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Election Fraud Models: Cumulative Vote Shares and True Vote Analysis

Election Fraud Models: Cumulative Vote Shares and True Vote Analysis

Richard Charnin
Aug. 2, 2015
WEB SITE

CVS and TVM analysis is confirmed by the following studies:
– A statistical study of precinct level data in US presidential elections reveals a correlation of large precincts and increased fraction of Republican votes.
http://arxiv.org/pdf/1410.8868.pdf

– Wichita State University engineering professor and statistician Beth Clarkson has accused three states — Wisconsin, Ohio, & Kansas — of voting irregularities that indicate a tampering of electronic voting machines. In her recently published journal article, she reviews the statistical anomalies in the three states — including laying out her entire mathematical methodology, inviting others to replicate the study. Clarkson has filed suit trying to gain full access to the ballots for an independent audit of the paper ‘hard copies.’
http://ivn.us/2015/07/20/report-2014-voting-machine-tampering-likely-wisconsin-ohio-kansas/

2014 Elections

Illinois Gov
https://richardcharnin.wordpress.com/2015/07/31/2014-illinois-governor-cumulative-vote-shares-and-exit-poll-anomalies/
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/edit#gid=1895227029

Florida Gov
https://richardcharnin.wordpress.com/2014/11/14/florida-2014-governor-true-voteexit-poll-analysis-indicates-fraud/
https://docs.google.com/spreadsheets/d/1H3nozgY4T-rdF-RrM8rw5K_FRsoULsGDdLVYpiFVNeQ/edit#gid=0

South Dakota
https://richardcharnin.wordpress.com/2015/01/02/south-dakota-2014-cumulative-vote-share-analysis/
https://docs.google.com/spreadsheets/d/11pw_YbGe9iidkziW1R8sks5HpseapEGpABD0l2Twebw/edit#gid=1519263458

Maryland Governor
https://richardcharnin.wordpress.com/2015/02/27/proving-election-fraud-cumulative-vote-share-analysis/
https://docs.google.com/spreadsheets/d/17SpMcLyJ0607RyasTG4tRqrFmyDEKmEG45DKGGLZFmA/edit?usp=sheets_home

Kansas Senate
https://richardcharnin.wordpress.com/2015/04/02/12370/
https://docs.google.com/spreadsheets/d/1D087y0AlsFiITeypDEk3W_c4P-O2iytQRCp85wFIw-Q/edit#gid=1367668624

Four Wisconsin Elections:a pattern of county unit ward vote share anomalies
https://richardcharnin.wordpress.com/2012/12/20/four-wisconsin-elections-a-pattern-of-county-unitward-vote-share-anomalies/

Wisconsin 2014 Gov
https://richardcharnin.wordpress.com/2014/11/12/wisconsin-2014-governor-true-voteexit-poll-analysis-indicates-fraud/
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdEhqXzdlbUhZT1Vic3RSQmU2cUVkc3c#gid=9

Wisconsin 2012 Walker Recall
https://richardcharnin.wordpress.com/2012/12/09/walker-recall-county-cumulative-vote-trend-by-ward-group/
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdF95dGdleVBSYkdISmplWVZXdXlQQ0E#gid=1

Wisconsin 2010 Senate
https://richardcharnin.wordpress.com/2015/07/23/wisconsin-2010-senate-true-vote-model-and-cumulative-vote-shares-indicate-feingold-won/
https://docs.google.com/spreadsheets/d/1tXw5LpgQrZjn_YFOkLoLqtQhIAco_V9EEApXvva58kE/edit

Wisconsin 2011 Supreme Court
https://richardcharnin.wordpress.com/2011/06/28/2011-wisconsin-supreme-court-true-vote-analysis/
https://docs.google.com/spreadsheets/d/1ziSkkHnYz-bVvAfHd_VciBBEUKQqFafJJjico4WbwTE/pubchart?oid=505176002&format=interactive
https://docs.google.com/spreadsheets/d/1ziSkkHnYz-bVvAfHd_VciBBEUKQqFafJJjico4WbwTE/edit#gid=1966172904

Presidential Elections
Historical Overview and Analysis of Election Fraud
https://richardcharnin.wordpress.com/2013/01/31/historical-overview-of-election-fraud-analysis/

2000 Florida: Duval County
https://docs.google.com/spreadsheets/d/1eiVf34eX9LSptAXZ-EvgCmW88JRjLu8Z5Bxfleg_RgQ/pubchart?oid=1722819743&format=interactive

2004 Ohio: Lucas County
https://docs.google.com/spreadsheets/d/1zcUZQ49a5fAmx2fomZ_xcCp2vDbCIitNKyfoQnVQKao/pubchart?oid=1403163968&format=interactive

2008 Wisconsin presidential
https://docs.google.com/spreadsheets/d/1ReruOWQ_DgUZFHAN6y0xFO6A25B2RtTsVxY3WyOMOoQ/edit#gid=0

 
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Posted by on August 2, 2015 in Uncategorized

 

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2014 Illinois Governor Cumulative Vote Shares and Exit Poll Anomalies

2014 Illinois Governor Cumulative Vote Shares and Exit Poll Anomalies

Richard Charnin
July 31, 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 2014 Illinois Governor spreadsheet contains precinct votes by county, True Vote Model and adjusted exit poll.

The following analysis indicates that Quinn may have won re-election.

Pre-election Polls: Quinn (Dem) led the final LV pre-election polls: 45.6-44.8%. Rauner (Rep) won by 170,000 votes (50.7-45.9%). LV polls are a subset of Registered Voter (RV) polls. Respondents deemed unlikely to vote (most of them Democratic) are eliminated from the full RV sample. RV polls usually match the Unadjusted exit polls and the True Vote Model. LV polls have closely matched the recorded vote.

Cumulative Vote shares: The largest counties all showed increasing Rauner vote shares as cumulative precinct vote totals increased, a major red flag.

Exit poll anomalies: The Governor exit poll matched the recorded vote to within 0.4%. It is standard procedure to adjust the poll to match the recorded vote.In the Party-ID category, Democrats led Republicans by 43-30%. But only 85% of Democrats voted for Quinn while 64% of Independents voted for Rauner.

True Vote Model: Quinn won 65 of 75 scenarios. His share of returning Obama voters ranged from 80-89%; returning Romney voters from 5-9%; new voters from 44-52%.

The True Vote Model

Assuming
1) Obama’s 2012 recorded 57% Illinois share
2) An 80% turnout of Obama and Romney voters
3) Quinn has just 84% of returning Obama voters
4) Quinn has 7% of returning Romney voters.
then Quinn wins by 50.8-47.5%, a 113,000 vote margin.

The built-in sensitivity analysis shows the effects of a range of voter turnout and vote shares assumptions. The basis is the 2012 presidential election. Quinn wins 72 of 75 scenarios. His share of returning Obama voters ranges from 80-89%; returning Romney voters from 5-9%; new voters from 44-52%.

A win probability matrix is displayed for 25 combinations of Quinn’s share of returning Obama and Romney voters. Quinn’s win probabilities range from 18% in the worst case to 100%. His win probability exceeds 50% in 24 of 25 scenarios.

Cumulative Vote Shares

The largest counties all showed increasing Rauner vote shares as the cumulative precinct vote totals increased – a major red flag.

Heavily Democratic Cook county had 1.3 million of the 3.6 million state voters.
Quinn had 75% of the first 100 thousand votes in the smallest Cook precincts,
72% of the first 500 thousand,
69% of the first 1 million,
64.8% of the total 1.3 million who voted in Cook county.

Exit Poll Anomalies

The Illinois Governor exit poll matched the recorded vote to within 0.4%.
But it is standard procedure to adjust the poll to match the recorded vote.
The following crosstabs reflect the recorded vote – not the True Vote:

Gender: Quinn led the female vote by 51-44%, an increase from his 49-44% share in the 2010 election.

Race: Minority voters were 9% of the vote, but the vote shares are missing.

Philosophy: Liberals comprised just 25% of the electorate. Quinn’s 80% share declined from 84% in 2010. His share of moderates declined from a winning 7% margin in 2010 to a 12% loss.

Party-ID: Self-identified Democrats led Republicans by 43% – 30%. But only 85% voted for Quinn? Independents voted for Rauner by 64-29%?

Education: Quinn won Post graduates by 55-43% (20% of the vote). But Rauner won College grads by 60-36% (31% of the vote). This is an implausible discrepancy.

Labor: Quinn had just 58%?

Senate Election: Durbin (D) easily won re-election by 55-43%.
But just 82% of Durbin voters voted for Quinn?

Note:
Cumulative Vote Share posts:
https://docs.google.com/document/d/1KU4D23gIamrsXb4pPnrIcoA3FjDkzqkeaX_kApIh1J0/pub

– A statistical study
 of 
precinct 
level 
data
 in 
US
 presidential 
elections 
reveals
 a 
correlation
 of
 large
 precincts 
and 
increased
 fraction
 of
 Republican
 votes. 
http://arxiv.org/pdf/1410.8868.pdf

– Wichita State University engineering professor and statistician Beth Clarkson has accused three states — Wisconsin, Ohio, & Kansas — of voting irregularities that indicate a tampering of electronic voting machines. In her recently published journal article, she reviews the statistical anomalies in the three states — including laying out her entire mathematical methodology, inviting others to replicate the study. Clarkson has filed suit trying to gain full access to the ballots for an independent audit of the paper ‘hard copies.’
http://ivn.us/2015/07/20/report-2014-voting-machine-tampering-likely-wisconsin-ohio-kansas/







Adams
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=1325736154&format=interactive

Champaign
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=269618494&format=interactive

Cook
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=694821319&format=interactive

DuPage
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=1407248476&format=interactive

Kane
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=333132230&format=interactive

Kankakee
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=1506081481&format=interactive

Lake
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=907532757&format=interactive

Madison
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=1410720243&format=interactive

McHenry
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=1879256266&format=interactive

Peoria
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=596243564&format=interactive

St.Clair
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=363120484&format=interactive

Will
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=596651451&format=interactive

Winnebago
https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/pubchart?oid=184351637&format=interactive

 
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Posted by on July 31, 2015 in Uncategorized

 

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A Simple 2004-2012 Electoral Vote Simulation Model

A Simple 2004-2012 Electoral Vote Simulation Model

Richard Charnin
July 27, 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 purpose of the Monte Carlo Electoral Vote Simulation Model is to calculate the probability of a candidate winning at least 270 Electoral votes.

The Total Electoral Vote is calculated using individual state projections. 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) as input to the Normal distribution.

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

The probability of winning the election is the ratio of winning simulation trials (at least 270 EV) to the total number of simulation trials (200).

The model contains the following 2-party vote shares:
2004- Kerry unadjusted state and national exit polls and recorded shares
2008- Obama Unadjusted state and national exit polls and recorded shares
2012- Obama state and national True Vote and recorded shares
(In 2012, 19 states were not exit polled)

Only ONE input (code 1-6) is required to indicate the election and method:
2004: 1- exit poll, 2- recorded votes
2008: 3- exit poll, 4- recorded votes
2012: 5- True vote, 6- recorded votes

In order to see the effects of changes to any of the 2004-2012 vote shares, a blank column is inserted so that actual vote shares can be overridden.

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

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

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.

In 2004, Kerry had a 48.3% recorded share, 252 EV and lost by 3 million votes. But the unadjusted state and national exit polls indicate that he had 51-52% and won by 5-6 million votes with 349 EV. The True Vote Model indicates that he had 53.5% and won by 10 million 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.

The 2008 TVM exactly matched Obama’s 58% share of the unadjusted state exit polls: he won by 23 million votes (not the 9.5 million recorded) and had 420 electoral votes. Obama led the unadjusted National Exit Poll (17,836 respondents, 2% MoE) by 61-37%, an astounding 30 million vote margin.

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.

Pre-election Registered Voter (RV) polls projected a 57% Obama share which closely matched the True Vote Model. Likely Voter (LV) polls are a subset of the RV polls. The LVs eliminate many new voters or others who did not vote in the prior election, cutting the projected Democratic share.

LV polls have an excellent track record in predicting the bogus recorded vote, as proven by the 2008 and 2012 Election Models. Final pre-election LV polls are used by the political pundits for their projections. After all, the media is paid to forecast the official recorded vote – not the true vote.

 

 
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Posted by on July 27, 2015 in 2012 Election

 

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The Media and Scott Walker’s 2014 Election Fraud

Richard Charnin
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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Posted by on July 25, 2015 in Uncategorized

 

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Wisconsin 2010 Senate: True Vote Model and Cumulative Vote shares indicate Feingold won

Wisconsin 2010 Senate True Vote Analysis

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

Wisconsin exit polls
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.

https://docs.google.com/spreadsheets/d/1tXw5LpgQrZjn_YFOkLoLqtQhIAco_V9EEApXvva58kE/pubchart?oid=282743022&format=image

 
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Posted by on July 23, 2015 in Uncategorized

 

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Edison Research Exit Poll Analysis: No Discussion of 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

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.
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%. 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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Posted by on July 20, 2015 in Uncategorized

 

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

Richard Charnin
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?

In 2008, Obama had 52.71% and McCain 45.35% on Election Day.
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
Total..100.0 50.3 47.8 52.7 45.3 ... 49.0 49.1

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
Total.. 131.4 100% 52.9% 45.6% 1.5%
Votes............... 69.50 59.95 2.02

 
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Posted by on July 16, 2015 in Uncategorized

 
 
Richard Charnin's Blog

JFK Conspiracy and Systemic Election Fraud Analysis

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