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2016 True Vote Models in Confirmation: Party-ID and Returning 2012 Voters

2016 True Vote Models in Confirmation: Party-ID and Returning 2012 Voters

Richard Charnin
Aug.28, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll
Reclaiming Science: The JFK Conspiracy
LINKS TO  POSTS
Last 3 Elections: Exact Forecast of Electoral Vote

Pollsters no longer ask the question “How did you vote in the last election”? Why? Because posing the question provides an analyst with data to indicate election fraud.

In 1972, 1988, 1992, 2004 and 2008, in order to match the recorded vote (SOP), the exit pollsters (who work for the MSM) required a greater turnout of Bush voters from the prior election than were still alive. This is a MATHEMATICAL IMPOSSIBILITY. If the exit poll is impossible, the recorded vote it was forced to match must also be impossible. That is proof of fraud. It’s why the exit pollsters (the MSM) no longer ask the question “Who Did You Vote for in the Last Election”?

The Exit Poll Smoking Gun: “How did you vote in the last election”?

These 2016 models calculate a true vote estimate for each state.
Model 1: Obama and Romney voter turnout in 2016.
Model 2: Gallup Party-ID voter affiliation. Used in the 2016 forecast model.

Base case vote shares were identical in each model. The shares were forced to match the recorded vote assuming equal 95% turnout. To calculate the True Vote, returning Obama voter turnout in 2016 was adjusted to 89%. The assumption is that 6% of Obama voters were Bernie Sanders 2016 primary voters who did not return to vote in the presidential election.

Important note: Since the vote shares were forced to match a likely fraudulent recorded vote (the Mainstream Media was heavily biased for Clinton), the following results are conservative. Trump probably did at least 2% better than indicated in the base case calculations. View the sensitivity analysis.

So how can we determine Obama and Romney returning voter turnout in 2016? Where can we get that information? Why don’t the exit pollsters provide the data? Should we just guess or estimate turnout based on historical elections? I chose the latter.

Using the prior 2012 vote as a basis, a voter mortality estimate is factored in. Approximately 4% of voters pass between each election (1% annual mortality). The simplest approach is to assume an equal 95% turnout of Obama and Romney voters still living. Now we have a plausible approximation of the (unknown) mix of returning voters. Since we know the current election recorded vote, the number of new 2016 voters who did not vote in 2012 can be calculated: DNV = 2016 total vote – returning 2012 voters.

The first step is to force the candidate shares of returning voters to match the recorded vote assuming equal 95% turnout.

In the True Vote calculation, the percentage of returning Obama voters was lowered to 89% to reflect disenchantment among Bernie Sanders’ primary voters who did not vote in the general election or voted for Jill Stein or Donald Trump.

To view the sensitivity of the True Vote to Trump shares of returning Obama and Romney voters, a matrix of total vote shares is calculated in 1% increments around the Trump base case estimate. There are 25 vote share scenario combinations in the 5×5 matrix. Corresponding matrices of Clinton shares and vote margins are also included. The base case is in the central cell.

2016 Presidential State Election Model Summary
https://docs.google.com/spreadsheets/d/10dlTnin814phKJWjYdkG-ujNKak3zo6ywIP0u0-TGFg/edit#gid=667189511

Recorded Vote
Clinton: 48.25-46.17% (2.83 million votes)
Trump: 306 Electoral Votes

Model 1
(returning 2012 voters)
2012 recorded vote: Obama 51.03-Romney 47.19% (4.98 million)
2016 voter turnout: Obama 89%, Romney 95%
Trump: 47.8-46.7% (1.51 million votes)
Trump: 323 Electoral Votes

Model 2
Gallup National Voter Affiliation Survey: 32D-28R-40I (state adjusted)
1. Trump and Clinton split the undecided vote:
Trump: 46.8-45.8% (1.35 million votes)
Trump: 307 Electoral Votes

2. Trump had 75% of the undecided vote:
Trump: 48.1-44.5% (4.97 million votes)
Trump: 352 Electoral Votes

The National Model
https://docs.google.com/spreadsheets/d/10dlTnin814phKJWjYdkG-ujNKak3zo6ywIP0u0-TGFg/edit#gid=1768941212

Vote share sensitivity analysis (Model 1)
-Best case: Trump had 92% of returning Romney voters and 9% of Obama voters
Trump by 49.4-45.0% (5.98 million votes)
-Base case: Trump had 90% of returning Romney voters and 7% of Obama voters
Trump by 47.8-46.7% (1.51 million votes)
-Worst case: Trump had 88% of returning  Romney voters and 5% of Obama voters
Clinton by 48.3-46.1% (2.97 million votes).

Mathematical Proof: the 2004 election was stolen
The 2004 National Exit Poll was impossible as it was forced to match the recorded vote (Bush 50.7-48.3%) using an impossible number of returning Bush 2000 voters. It indicated that 52.6 million (43% of the 2004 electorate) were returning Bush 2000 voters and just 45.3 million (37%) were returning Gore voters. But Bush had just 50.5 million recorded votes in 2000. It indicated an impossible 110% turnout of living 2000 Bush voters in 2004.

2004 Election Fraud
https://richardcharnin.wordpress.com/2015/10/30/2004-election-fraud-overwhelming-statistical-proof-that-it-was-stolen/

2004 Spreadsheet 1
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdFIzSTJtMTJZekNBWUdtbWp3bHlpWGc&usp=sheets_web#gid=7

2004 Spreadsheet 2
https://docs.google.com/spreadsheets/d/1x2WCPJautd_eZPIfkmW9W9vD2p1Zu0ZlvgqV_gUwLNM/edit#gid=13

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Posted by on August 28, 2017 in 2016 election, True Vote Models

 

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2016 State Presidential True Vote Model

2016 State Presidential True Vote Model

Richard Charnin
Aug. 25, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll
Reclaiming Science: The JFK Conspiracy
LINKS TO  POSTS
Last 3 Elections: Exact Forecast of Electoral Vote

This is an analysis of the presidential vote in each of the 50 states and DC. To view the calculations for any state, just click the State tab. No input is required.

Since the 2012 election,  exit pollsters no longer provide the crosstab Who did you vote for in the previous election?  Like all crosstabs, it was matched to the recorded vote.  The  Trump, Clinton and 3rd party shares of returning Obama and Romney voters are not available. However we can closely approximate the crosstab  by calculating the shares required to match the recorded vote.

National Result
Clinton won the recorded vote by 2.87 million (48.25-46.14%).
Trump had 306 electoral votes.
Trump won the True Vote by 1.69 million (47.61-46.37%). He had 323 electoral votes.

Note:  Trump must have done better than the model indicates, since it uses vote shares derived to match the recorded vote that was biased for Clinton.

Assumptions

  • Recorded vote: 95% turnout of Obama and Romney voters in 2016. Vote shares are forced to match the state recorded vote.
  • True Vote: 89% turnout of Obama voters and 95% turnout of Romney voters.  Vote shares remain the same as used in the recorded vote.  The assumption is that 6% of Obama voters who were for Bernie Sanders in the primary did not return to vote in the presidential election. But an unknown number voted for Jill Stein and Donald Trump.

View the data and calculations for each state.  For instance, click the FL tab.
https://docs.google.com/spreadsheets/d/10dlTnin814phKJWjYdkG-ujNKak3zo6ywIP0u0-TGFg/edit#gid=517146616 

This sheet contains a Recorded and True Vote summary for  each state.  https://docs.google.com/spreadsheets/d/10dlTnin814phKJWjYdkG-ujNKak3zo6ywIP0u0-TGFg/edit#gid=667189511

Sensitivity Analysis
To see the effects of  changes in returning vote share assumptions, view the Sensitivity Matrix. It contains 25 scenarios of Trump and Clinton vote shares in one percent increments above and below the base case. The base case is the central cell  of the matrix.

Note: the difference between Recorded and True Vote is assumed strictly due to 2012 voter turnout in 2016. Granted, this is a simplifying assumption which is obviously not the case for each state.

 
 

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The 2016 True Vote Model (TVM)

Richard Charnin
Aug. 20, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll
LINKS TO  POSTS

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

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

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

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

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

Case 2. Base case TRUE VOTE
Estimate: 89% turnout of Obama, 95% turnout of Romney voters.
Trump wins by 48.7-45.7% (142,000 votes)
Assumption: Approximately 147,000  of Sanders MI primary voters who voted for Obama did not return to vote in the presidential election.

True Vote Sensitivity Analysis
View a 25 scenario matrix for 5 Trump shares of returning Obama and 5 Trump shares of returning Romney voters. Trump wins 24 of 25 scenarios.

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

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

 
 

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

Richard Charnin
Aug, 4, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll
LINKS TO  POSTS

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

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

National True Vote Model: Basic Methodology

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

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

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

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

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

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

 
1 Comment

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

 

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2016 National Exit Poll vs. True Vote Model: How did you vote in the 2012 election?

Richard Charnin
July 9, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll
LINKS TO  POSTS

The 2008 presidential election was the last one in which the National (NEP) and state exit polls asked “How Did You Vote in the Last Election?”. A plausible reason is that the question provided clear proof of fraud in all elections from 1988-2008. The How Voted crosstab matrix required more returning Bush voters than were still alive in order to match the bogus recorded vote in 1992 (119% turnout), 2004 (110%) and 2008 (103%). Conversely, the True Vote Model, which used a feasible estimate of returning voters, confirmed the unadjusted, pristine state and national exit polls.

Since the “How Voted” question was not asked, we can derive a crosstab to match the 2016 recorded vote using assumptions for 2012 returning voter turnout and 2016 vote shares.

General Assumption: 1% Annual voter mortality

2016 Estimated National Exit Poll assumptions
Equal 96% turnout of living 2012 Obama and Romney voters.
Clinton wins 87% of returning Obama and 7% of returning Romney voters.
Trump wins 7% of returning Obama and 88% of returning Romney voters.
Trump wins new voters by 48-47%.
Clinton wins by 2.9 million recorded votes, 48.3-46.2%.

2016 True Vote Model assumptions
Voter turnout: 92% of living Obama voters and 96% of Romney voters
Clinton wins 82% of returning Obama and 7% of returning Romney voters
Trump wins 10% of returning Obama and 88% of returning Romney voters
New voters: Trump and Clinton 45% tie
Trump wins the base case scenario by 3.6 million votes, 47.8-45.1%.

2016 TVM rationale
– 96% Romney voter turnout vs. 92% for Obama: approximately 2.5 million living Obama voters were angry Sanders voters who did not vote.
– Clinton’s 82% share of returning Obama voters: approximately 2.6 million Obama voters were angry Sanders voters who defected to Jill Stein, Trump and Johnson.

NATIONAL EXIT POLL – is always forced to match the recorded vote
“HOW VOTED IN 2012” was not asked in the 2016 NEP.
It would have looked something like this…
2016….. Mix Clinton Trump Other
Obama…. 44.6% 87% 7% 6%
Romney… 41.2% 7% 88% 5%
Other…… 1.5% 45% 45% 10%
DNV….. 12.6% 47% 48% 5.4%

Total…. 100% 48.3% 46.2% 5.5%
Vote…. 136.2 65.7 62.9 7.6

TRUE VOTE
2012….. Mix Clinton Trump Other
Obama…. 42.7% 82% 10% 8%
Romney… 41.2% 7% 88% 5%
Other…… 1.5% 45% 45% 10%
DNV…… 14.5% 45% 45% 10%

Total…. 100% 45.1% 47.8% 7.1%
Vote…. 136.2 61.5 65.1 9.7

Sensitivity analysis
The tables display Trump’s total vote share and margin over a range of 25 scenarios of his  shares of returning Obama (8-12%) and Romney voters (86-90%). He wins 24 of the 25 scenarios. In the worst case scenario, Trump loses by 1 million votes (46.9-46.1%). In the best case, he wins by 8 million (49.5-43.5%). Trump wins the base case scenario by 3.6 million votes, 47.8-45.1%.

View the spreadsheet:
https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1768941212

 
 

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Proving Election Fraud: The PC, Spreadsheets and the Internet

Proving Election Fraud: The PC, Spreadsheets and the Internet

Richard Charnin
Mar. 31, 2016

Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll 
LINKS TO POSTS

Election Fraud Overview

This post is an overview of major advances in technology which ultimately proved that election fraud is systemic. There were three major turning points:

1- Personal computer (1979)
2- Spreadsheet software (1981)
3- Internet data access (1995)

A BRIEF HISTORY OF COMPUTERS AND SPREADSHEET TECHNOLOGY

Before the advent of the personal computer,  mainframes and minicomputers were programmed by professionals  in major corporations. Programming was hard and time consuming. Computers were used by scientists, engineers, investment bankers and other analytical professionals.

In 1965, my first job was as a numerical control FORTRAN programmer in the aerospace industry. The 7094 IBM mainframe  was a 512k machine which required a full floor of office space. It was on rental from the U.S. Navy.

Computers grew in power and were smaller in size during the 1970s. As manager of software development in Investment Banking  at Merrill Lynch on Wall Street . I used FORTRAN to develop financial models.

In the late 1970s, personal computers were considered as toys- until the first spreadsheets appeared. All of a sudden,  one could do simple calculations without having to write complex programs. Lotus 1-2-3 had limited programming features (“macros”). I immediately converted  FORTRAN financial programs to spreadsheets  with graphics capabilities. As a consultant to major domestic and foreign  corporations I switched to Excel in 1995 . Excel was used with C++ for advanced financial data base and derivatives models.

MATRIX OF DECEIT

A matrix is just a table (rectangular array) of numbers. In a spreadsheet, the table consists of data in cells (column, row). Basic arithmetic operations applied to the matrix are sufficient to prove election fraud. 

Actual, raw unadjusted exit poll results are changed in all matrix crosstabs (demographics) to conform to the recorded vote. The crosstab “How Did You Vote in the previous  election?” has proved to be the Smoking Gun in detecting presidential election fraud from 1988-2008. 

2000

Gore won the unadjusted National Exit Poll and State Exit Poll aggregate which indicated that he won by 3-5 million votes – not the 540,000 recorded. But the National Exit Poll  was forced to match the recorded vote. The election was stolen – big time.

2000 Unadjusted National Exit Poll (13,108 respondents)
Total Gore Bush Nader Other
13,108 6,359 6,065 523 161
48.51% 46.27% 3.99% 1.23%

 

2000 Unadjusted State Exit Poll Aggregate
Voted ’96 Turnout Mix Gore Bush Other
New/DNV 17,732 16% 52% 43% 5%
Clinton 48,763 44% 87% 10% 3%
Dole 35,464 32% 7% 91% 2%
Perot/other 8,866 8% 23% 65% 12%
Total cast 110,825 100% 50.68% 45.60% 3.72%
110,825 56,166 50,536 4,123

 

2000 National Exit Poll (forced to match recorded vote)
Voted ’96 Turnout Mix Gore Bush Other
New/DNV 18,982 18% 52% 43% 5%
Clinton 42,183 40% 87% 10% 3%
Dole 35,856 34% 7% 91% 2%
Other 8,437 8% 23% 65% 12%
Total 105,458 100% 48.38% 47.87% 3.75%
105,458 51,004 50,456 3,998

2004

The Final National Exit Poll was forced to match the recorded vote (Bush won by 3 million). The election was stolen.

Kerry won the unadjusted National Exit Poll and  State Exit Poll aggregate by 6 million votes. The True Vote Model (assuming a plausible estimate of returning 2000 election voters)  indicated that he won by 10 million votes with a 53.7% share.  

                                           2004 Unadjusted National Exit Poll (13,660 respondents)
Kerry Bush Other
13,660 7,064 6,414 182
share 51.71% 47.0% 1.3%

 

                   2004 Unadjusted National Exit Poll
                             (implausible 2000 returning voters; Gore won by 4-6m)
2000 Voted Mix Kerry Bush Other
DNV 23,116 18.38% 57% 41% 2%
Gore 48,248 38.37% 91% 8% 1%
Bush 49,670 39.50% 10% 90% 0%
Other 4,703 3.74% 64% 17% 19%
Total 125,737 100% 51.8% 46.8% 1.5%
125,737 65,070 58,829 1,838

 

2004 Final Adjusted National Exit Poll
                      (Impossible Bush 2000 voter turnout; forced to match recorded vote)
2000 Turnout Mix Kerry Bush Other Alive Turnout
DNV 20,790 17% 54% 44% 2%
Gore 45,249 37% 90% 10% 0% 48,454 93%
Bush 52,586 43% 9% 91% 0% 47,933 110%
Other 3,669 3% 64% 14% 22% 3,798 97%
Total 122,294 100% 48.27% 50.73% 1.00% 100,185 94%
59,031 62,040 1,223

2008

Obama won the unadjusted National Exit Poll by 61-37% (a 30 million vote margin). He won the  State Exit Poll aggregate 58-40% (a 23 million vote margin). But the Final National Exit Poll was forced to match the recorded 9.5 million vote margin. The landslide was denied.

                                      2008 Unadjusted National Exit Poll (17,836 respondents)
Obama McCain Other
17,836 10,873 6,641 322
100% 61.0% 37.2% 1.8%

 

                      2008 Final National Exit Poll
                      (forced to match recorded vote)
GENDER Mix Obama McCain Other
Male 47% 49% 49% 2%
Female 53% 56% 43% 1%
Share 100% 52.87% 45.59% 1.54%
Votes(mil) 131.463 69.50 59.94 2.02

 

2008 Unadjusted National Exit Poll
 (plausible returning 2004 voter mix)
Voted 2004 2008 Exact match to TVM & unadj state exit pollls
2004 Implied Votes Mix Obama McCain Other
DNV 17.66 13.43% 71% 27% 2%
Kerry 50.18% 57.11 43.44% 89% 9% 2%
Bush 44.62% 50.78 38.63% 17% 82% 1%
Other 5.20% 5.92 4.50% 72% 26% 2%
Total 131.46 100% 58.00% 40.35% 1.65%
Votes 131.463 76.25 53.04 2.17

 

Adjusted 2008 National Exit Poll
(forced to match recorded vote with
Voted 2004 2008 impossible returning 2004 voters)
2004 Implied Votes Mix Obama McCain Other
DNV 17.09 13% 71% 27% 2%
Kerry 42.53% 48.64 37% 89% 9% 2%
Bush 52.87% 60.47 46% 17% 82% 1%
Other 4.60% 5.26 4% 72% 26% 2%
Total 131.46 100% 52.87% 45.60% 1.54%
Votes 131.463 69.50 59.95 2.02

2004 Sensitivity Analysis

How is Kerry’s vote share effected by changes in vote share assumptions? Consider the following matrices (tables). He wins all plausible scenarios. 

https://docs.google.com/spreadsheets/d/1_foUi89DGNmwspKRFTgh5tOjjba4el2GLJEJLK-M2V8/edit#gid=0

2004 True Vote Model
                    (Plausible 2000 returning voter mix)
2000 Voted Mix Kerry Bush Other
DNV 22,381 17.8% 57% 41% 2%
Gore 52,055 41.4% 91% 8% 1%
Bush 47,403 37.7% 10% 90% 0%
Other 3,898 3.1% 64% 17% 19%
Total 125,737 100% 53.6% 45.1% 1.4%
67,362 56,666 1,709
                           Kerry share of returning Gore voters
89.0% 90.0% 91.0% 92.0% 93.0%
Share of returning Bush 2000                                              Kerry Vote Share
12.0% 53.2% 53.6% 54.1% 54.5% 54.9%
11.0% 52.9% 53.3% 53.7% 54.1% 54.5%
10.0% 52.5% 52.9% 53.3% 53.7% 54.1%
9.0% 52.1% 52.5% 52.9% 53.3% 53.7%
8.0% 51.7% 52.1% 52.5% 52.9% 53.4%
      Margin (000)    
12.0% 9,827 10,859 11,892 12,924 13,956
11.0% 8,871 9,903 10,935 11,967 13,000
10.0% 7,914 8,946 9,978 11,011 12,043
9.0% 6,957 7,990 9,022 10,054 11,086
8.0% 6,001 7,033 8,065 9,097 10,130
                    Kerry share of New voters (DNV)
Kerry share of 53.0% 55.0% 57.0% 59.0% 61.0%
returning Bush 2000 voters   Kerry Vote Share  
12.0% 53.3% 53.7% 54.1% 54.4% 54.8%
11.0% 53.0% 53.3% 53.7% 54.0% 54.4%
10.0% 52.6% 52.9% 53.3% 53.6% 54.0%
9.0% 52.2% 52.6% 52.9% 53.3% 53.6%
8.0% 51.8% 52.2% 52.5% 52.9% 53.2%
      Margin    
12.0% 10,098 10,995 11,892 12,789 13,686
11.0% 9,141 10,038 10,935 11,832 12,729
10.0% 8,184 9,081 9,978 10,876 11,773
9.0% 7,228 8,125 9,022 9,919 10,816
8.0% 6,271 7,168 8,065 8,962 9,859
Kerry Win Probability  53.0% 55.0% 57.0% 59.0%  61.0%
Win Prob  (3% MoE)
12.0% 99.6% 99.8% 99.9% 100.0% 100.0%
11.0% 99.2% 99.6% 99.8% 99.9% 100.0%
10.0% 98.4% 99.2% 99.6% 99.8% 99.9%
9.0% 97.2% 98.4% 99.1% 99.6% 99.8%
8.0% 95.1% 97.0% 98.3% 99.1% 99.5%
 

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Exit Pollsters at Edison Research: Never Discuss the Election Fraud Factor

Richard Charnin
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

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/

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

In all exit polls, the pollsters adjust returning voters and/or vote shares 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.

The following is a summary of the major points in the Edison Research article. 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.
There is nothing intricate about forcing unadjusted exit polls to match the recorded vote. It is quite simple. And it happens in every election.

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% (6 million votes). But Gore was held to a 48.4-47.9% (540,000 vote margin) 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. The expected EV is the product sum of the state win 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 the prior election returning voter mix in five presidential elections was mathematically and physically impossible. The exit polls indicate that there were more returning Nixon and Bush voters from the prior election than were actually still alive. This is absolute proof that the published exit polls were adjusted to match vote-miscounts. Garbage in, garbage out.

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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JFK Conspiracy and Systemic Election Fraud Analysis