton’s total vote share and margin for incremental changes in her shares of

1) New (51-59%) and returning Romney voters (5-9%)

Vote margins (in millions): Low: 0.91, Base: 4.59, High: 8.26

2) Returning Obama (88-92%) and Romney voters (5-9%)

Vote margins: Low: 0.01, Base: 4.59, High: 9.17

3) Clinton’s total vote share for (89-97%) Obama and (93-97%) Romney voter turnout

Vote margins: Low: 0.84, Base: 4.59, High: 6.58

4) Clinton’s probability of winning the popular vote if she wins (88-92%) of returning Obama voters and (5-9%) of Romney voters.

Win probabilities: Low: 50.28%, Base: 99.46%, High: 100.00%

For Bush to win, it is a fair guess the media will report that he had 8% of returning Obama voters, 95% of returning Romney voters and matched Clinton’s 45% share of voters who did not vote (DNV) in 2012 (recorded vote basis).

To calculate what Bush really needs to win, we assume the 2012 True Vote as a basis.

He needs at least 17% of returning Obama voters, 92% of returning Romney voters and match Clinton’s 47% of voters who did not vote in 2012.

For Sanders to win, he needs at least 50% of returning Obama voters, 20% of returning Romney voters and 40% of voters who did not vote in 2012 (recorded vote basis).

View the Clinton, Sanders, Bush Win Scenarios at the bottom of this sheet

https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdFpDLXZmWUFFLUFQSTVjWXM2ZGtsV0E&usp=sheets_web#gid=11

Track record:

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

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Richard Charnin

June 30, 2015

JFK Blog Posts

Twitter Chronological Links

The cover of * Reclaiming Science: The JFK Conspiracy* shows a graph displaying probabilities of unnatural witness deaths assuming 1500, 2000 and 2500 witnesses over a range of 0 to 50 unnatural deaths. Of the 122 suspicious deaths in the

The x-coordinate of the peak in each curve is the EXPECTED number of unnatural deaths given the number of JFK-related witnesses.

The graph illustrates the power of SENSITIVITY ANALYSIS to display how a target variable (the probability) changes as input variables change in value. In the * JFK Calc* spreadsheet, the calculation is given by the

P= Poisson (n, E, false) where

n= number of unnatural deaths

E= expected number of unnatural deaths

N= 1500 = number of witnesses (the universe),

T= 15 years from 1964-78.

R= 0.000822= unnatural national mortality rate (unweighted).

E= N*R*T = 18.5= 1500*0.000822*15

Note: The applicable unnatural rate is the JFK-weighted rate: 0.000247. The true weighted probabilities are actually much lower than those given below.

**UNNATURAL DEATHS**

**The graph shows the probability of**

30 unnatural deaths; 1500 witnesses: 0.0034 (1 in 300)

40 unnatural deaths; 2000 witnesses: 0.0011 (1 in 1000)

50 unnatural deaths; 2500 witnesses: 0.0003 (1 in 3000)

**The probability of 78 unnatural deaths for**

1500 witnesses: 4.15 E-25 (base case: 1 in a trillion trillion)

2000 witnesses: 5.00 E-18 (1 in a 200,000 trillion)

2500 witnesses: 3.92 E-13 (1 in 2 trillion)

**HOMICIDES**

There are 34 officially-ruled JFK-related homicides in **JFK Calc**. A statistical estimate of the expected cause of death indicates that approximately 50 of 88 official accidents, suicides, heart attacks, sudden cancers and other suspicious deaths were actually homicides. Therefore, there were least 80 homicides among the 122 suspicious deaths.

**Given the average 0.000084 homicide rate for 1964-78, the probability of**

34 homicides; 1500 witnesses: 1.4 E-30 (1 in a million trillion trillion)

50 homicides; 2000 witnesses: 3.7 E-46 (1 in a billion trillion trillion trillion)

80 homicides; 3000 witnesses: 6.7 E-75 (1/trillion^6)

80 homicides; 1500 witnesses: 3.7 E-98 (1/trillion^8)

**If we triple the average homicide rate to 0.000252, the probability of**

34 homicides; 1500 witnesses: 5.4 E-16 (1 in 2,000 trillion)

50 homicides; 2000 witnesses: 1.7 E-24 (1 in a trillion trillion)

80 homicides; 3000 witnesses: 5.0 E-40 (1 in a trillion trillion trillion)

80 homicides; 1500 witnesses: 1.3 E-61 (1/trillion^5)

JFK Calc: Sensitivity Analysis Tables

**SIMKIN JFK INDEX**

Of the 656 names, 66 deaths are suspicious, of which 42 were ruled unnatural (including 22 homicides).

**Unnatural deaths; probability**

42 1.65E-17 (1 in 60,000 trillion – base case)

45 9.72E-20 (1 in 10 million trillion)

50 1.21E-23 (1 in 80 billion trillion)

**Homicides; probability**

22 5.89E-24 (1 in 100 billion trillion – base case)

30 5.44E-36 (1 in 1 trillion trillion trillion)

40 2.63E-52 (1 in 1 trillion trillion trillion trillion)

JFK Calc: Simkin JFK Index

**WARREN COMMISSION**

552 testified, 31 deaths suspicious, of which 16 were ruled unnatural (4 homicides).

**Unnatural deaths; probability**

16 4.91E-09 (1 in 200,000 billion – ruled base case)

18 8.81E-11 (1 in 100 billion)

21 1.43E-13 (1 in 7 trillion)

**Homicides; probability**

4 4.92E-03 (1 in 200 – base case)

10 3.77E-11 (1 in 30 billion)

17 3.11E-18 (1 in 300,000 trillion)

JFK Calc: Called to Testify

**DEALEY PLAZA**

20 Suspicious deaths: 13 unnatural, 14 testified at Warren Commission

Witnesses; Probability of Unnatural Death

300; 3.60E-10 (1 in 2.7 billion)

400; 1.06E-08 (1 in 90 million)

500; 1.36E-07 (1 in 7 million)

600; 1.02E-06 (1 in 1 million)

JFK Calc: Dealey Plaza

**HSCA – 1977**

Suspicious deaths of 7 FBI officials called to testify in 6 month period

Official cause of death: 5 heart attacks, 2 accidents

FBI est.

called ; Probability

8 8.72E-18 (1 in 100,000 trillion)

20 5.22E-15 (1 in 200 trillion)

50 3.07E-12 (1 in 300 billion)

100 3.68E-10 (1 in 1 billion)

**LONDON SUNDAY TIMES ACTUARY**

Calculated a 1 in 100,000 trillion probability of 18 material witness deaths (13 unnatural) in the three years following the assassination (actually over 40).

Weighted average unnatural mortality rate: 0.000209

Witnesses; probability of at least 13 unnatural deaths

454; 9.83 E-18 (1 in 100,000 trillion)

600; 3.36 E-16 (1 in 3,000 trillion)

800; 1.25 E-14 (1 in 80 trillion)

1000; 2.00 E-13 (1 in 5 trillion)

1200; 1.89 E-12 (1 in 500 billion)

1500; 2.85 E-11 (1 in 35 billion)

2000; 8.76 E-10 (1 in 1 billion)

5000; 1.99 E-05 (1 in 50,000)

10000; 7.07 E-03 (1 in 140)

References:

Michael Benson: Who’s Who in the JFK Assassination 1400+ JFK-related individuals (97 suspicious deaths).

John Simkin: Spartacus Educational JFK Index 656 JFK-related individuals (66 suspicious deaths).

Jim Marrs: Crossfire

Richard Belzer and David Wayne: Hit List

Craig Roberts: Dead Witnesses

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Richard Charnin

June 19, 2015

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The following is a summary of Griffith’s key points in his 1997 essay.

http://johnfitzgeraldkennedy.net/evidenceofalterationinthezapruderfilm.htm

**GRIFFITH’S KEY POINTS**

**What follows are some of the indications that the Zapruder film has been altered. By “altered” I mean that certain frames have been removed and that others are composites. Why was the film altered? To remove episodes and images that clearly showed there were more than three shots (at least one from the front) and therefore that there were multiple gunmen involved in the shooting.
**

**The Limo Stop**

* Numerous witnesses, over 40, including the escort patrolmen to the rear of the limousine, said the limousine stopped or slowed down drastically for a second or two. This event is not seen in the Zapruder film; in fact, the limousine never comes close to performing this action in the current film.

**Impossible timings**

* In Z353-356 we see Malcolm Summers diving to the ground. Summers is to the right of James Altgens. In Z353 Summers’ left leg is extended most of the way out. But, in the very next frame, Z354, amazingly, the foreleg is bent markedly backward. Can anyone flex their foreleg to that degree so quickly? In 1/18th of a second?

* Another seemingly impossible action in the Zapruder film is the extremely rapid and precise movement of Charles Brehm’s son in Z277-287. In Z277 Brehm junior is standing behind his father. Then, from Z277-287, or in just over half a second, he bolts out from behind his father and comes to stand beside him, clapping his hands no less.

**JFK reaction**

* Several witnesses said Kennedy was knocked visibly forward by a shot to the head, and Dan Rather reported seeing this event when he viewed the film the day after the shooting. No such motion of the head is now visible in the film, only the split-second forward movement from Z312-313, which no one could have noticed.

* Former FBI official and J. Edgar Hoover aide Cartha DeLoach recently provided further evidence of alteration in the Zapruder film (albeit unintentionally and unknowingly, I’m sure). DeLoach recalls in his book HOOVER’S FBI that he watched the Zapruder film at FBI HQ the day after the shooting and that he saw Kennedy “PITCHING SUDDENLY FORWARD” in the film. No such motion, of course, is seen in the current film.

* Special Agent George Hickey, riding in the follow-up car, said the final shot made Kennedy “fall forward and to his left.”

* William Newman, who was standing on the Elm Street sidewalk right in front of the grassy knoll and who had one of the best views of the shooting, tried to tell New Orleans District Attorney Jim Garrison that JFK was knocked forward and to the left as if struck by a baseball bat, but Garrison wouldn’t believe him because the event wasn’t in the film.

I believe the above is good evidence that the original Zapruder film showed Kennedy being knocked rapidly forward. How do defenders of the film’s authenticity explain this testimony?

**The head snap**

*The violent, dramatic backward head snap in Z313-323, which for so many years was thought to be concrete proof of a shot from the front, actually constitutes further evidence of alteration. It has been established that no bullet striking the front of the skull could have caused the backward head snap. However, no bullet striking from behind could have caused this motion either. Warren Commission supporters have put forth two theories to explain how a bullet striking from behind might have caused the head snap, the jet-effect theory and the neuromuscular-reaction theory. Both theories are untenable.

So if neither a bullet from the front nor a bullet from behind could have caused the head snap, what caused it? So how can we explain it? Dr. David Mantik, who holds a doctorate in physics, suggests that what we now see as the head snap was originally a much slower motion and was actually the action of Jackie lifting her husband back up to look at him.

**Visual anomalies**

* Seemingly impossible inconsistencies occur in the streaking of background figures in relation to the camera’s movement. Mathematician Daryll Weatherly’s vector analysis of image streaking constitutes powerful evidence of alteration in the Zapruder film.

* A white spot on the grass behind the limousine is seen to behave in an unnatural manner. When the spot’s width is measured in relation to the camera’s tracking, the spot should be at its smallest when the image is at the left edge of the frame. But it doesn’t do this. On some occasions, the spot’s width is two to three times what it should be.

* The head turn of the driver, William Greer, from Z315-317 is too fast–it seems to be well beyond human capability. His head turns about 165 degrees in six frames, or in only 1/3rd of a second.

**Blood and brain splatter to the left rear**

* At least four witnesses saw blood and brain from Kennedy’s skull blow out toward the rear of the limousine. Blood and brain splattered onto the left side of the follow-up car’s windshield and onto the driver’s arm. A considerable amount of blood and brain also splattered onto the two patrolmen who were riding to the limousine’s left rear. At least one of those witnesses specified that the brain matter blew out from the back of the skull, and dozens of witnesses, including doctors and nurses, saw a large hole in the right rear part of President Kennedy’s head. In the Zapruder film no blood or brain is seen to spray backward. (It cannot be said that the right frontal explosion of blood and brain, which is itself suspect, caused all the blood splattering. In the Zapruder film the right-frontal spray blows mainly forward, and also up and toward the camera, and quickly dissipates–in fact it dissipates in no more than three frames. This effusion of spray could not have caused all of the blood splattering that occurred.)

**Right-rear head exit wound**

*Kinney’s description of a large, blown-out right-rear exit wound matches the reports given by numerous Parkland doctors and nurses and by several witnesses at the autopsy. Also, his account of particulate matter exploding out the back of the skull and landing on his windshield and left arm agrees with Patrolman Bobby Hargis’s report that the head shot sent blood and brain flying toward him so fast that when it struck him he initially thought he himself had been hit and that the debris got all over his motorcycle and uniform (in an interview he gave a few years ago, Hargis described the head shot as an “explosion”). Hargis, of course, was riding to the left rear of the limousine.

*Another example is the account of surveyor Chester Breneman, who was allowed to study enlargements of Zapruder frames to aid him in determining locations and distances. Breneman insisted that on some of the frames he saw a blob of blood and brain blow out from the back of Kennedy’s head. No such event is visible on the current film. (As mentioned, some witnesses in the plaza likewise saw blood and brain blown backward.)

**One frame right-frontal explosion**

* The bloody spray from the right-frontal explosion that is seen in the film blows upward, forward, and also toward the camera, and is really clearly visible for only one frame, and dissipates in two to three frames–or in no more than 1/6th of a second. Yet, in films of two ballistics tests the resulting spray is visible for multiple frames. In other words, the right-frontal effusion in the Zapruder film seems to disappear too quickly, with unnatural speed.

**More anomalies**

* There is a “remarkably symmetric” plus sign at the center of Elm Street in Z028 (Z28). This might have been used as a register mark for aligning the film when it was being copied by those who altered the film.

* There are magnification anomalies in the film for which there appears to be no credible natural or innocent explanation. One clear example of this is the measured width between the two posts on the back side of the Stemmons Freeway sign from Z312-318. This distance increases by over 12 percent in only six frames. Yet, from Z191-207 the interval remains constant.

**Location of start of film**

*Abraham Zapruder told CBS News that he began filming as soon as the President’s limousine turned onto Elm Street from Houston Street, as one would logically expect him to have done. But the present Zapruder film begins with the limousine already on Elm Street at Z133. On the day after the assassination, Dan Rather of CBS News watched what was quite possibly an earlier version of the film. Rather reported that in the film he watched that day the limousine “made a turn, a left turn, off Houston Street onto Elm Street.” Again, no such event is now seen in the film.

**Why forge the rapid head snap?**

Before I conclude, I would like to address two questions that have been raised by those who deny alteration: Why would the forgers, who were presumably trying to conceal or remove evidence of multiple gunmen and of shots from the front, produce an altered film that included the rapid backward head snap seen in the current film? And, why would the forgers have produced a film that contained indications of more than three shots? My answer to both of these objections is twofold:

**One, they do not explain the evidence of alteration. If there is scientific proof of alteration, then these philosophical objections must be rejected. **

**Two, I do not believe the forgers were at all satisfied with the results of their tampering. I think they had to create the backward head snap because they had to remove images that were even more unacceptable and problematic.**

We must keep in mind that the Zapruder film was suppressed from public view for over a decade. In short, I believe the forgers concluded that even after all of their editing the film was still unacceptable, and that this is why the film was suppressed for so long.

**Extensive editing**

A strong case can now be made for extensive editing of the Zapruder film. In fact, the conclusion seems inescapable–the film was deliberately altered. No other explanation is in the same league, in terms of explanatory power, for the myriad of anomalous characteristics that are seen everywhere in this case. Many frames were excised, some individual frames were extensively altered, others were changed only enough to fill in for missing frames, and others were left alone. . . .

**Too many anomalies to dismiss**

Even if some of the apparent technical anomalies in the Zapruder film can be explained, strong indications of tampering would still remain. To put it another way, if opponents of alteration are able to explain the absence of background streaking in certain frames, the magnification anomalies, the odd behavior of the white spot, and other seeming difficulties, would this establish the film’s authenticity? No.

Do we dismiss..

1-the witnesses who reported the limousine stopped or slowed drastically?

2-the witnesses who saw blood and brain blown visibly to the rear?

3-the fact that the backward head snap is physically impossible according to everything we know about physics and the human body?

4-the fact that Zapruder said he filmed the motorcade from the time it turned onto Elm Street?

5-the fact that Brehm’s son is positioned behind his father one moment but half a second later is standing calmly clapping at his side?

6-the fact that the 12/5/63 Secret Service survey placed the last shot at Z358 and that this placement matches the testimony of Emmett Hudson and James Altgens regarding the explosive head shot?

**Questions**

The numerous indications of alteration in the Zapruder film naturally raise some disturbing questions. **The answer to the question of why the film was altered is fairly apparent–to conceal obvious evidence of a frontal shot, of multiple gunmen, and of more than three hits.** But, who performed the alteration? Whoever they were, they were very well connected (so as to gain access to the film) and had at their disposal considerable technical expertise. It would seem self-evident that those who altered the Zapruder film were either working with or following orders from the men who were responsible for the assassination of President Kennedy.

**Doug Horne (Chief ARRB Analyst for Military Records)**

The following post contains a link to an essay by Doug Horne and to a video on the Z-film chain of custody.

https://richardcharnin.wordpress.com/2014/02/04/jfk-assassination-mathematical-proof-that-the-zapruder-film-was-altered/

Horne interviews Dino Brugioni (a photo interpretation expert) who viewed the original Zapruder film on the weekend following the assassination. http://assassinationofjfk.net/the-two-npic-zapruder-film-events-signposts-pointing-to-the-films-alteration/

Horne writes:

*“As discussed earlier in this paper, Dino Brugioni opined during his July 9, 2011 interview with the author that the head explosion seen today in the extant Zapruder film is markedly different from what he saw on 11/23/63, when he worked with what he is certain was the camera-original film. The head explosion he recalls was much bigger than the one seen today in frame 313 of the extant film (going “three or four feet into the air”); was a “white cloud” that did not exhibit any of the pink or red color seen in frame 313 today; and was of such a duration that he is quite sure that in the film he viewed in 1963, there were many more frames than just one graphically depicting the fatal head shot on the film he viewed in 1963. Mr. Brugioni cannot, and does not, accept frame 313 of the extant Zapruder film as an accurate or complete representation of the fatal head shot he saw in the camera-original Zapruder film on the Saturday evening following President Kennedy’s assassination”*.

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Richard Charnin

Updated: June 27, 2015

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**

This is a summary update of a previous post on John Simkin’s Index of 656 JFK-related individuals. https://richardcharnin.wordpress.com/2013/12/25/jfk-related-unnatural-and-suspicious-deaths-in-the-jfk-calc-spreadsheet-and-simkins-jfk-index/

Simkin’s Index: http://spartacus-educational.com/JFKindex.htm

The list is in JFK Calc for reference and probability calculations.

https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdDFSU3NVd29xWWNyekd2X1ZJYllKTnc#gid=81

Sixty-six (66) individuals in the JFK Index are also included among 122 suspicious deaths in the JFK Calc spreadsheet. Of the 122 suspicious deaths in JFK Calc, approximately 67 were called to testify in four investigations. The fact that both lists contain more than 60 names is proof that they are relevant. Naysayers can no longer make the ridiculous argument that they are not JFK-related.

Of the 66 suspicious deaths in Simkin’s index, 42 were OFFICIALLY RULED UNNATURAL, including 22 homicides. Only 8 unnatural deaths and ONE homicide would be expected in a random group of 656 from 1964-78 based on historical mortality rates.

**The probability of 22 homicides among the 656 is 1 in 150 billion trillion (6.4E-24). If we triple the 0.000084 national homicide rate, the probability of 22 homicides is higher: 1 in 23 trillion (4.3E-14).
**

But these probabilities are too HIGH. Statistical expectation indicates that of the 45 suspicious deaths (officially ruled accidents, suicides, heart attacks and sudden cancers) approximately 26 were HOMICIDES. So there were approximately 48 homicides among the 66 suspicious deaths.

**The probability of 48 homicides from 1964-78 among the 656 in the JFK Index is 1 in a trillion trillion trillion trillion trillion!
**

The Simkin JFK Index of 656 key individuals consists of 4 categories.

Suspicious deaths include:

10 of 190 Important Figures;

15 of 86 Important Witnesses;

5 of 206 Investigators, Researchers and Journalists;

36 of 174 Possible Conspirators

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