Aug. 24, 2015

**Look inside the books: **Reclaiming Science: The JFK Conspiracy

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

2004 Election Fraud: Confirmation of a Kerry Landslide

1988-2012 Presidential Elections: The Master Spreadsheet

The 2004 Election “Game” debate

Today is the 10th anniversary of my banning during the “Game” debate on the Democratic Underground (“DU”). The purpose of the debate was to determine if Bush really did steal the election. In those days I posted as TruthIsAll (TIA).

In 2004-2005, DU was the most popular debating site for analyzing the 2004 exit polls. There were scores of heated debates. Viewing the “Game” thread is instructive in understanding the nature of the debates which fueled the election integrity movement.

http://www.democraticunderground.com/discuss/duboard.php?az=show_mesg&forum=203&topic_id=390193&mesg_id=390193

I expressed frustration with the exit poll naysayers who had a myriad of bogus excuses as to why the exit polls were wrong and claimed that Bush did not steal the 2004 election:

*Not ONE mathematical scenario, but many theories and hypotheses based on reluctant Bush voters, the Bush Bandwagon effect, lying Gore voters, forgetful Gore voters, bad weather at the exit polls, exuberant Kerry-biased exit pollsters, inexperienced exit pollsters, exit polls not designed to catch fraud, young exit pollsters, early Democratic voters, late Republican voters, inaccurate exit polls, cluster effect, faulty assumptions, High MoEs, massive Fundamental Christian turnout, Bush the War President, conspiracy fraudsters, sore Kerry losers, rabid spreadsheet-wielding liberal bloggers, Democrats weak on defense, Kerry a lousy campaigner, Repubs united, Democrats divided, Bush strong on moral issues, Bush a religious born-again Christian..
*

This comment by the brilliant poster *anaxarchos* sums it all up nicely:

http://www.democraticunderground.com/discuss/duboard.php?az=show_mesg&forum=203&topic_id=390193&mesg_id=390632

In 2005, unadjusted state and national exit polls were not available. The 12:22am preliminary 2004 National Exit Poll timeline (at 13,047 respondents) showed Kerry leading by 51-48%. But the final adjusted poll (13660 respondents) magically matched Bush’s 51-48% margin. Analysts questioned how vote shares could flip an impossible 3% with just 613 additional respondents. The anomaly was strong evidence that the election was stolen.

Another major red flag was that the adjusted poll indicated that 43% (52.6 million) of the 122.3 million who voted in 2004 were returning Bush 2000 voters. Bush only had 50.5 million votes in 2000. Approximately 2 million Bush voters died and another 1 million did not return in 2004. Therefore, there could not have been more than 47.5 million returning Bush voters.

**There had to be at least 5 million phantom Bush voters.
**

In order to explain away these anomalies, the naysayers offered a number of arguments:

1) Reluctant Bush exit poll responders

2) Gore voter false recall

3) near zero correlation of 2000 to 2004 vote-swing and the 2004 exit poll red-shift.

All of these arguments were subsequently refuted.

Notes:

In 2006, Mark Lindeman wrote “A TruthIsAll FAQ” in which he responded to questions on my analysis. This was my response: https://richardcharnin.wordpress.com/2011/07/14/361/

These posts were written in 2012 after the 1988-2008 state and national unadjusted exit polls became available. The data was utilized in the True Vote Model.

** 2004 unadjusted state and national exit polls vs. the recorded vote**

https://docs.google.com/spreadsheet/pub?key=0AjAk1JUWDMyRdFIzSTJtMTJZekNBWUdtbWp3bHlpWGc&gid=7

**Exit Polls- adjusted to force a match to the recorded vote** https://richardcharnin.wordpress.com/2012/01/07/1395/

https://richardcharnin.wordpress.com/2012/02/21/the-final-2004-national-exit-poll-switched-7-2-of-kerry-responders-to-bush/

https://richardcharnin.wordpress.com/2012/04/05/fixing-the-exit-polls-to-match-the-policy/

**True Vote Models- a powerful sensitivity analysis tool**

https://richardcharnin.wordpress.com/2012/02/07/using-true-vote-model-sensitivity-analysis-to-prove-that-kerry-won-the-2004-election/

https://richardcharnin.wordpress.com/2012/01/08/a-true-vote-probability-analysis-of-a-kerry-win-in-ohio/

2004 Pre-election and Exit Poll Simulation Model

**Election Myths**

The Unadjusted 2004 National Exit Poll: Closing the Book on the False Recall Myth

Vote Swing vs. Exit Poll Red-Shift: Killing the “Zero slope means No Election Fraud” Canard

Exposing Election Myths: Facts and Graphs

**Media Complicity- covering up the Fraud**

https://richardcharnin.wordpress.com/2011/11/22/why-do-all-election-forecasters-political-scientists-academics-and-media-pundits-avoid-the-systemic-fraud-factor/

]]>

Richard Charnin

August 17, 2015

Updated: Aug.19, 2015

**Look inside the books: **Reclaiming Science: The JFK Conspiracy

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

2004 Election Fraud: Confirmation of a Kerry Landslide

1988-2012 Presidential Elections: The Master Spreadsheet

Questions have been raised as to whether the number of elections analyzed is sufficient to draw conclusions. Given that approximately 20 million votes in 13 elections have been analyzed, the results are statistically significant. The analysis is confirmed by other forensic methods (True Vote Model, exit polls) for competitive and non-competitive races.

The analysis of cumulative vote shares (CVS) has revealed a consistent pattern. It is a well-known fact that Democrats are the majority in highly populated urban locations; the largest precincts are usually Democratic. Republicans are heavily represented in rural areas. But in scores of state elections there has been an increase in cumulative Republican vote shares in larger precincts. This anomaly has been noted by PhDs in Kansas and Vanderbilt University.

This post links to CVS blog posts and related spreadsheets:

https://richardcharnin.wordpress.com/2015/08/02/election-fraud-models-cumulative-vote-shares-and-true-vote-analysis/

The basic premise is that Republican increase in cumulative precinct vote shares is counter-intuitive since the Democrats do much better in urban and suburban counties than in rural areas where the GOP is dominant. Precincts in Urban areas contain more voters than rural areas.

Since the GOP gains share in Democratic locations in virtually all of the competitive elections analyzed, it is highly suggestive evidence that Democratic precincts are where the majority of votes are stolen. In competitive elections, the correlation between county/precinct vote-size and the change in Democratic vote share is negative; Democrats lose share as county/precinct size increase. On the other hand, in non-competitive races, the statistical correlation is close to zero; there is virtually no relationship.

https://docs.google.com/spreadsheets/d/1dUBFrWmJxiopewHCUpHKbuTICmfcisJ9RXg48F-p1ec/edit#gid=0

The numerical evidence in each election is clear.

1- In the 15 largest counties, Republican vote share increases from the 25% mark to the final.

2- In the other smaller counties, there is virtually no change in vote share from the 25% mark.

3- In counties where the Democrats led at the 25% mark, their vote share declined significantly.

CVS Summary Graph: https://docs.google.com/spreadsheets/d/1dUBFrWmJxiopewHCUpHKbuTICmfcisJ9RXg48F-p1ec/pubchart?oid=1134854289&format=image

Consider the following changes from the 25% cumulative vote share to the final recorded share for five Governor elections (all but one competitive) and one senate election.

https://docs.google.com/spreadsheets/d/1dUBFrWmJxiopewHCUpHKbuTICmfcisJ9RXg48F-p1ec/edit#gid=0

**Final recorded shares differ slightly from Final CVS shares due to rounding.
Votes in millions.
.......Recorded Vote......All counties.......Top counties........ Other counties
......Votes Dem% Rep%.. 25% Final Chg.. Votes 25% Final Chg.. Votes 25% Final Chg
Total 14.44 46.6 50.4.. 50.7 46.0 -4.6.. 9.75 55.9 51.0 -4.9.. 4.69 38.3 35.7 -2.7 **

**
**

`FL Gov 5.95 47.0 48.1.. 52.7 47.0 -5.7.. 3.47 58.5 54.4 -4.1.. 2.48 41.8 36.8 -4.9`

WI Gov 2.38 46.7 52.2.. 49.1 46.6 -2.5.. 1.57 53.5 48.6 -4.9.. 0.81 40.9 42.7 +1.8

IL Gov 3.63 48.4 50.3.. 52.7 46.5 -6.2.. 2.80 58.6 51.2 -7.4.. 0.83 33.1 30.4 -2.7

MD Gov 1.32 47.2 51.0.. 49.7 46.4 -3.3.. 1.10 54.0 50.6 -3.4.. 0.20 27.8 25.2 -2.6

SD Gov 0.28 25.4 70.5.. 25.5 25.0 -0.5.. 0.19 24.7 23.3 -1.4.. 0.08 27.2 28.8 +1.6

KS Sen 0.88 42.3 53.5.. 42.3 42.2 -0.1.. 0.62 48.5 47.1 -1.4.. 0.26 27.7 30.8 +3.1

Non-competitive races:

SD Gov: Daugaard (R) won easily (70.5-29.4%). Note the slight 0.49% change in vote shares.

KS senate: the Independent lost by 53.2-42.2% but nearly tied the Republican in the Top 15 counties (618,000 votes). In the other counties (264,000 votes), he lost by 64.3-31.3%.

The Maryland 2014 Governor election is of particular interest: Hogan (R) defeated Brown (D) by 65,000 votes (51.7-47.2%). But Brown won 301,000 early and 83,000 late votes (absentee and provisional ballots) by 53.9-44.5%. Hogan led Election Day (1,319,000 votes) by 52.9-45.3%.

Election Day voting is on machines whereas early and late votes use paper ballots.

http://elections.state.md.us/elections/2014/results/General/gen_results_2014_2_003-.html

This anomaly also occurred in the 2000-2012 presidential elections. The Democrats did much better in early and late voting.

In 2012 Obama led in early voting (40 million) by 55-43%; he led in late voting (11.7 million) by 58-38%. He lost to Romney on Election Day voting (77 million) by 50.4-47.9%.

https://richardcharnin.wordpress.com/2013/01/09/election-fraud-2012-simple-algebra-of-early-election-day-and-late-recorded-votes/

Exit polls (Party-ID) are adjusted to match the recorded vote.

https://richardcharnin.wordpress.com/2015/08/14/election-forensics-2014-wi-fl-il-governor/

**Other 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.

http://ivn.us/2015/07/20/report-2014-voting-machine-tampering-likely-wisconsin-ohio

Cumulative Vote Share (CVS) anomalies were noticed in a special Ohio 2005 congressional election favoring Schmidt (R) and in the 2012 GOP primaries favoring Romney.

Michael Collins wrote this article on the 2005 race:

https://i1.wp.com/img.scoop.co.nz/stories/images/0508/822661ff432645f80c0e.jpeg?zoom=2

“Richard Charnin, posting as TruthIsAll, first noted the pattern with an analysis of the 2005 special election for a vacated seat for Ohio’s 2nd district, in the House of Representatives. The candidates were the liberal-populist Democrat Paul Hackett versus a right-wing Republican, Jean Schmidt. Charnin noticed that Schmidt’s votes and percentages increased substantially from the smallest to largest precincts in that district. This was a patently absurd pattern of vote accumulation since the liberal Hackett wins were in highly conservative counties that rarely voted for any Democrat.

Precincts with the most votes favored Schmidt at nearly 100%, with Hackett winning in only those with less than 200 votes counted.

A review of precinct level results by Richard Charnin (TruthIsAll on Democratic Underground) reveals this interesting trend. This data is preliminary and more detail needs to be obtained from the Clermont Board of Elections. However,the data observed for Clermont makes little sense on the face of it.

Hackett won 38 of 191 Clermont precincts with fewer than 187 votes, but lost ALL of the largest 54 precincts (those with more than 187 votes each). This is reflected in the following graph produced by Democratic Underground poster TruthIsAll, one of the first election fraud analysts to notice anomalies in Clermont County.

Hackett’s percentage by precinct group size:

46.9% in precincts under 100 votes

43.5% in precincts of 100-200 votes

39.6% in precincts of 200-300 votes

34.6% in precincts of 300 + votes

These results raise interesting questions. Why does Hackett do much better in the smaller precincts? Are they more rural than the larger precincts? If so, does this not present a counter-intuitive pattern, with the Democrat taking some of the conservative, less populated areas and the Republican winning all of the precincts in the most populated areas?

A question can be raised about the difference between turnout (the votes cast) and the actual size of the precinct, which may or may not be a reflection of votes cast.

The following graph, also produced by TruthIsAll, answers the question.

https://i1.wp.com/img.scoop.co.nz/stories/images/0508/822661ff432645f80c0e.jpeg?zoom=2

As he said while commenting on this data on 8/5/05: “The regression line has zero slope. Voters turned out at a fairly constant rate across precincts. So turnout wasn’t a factor in explaining why the Schmidt vote percentage increased as precinct size increased.”

Collins also wrote a two-part article on the 2012 GOP primaries.

http://www.dailykos.com/story/2012/11/06/1157009/-Rigged-Elections-for-Romney-Vote-Flipping#

“Part I of this series suggested that there may well have been massive vote flipping for candidate Mitt Romney in the Republican primaries (Rigged Elections for Romney (10/22/12) The article and the initial research analysis were received broadly. In addition, highly motivated citizens across the country and a team of high school students contacted the authors for help replicating the research in their states. The researchers, Francois Choquet et al., point out that this can be done with their open source techniques.

The basic argument is straightforward. If you look at precinct level voting data arranged from the smallest to the largest precincts, you will see Romney’s gains increasing substantially as the cumulative vote increases. For example, Ohio and Wisconsin show this clearly as do eleven other states presented here. This extraordinary vote gain from smallest to largest precincts is so out of line, that the probability that this would happen by chance alone is often less than 1 out of a number represented by 1 preceded by 100 zeros and a decimal point, a value beneath the statistical package’s lower limits. As a result, the researchers termed the suspected vote flipping for Romney the “amazing anomaly.” (The Amazing Statistical Anomaly)

The research team’s observation of Romney gains based on precinct size is not unique. The anomaly was raised previously concerning the Republican presidential primaries on a political discussion forum.”

]]>

Richard Charnin

Aug.13, 2015

Updated: Aug.17, 2015

**Look inside the books: **Reclaiming Science: The JFK Conspiracy … Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

An analysis of Exit Polls, True Votes and Cumulative Vote Shares indicates that the 2014 Governor elections in Wisconsin, Florida, Illinois and Maryland were likely stolen.

Although most voters believe that politicians are corrupt, many still cling to the myth that votes are accurately and fairly counted – and that election fraud is a conspiracy theory.

Many voters are still unaware that the unadjusted, pristine exit polls are routinely adjusted to match the recorded vote. The implicit assumption is that the recorded vote represents true voter intent.

Mathematical analysis of discrepancies between unadjusted presidential state and national exit polls versus recorded votes from 1988-2008 confirms systemic fraud to a 100% probability. For a suspicious election, mathematical analysis is a useful way to determine the likelihood of fraud.

**Historical Analysis of Election Fraud**

https://richardcharnin.wordpress.com/2013/01/31/historical-overview-of-election-fraud-analysis/

**Pre-election polls**

To set the context,Democrats do much better in Registered Voter (RV) polls than in the Likely Voter (LV) subsets. The reason is simple: voters considered unlikely to vote (mostly newly registered Democrats) are eliminated from the full RV sample. The RV polls are often close to the unadjusted exit polls and the True Vote. On the othr hand, final LV polls published on Election eve are excellent predictors of the bogus final recorded vote.

**2012 Presidential Election- Final Forecast and True Vote **

https://richardcharnin.wordpress.com/2012/11/05/final-forecast-the-2012-true-vote-election-fraud-model/

In each of the 1988, 1992, 2004 and 2008 presidential elections, in order for the pollsters to force the unadjusted exit polls to match the recorded vote, they needed to assume an impossible number of returning Republican voters. In the 1988-2008 presidential elections, the Democrats led the average recorded vote by 48-46%. But they led by 52-42% in the unadjusted state and national exit polls and the True Vote Model. The 8% margin discrepancy was far beyond the margin of error. But the discrepancy was not due to poor polling design, or exit poll respondents lying about their past vote, their current vote. They would have to lie in response to every one of dozens of questions.

Naysayers and media pundits want voters to believe that the exit polls are always wrong and must be “corrected” to conform to the recorded vote. But they never consider that the unadjusted exit polls are accurate and reflect true voter intent. To the pundits, election fraud is not a factor and the published exit polls accurately reflect voter intent. https://richardcharnin.wordpress.com/2011/11/13/1988-2008-unadjusted-state-exit-polls-statistical-reference/

But fraud is not limited to presidential elections. House, senate, governor and local elections have also been compromised by maliciously coded voting machines and voter disenfranchisement.

Who will argue with these points?

1- Unadjusted exit polls are hidden from the public.

2- Unadjusted exit polls are adjusted to match the recorded vote.

3- Voting machine software is proprietary, not open to public viewing

4- Auditing and hand-counting of votes are denied.

**Exit polls**

Since we cannot view unadjusted exit polls until years later (if at all), we are left with final, adjusted polls. The question “How did you vote in the last election” is no longer asked by the pollsters. An exhaustive analysis of 1988-2008 presidential election unadjusted state and national exit polls shows why the question is no longer asked: it gives an analyst the ability to check the number of returning voters from the prior election. The number provided in the adjusted polls has often proved to be impossible. There were more returning Bush voters in 1992, 2004 and 2008 than were alive. The 2004 election is a case in point. Simple arithmetic proves that it was stolen. It is a fact that…

1- Bush had 50.5 million recorded votes in 2000.

2- The 2004 National Exit Poll indicates that there were 52.6 million returning Bush 2000 voters. This is obviously impossible; the pollsters had to adjust the number of returning voters to match the 2004 recorded vote.

3- Of the 50.5 million Bush 2000 voters, approximately 2 million died before the 2004 election.

4- Approximately one million Bush 2000 voters did not return in 2004.

Therefore, an estimated 47.5 million Bush 2000 voters returned in 2004. Simple arithmetic shows there had to be at least 5 million (52.6-47.5) phantom Bush 2004 voters.

https://richardcharnin.wordpress.com/2012/04/05/fixing-the-exit-polls-to-match-the-policy/

We are forced to analyze adjusted exit polls to look for anomalies. Party-ID is a demographic we can check in lieu of the past vote question. Of course, there is no way to check the Party-ID mix mathematically as we can with the past vote. But it is still useful to see if the percentage mix and corresponding vote shares are plausible based on voter registration and prior elections.

**View the Party-ID sensitivity analysis for FL, IL, WI**

https://docs.google.com/spreadsheets/d/1EGmR_gaXMGRWs4_iPYLNL7qsTDEkD5LMr-oTMJoOWQs/edit#gid=0

**Florida 2014**

True Vote Model

https://docs.google.com/spreadsheets/d/1SnErWihwCvq5puGw3sBF9E4jr585XV2NChqvxGObLAU/edit#gid=841488888

2014 True Vote

2-party Estimated

`2010 True Turnout Votes Mix Crist Scott`

Sink 52.2% 93.0% 2.463 43.5% 92.5% 7.5%

Scott 47.8% 93.0% 2.255 39.9% 6.9% 93.1%

....................... True 52.0% 48.0%

................... Recorded 49.4% 50.6%

```
```

`Sensitivity Analysis`

........Crist Share of Sink

Crist%..89.5% 92.5% 95.5%

Scott...Crist Total Share

9.9%....51.9% 53.2% 54.5%

6.9%....50.7% 52.0% 53.3%

3.9%....49.5% 50.8% 52.1%

........Crist Margin (000)

9.9%...211.69 359.47 507.25

6.9%....76.37 224.15 371.93

3.9%.. -58.96 88.824 236.61

In 2010, Sink (D) won the unadjusted exit poll by 50.8-45.4% (280,000 votes).There were 3150 respondents (2% margin of error). Of course, the poll was adjusted to match Scott’s 49.6-48.4% recorded 64,000 vote margin. It indicated that 47% of the voters were returning Obama voters and 47% McCain voters. But Obama won the Florida easily. Scott needed 67% of the other 6% who voted (new voters and others who voted for third parties in 2008). These adjustments are highly implausible.

To match the recorded vote, the pollsters assumed a 36D-36R-28I split with Scott winning Independents by 52-44%. In matching the unadjusted exit poll, Sink required a 38D-34R-28I split while winning Independents by 47-43%.

The 2014 election was virtually a carbon copy of 2010. Scott won by 48.2-47.1% (66,000 votes). Crist had 52% of the 2-party True Vote if Sink had the 52.2% share in the unadjusted 2010 exit poll. There were 500,000 more voters than in 2010. Historically, heavy voter turnout is good for the Democrats.

So how did Crist lose by 1%?

Crist did not lose. To match the recorded vote, the pollsters assumed an implausible Party-ID split: 31D- 35R- 34I. Assuming the true mix was 35D- 35R- 30I, Crist won by 181,000 votes (49.2-46.1%). According to the adjusted exit poll (assumed biased for Scott), Crist had 91% of Democrats; Scott had just 88% of Republicans. Crist won Independents by 46-44%. Crist shares were most likely higher.

Florida Exit Poll

(adjusted to match the recorded vote)……….True Vote

........Pct Crist Scott Other... Pct Crist Scott Other

Dem.....31% 91.0% 6.00% 3.00%... 35.0% 92.0% 5.00% 3.00%

Rep.....35% 10.0% 88.0% 2.00%... 35.0% 10.0% 88.0% 2.00%

Other...33% 46.0% 44.0% 8.00%... 30.0% 46.0% 44.0% 10.0%

Total...99% 46.9% 47.2% 4.30%... 100.% 49.5% 45.8% 4.80%

Margin............17,044.............220,392

```
```

`Sensitivity Analysis`

................Crist Share of Dem

Dem Rep.....91.0% 92.0% 93.0%

................Crist Total share

32% 38%.....46.7% 47.0% 47.4%

35% 35%.....49.2% 49.5% 49.9%

38% 32%.....51.6% 52.0% 52.3%

................Crist Margin

32% 38%.....-1.8% -1.2% -0.5%

35% 35%......3.1% 3.8% 4.4%

38% 32%......7.9% 8.7% 9.4%

**Illinois 2014**

True Vote Model

https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/edit#gid=1727387709

Quinn won the True Vote assuming Obama’s 57% share and an equal 80% turnout of returning Obama and Romney voters.

`2012 Votes Turnout.Vote. Pct.. Quinn. Rauner Other`

Obama...57.0% 1,686 1,349 38.5% 87.0% 12.0% 1%

Romney..42.0% 1,243 994.. 28.4% 7.00% 93.0% 0%

Other....1.00% 00030 24....0.7% 50.0% 49.0% 1%

DNV (new).......... 1,133. 32.4% 48.0% 48.0% 4%

Total...3,019 2,959 3,500..True 1,799 1,642 59

................................51.4% 46.9% 1.7%

.......................Recorded 47.0% 51.9% 0.9%

...............................1,645 1,817 32

True Vote sensitivity analysis

Assumption: Quinn wins 48% of DNV/New voters

```
```Quinn Quinn share of returning Obama voters

Share of 85.0% 87.0% 89.0%

Romney Quinn Vote Share

9%......51.2% 52.0% 52.7%

8%......50.9% 51.7% 52.5%

7%......50.6% 51.4% 52.2%

6%......50.3% 51.1% 51.9%

5%......50.1% 50.8% 51.6%

........Quinn Margin (000)

9%......143.0 196.9 250.9

8%......123.1 177.0 231.0

7%......103.2 157.2 211.1

6%.......83.3 137.3 191.2

5%.,.....63.4 117.4 171.4

Party-ID heavily favored the Democrats: 43D- 30R- 27I. Quinn had just 85% of Democrats and 29% of Independents. Assuming Quinn had 87% and 44%, respectively, he would have been a 50.8-45.6% winner.

Illinois Exit Poll

(adjusted to match recorded vote)……….True Vote

.........Pct Quinn Rauner Other...Pct Quinn Rauner Other

Dem.....43.0% 85.0% 13.0% 2.00%...43.0% 85.0% 12.0% 1.0%

Rep.....30.0% 5.00% 93.0% 2.00%...30.0% 5.00% 93.0% 2.0%

Other...26.0% 29.0% 64.0% 7.00%...27.0% 40.0% 52.0% 8.0%

Total...99.0% 45.6% 50.1% 3.30%...100.% 49.3% 47.1% 3.6%

Margin............164,643.............79,058

```
```

`Sensitivity Analysis`

............Quinn Share of Dem

Dem Rep.........85.0% 86.0% 87.0%

............Quinn Total share

42% 31%.....48.1% 48.5% 48.9%

43% 30%.....48.9% 49.3% 49.7%

44% 29%.....49.7% 50.1% 50.5%

............Quinn Margin

42% 31%....-0.3% 0.6% 1.4%

43% 30%.....1.3% 2.2% 3.0%

44% 29%.....2.9% 3.8% 4.7%

**Wisconsin 2014**

True Vote Model

https://docs.google.com/spreadsheets/d/1oAq0CJ1QSfy4JaNYpM_5esTafUdpt3ipgJU0Iz8RlD0/edit#gid=841488888

Burke won the True Vote, assuming that Barrett won the 2-party vote in 2012 by 53-47% and there was an equal returning voter turnout.

`2-party Estimated 2014`

2012....True Turnout Votes.... Mix Burke Walker

Barrett 53% 93% 1,207,636.......50.7% 92.7% 7.3%

Walker. 47% 93% 1,070,923.......45.0% 6.5% 93.5%

New..............101,962........4.3% 54.0% 46.0%

...........................True Vote 52.2% 47.8%

............................Recorded 47.1% 52.9%

```
```Burke Share of Barrett

Share of.89.7% 92.7% 95.7%

Walker...Burke Share

9.5%.....52.1% 53.6% 55.1%

6.5%.....50.7% 52.3% 53.8%

3.5%.....49.4% 50.9% 52.4%

.........Burke Margin (000)

9.5%.....98.86 171.3 243.8

6.5%.....34.61 107.1 179.5

3.5%....-29.64 42.81 115.3

Party-ID was 36D- 37R- 27I, as opposed to 39D- 35R- 27I in prior elections. There was heavy voter turnout. Burke had just 43% of Independents. If the mix was actually 38D- 35R- 27I and Burke had 50% of independents, she would have been a 50.2-48.5% winner.

Wisconsin Exit Poll

(adjusted to match recorded vote)……….True Vote

........Pct Burke Walker Other...Pct Burke Walker Other

Dem.....36.0% 93.0% 6.00% 1.00%...38.0% 94.0% 5.00% 1.0%

Rep.....37.0% 4.00% 96.0% 0.00%...35.0% 4.00% 95.0% 1.0%

Other...27.0% 43.0% 54.0% 2.00%...27.0% 49.0% 49.0% 2.0%

Total...100.% 46.6% 52.3% 0.90%...100.% 50.4% 48.4% 1.3%

Margin............135,539..............46,936

```
```

`Sensitivity Analysis`

.............Burke Share of Dem

Dem Rep.....93.0% 94.0% 95.0%

.............Burke Total share

36% 37%.....48.2% 48.6% 48.9%

38% 35%.....50.0% 50.4% 50.7%

40% 33%.....51.8% 52.2% 52.6%

.............Burke Margin

36% 37%.....-2.4% -1.6% -0.9%

38% 35%......1.2% 2.0% 2.7%

40% 33%......4.8% 5.6% 6.4%

**National Exit Poll (House)**

The mix was 35D- 36R- 28I. The Republicans won by 52.0-45.8%. The Democrats had an implausibly low 42% of Independents. If the mix was 36D- 36R- 28I and the Democrats had 50% of the Independents, it would have been a virtual 49% tie.

**Cumulative Vote Shares**

It is well known fact that Democrats are the majority in highly populated urban locations; Republicans are heavily represented in rural areas. Highly populated precincts are mostly Democratic. But in scores of state elections there has been an increase in cumulative Republican vote shares in larger precincts. This anomaly has been confirmed by PhDs in Kansas and Vanderbilt University.

Consider the following changes from the 25% cumulative vote share to the final recorded share for five Governor elections (all but one competitive) and one senate election.

https://docs.google.com/spreadsheets/d/1dUBFrWmJxiopewHCUpHKbuTICmfcisJ9RXg48F-p1ec/edit#gid=0

-All 67 counties: Crist had 47.0% of 5.94 million votes

-12 Top counties: Crist had 52.0% of 3.67 million votes

-55 counties: Crist had 38.9% of 2.27 million votes

Top 12 counties, Crist’s 2-party share declined from 58.5% to 54.4%

Note: precinct data is not available for the 55 counties.

**Wisconsin**

https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdEhqXzdlbUhZT1Vic3RSQmU2cUVkc3c#gid=12

-All 72 counties (2.59 million): Burke’s vote share declined from 49.1% to 46.6% (61.1 million)

-Top 15 counties (1.75 million votes): Burke’s vote share declined from 53.4% to 48.6% (76 mil. votes)

-Other 57 counties (0.84 million): Burke’s vote share increased from 40.8% to 42.6% (14.9 mil.)

-All 102 counties (3.63 million): Quinn’s vote share declined from 52.7% to 46.6% (227.7 million)

-Top 15 counties (2.79 million votes): Quinn’s vote share declined from 58.6% to 51.2% (205.8 million votes)

-87 counties (0.83 million): Quinn’s vote share declined from 33.1% to 30.4% (21.9 million)

**Maryland**

There is no exit poll for the MD governor election.

Hogan (R) defeated Brown (D) 53.88-46.12%

Hogan: 710,854, Brown 608,476 votes.

But note this anomaly:

Brown led by 53.9-44.5% in early and late votes (absentee and provisional ballots).

Hogan led election Day voting by 52.9-45.3%.

This also occurred in the 2000-2012 presidential elections. The Democrats always did much better in late voting. https://richardcharnin.wordpress.com/2013/01/09/election-fraud-2012-simple-algebra-of-early-election-day-and-late-recorded-votes/

`MD Early ElectDay Absentee / Provisional`

Brown 53.74% 45.31% 54.51%

......Early+Prov Elect Day Total

Votes 390,340 1,342,837 1,733,177

Brown 53.91% 45.31% 47.20%

Hogan 44.46% 52.94% 51.00%

Hogan’s cumulative 2-party vote share increased from

50.3% at 25% of the total vote to 51.7% at 50% and 53.6% at 100%.

The 3.3% increase is a conservative estimate of the percentage of votes that may have been switched on Election Day given the 7% discrepancy between Election Day shares vs. Early and Late shares

CVS analysis for MD: https://docs.google.com/spreadsheets/d/17SpMcLyJ0607RyasTG4tRqrFmyDEKmEG45DKGGLZFmA/edit#gid=1626337891

**2005 Special Ohio Congressional Election**

Michael Collins has written about the GOP CVS trend in the 2005 Special Ohio congressional election and the 2012 primaries.

“Richard Charnin, posting as TruthIsAll, first noted the pattern with an analysis of the 2005 special election for a vacated seat for Ohio’s 2nd district, in the House of Representatives. The candidates were the liberal-populist Democrat Paul Hackett versus a right-wing Republican, Jean Schmidt. Charnin noticed that Schmidt’s votes and percentages increased substantially from the smallest to largest precincts in that district. This was a patently absurd pattern of vote accumulation since the liberal Hackett wins were in highly conservative counties that rarely voted for any Democrat.

Precincts with the most votes favored Schmidt at nearly 100%, with Hackett winning in only those with less than 200 votes counted. A review of precinct level results by Richard Charnin (TruthIsAll on Democratic Underground) reveals this interesting trend. This data is preliminary and more detail needs to be obtained from the Clermont Board of Elections. However, the trend observed for Clermont makes little sense on the face of it.

Hackett won 38 of 191 Clermont precincts with fewer than 187 votes, but lost ALL of the largest 54 precincts (those with more than 187 votes each). This is reflected in the following graph produced by Democratic Underground poster TruthIsAll, one of the first election fraud analysts to notice anomalies in Clermont County.

Hackett won 38 of 191 Clermont precincts but lost the 54 largest.

The following percentages help elaborate the graph above.

Hackett’s percentage by precinct group size:

46.9% in precincts under 100 votes

43.5% in precincts of 100-200 votes

39.6% in precincts of 200-300 votes

34.6% in precincts of 300 + votes

“Aside from the vote counting irregularities, other questions remain. Democrats typically do poorly in rural areas. A city-based attorney who supports the right to choose and refuses to support gay bashing legislation, who calls the President a “chicken hawk” and a “son of a bitch”, this candidate, Paul Hackett, carried the four most rural counties in District 2 by an average 59% to 41% margin. Yet this candidate failed in the more populous areas, where he would be expected to do better”.

These results raise interesting questions. Why does Hackett do much better in the smaller precincts? Are they more rural than the larger precincts? If so, does this not present a counterintuitive pattern, with the Democrat taking some of the conservative, less populated areas and the Republican winning all of the precincts in the most populated areas?

A question can be raised about the difference between turnout (the votes cast) and the actual size of the precinct, which may or may not be a reflection of votes cast. The following graph, also produced by TruthIsAll, answers the question. As he said while commenting on this data on 8/5/05: “The regression line has zero slope. Voters turned out at a fairly constant rate across precincts. So turnout wasn’t a factor in explaining why the Schmidt vote percentage increased as precinct size increased.”

Voter turnout in the larger precincts in Clermont County matches that in the overall 2nd District. Hackett sweeps rural, lower-income areas, while Schmidt takes those wealthier, more populous.

No Correlation between Precinct Registration and Voter Turnout

On the face of it, this is odd. The demographic blue-red maps for the 2004 election showed a positive correlation between population density and Democratic (Kerry) votes. Yet in the 2nd District of Ohio in 2005, the exact opposite was true.

Hackett dominated the least populated areas of the district, while Schmidt prevailed in the more populated areas. One observer said that Hackett performed as strongly as he did in rural District 2 because his handgun carry permit was publicized. This ignores the fact that the National Rifle Association endorsed Schmidt; it also ignores the generally prevailing positive attitude towards gun ownership in Southwest Ohio. This argument has one major problem. The NRA has one of the most disciplined political operations in the country. The members are consistent in following endorsements. The endorsement of Schmidt by NRA did not mean “think about voting for Schmidt” it meant “vote Schmidt.” Opposition from the NRA is a major impediment in rural areas.

**2012 primaries**

Consistent CVS anomalies in the 2012 GOP primaries favored Romney

Michael Collins wrote about it in a two-part article:

“Part I of this series suggested that there may well have been massive vote flipping for candidate Mitt Romney in the Republican primaries (Rigged Elections for Romney (10/22/12) The article and the initial research analysis were received broadly. In addition, highly motivated citizens across the country and a team of high school students contacted the authors for help replicating the research in their states. The researchers, Francois Choquet et al., point out that this can be done with their open source techniques.

The basic argument is straightforward. If you look at precinct level voting data arranged from the smallest to the largest precincts, you will see Romney’s gains increasing substantially as the cumulative vote increases. For example, Ohio and Wisconsin show this clearly as do eleven other states presented here. This extraordinary vote gain from smallest to largest precincts is so out of line, that the probability that this would happen by chance alone is often less than 1 out of a number represented by 1 preceded by 100 zeros and a decimal point, a value beneath the statistical package’s lower limits. As a result, the researchers termed the suspected vote flipping for Romney the “amazing anomaly.” (The Amazing Statistical Anomaly)

The research team’s observation of Romney gains based on precinct size is not unique. The anomaly was raised previously concerning the Republican presidential primaries on a political discussion forum.

Related links:

http://www.scoop.co.nz/stories/HL0508/S00186.htm

http://www.dailykos.com/story/2012/10/26/1150485/-Retired-NSA-Analyst-Proves-GOP-Is-Stealing-Elections#

Urban Legend:Implausible 2004 Bush vote shares in Urban counties.

http://www.richardcharnin.com/UrbanLegendLocation.htm

http://www.richardcharnin.com/LocationSizeKerryLandslide.htm

]]>

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 Governor Elections
CVS Analysis
**

```
.....Recorded Vote......All counties.....Top counties...........Other counties
......Votes Dem% Rep%.. 25% Final Chg.. Votes 25% Final Chg.. Votes 25% Final Chg
```

Total 14.44 46.6 50.4.. 50.7 46.0 -4.6.. 9.75 55.9 51.0 -4.9.. 4.69 38.3 35.7 -2.7

```
```

`FL Gov 5.95 47.0 48.1.. 52.7 47.0 -5.7.. 3.47 58.5 54.4 -4.1.. 2.48 41.8 36.8 -4.9`

WI Gov 2.38 46.7 52.2.. 49.1 46.6 -2.5.. 1.57 53.5 48.6 -4.9.. 0.81 40.9 42.7 +1.8

IL Gov 3.63 48.4 50.3.. 52.7 46.5 -6.2.. 2.80 58.6 51.2 -7.4.. 0.83 33.1 30.4 -2.7

MD Gov 1.32 47.2 51.0.. 49.7 46.4 -3.3.. 1.10 54.0 50.6 -3.4.. 0.20 27.8 25.2 -2.6

SD Gov 0.28 25.4 70.5.. 25.5 25.0 -0.5.. 0.19 24.7 23.3 -1.4.. 0.08 27.2 28.8 +1.6

KS Sen 0.88 42.3 53.5.. 42.3 42.2 -0.1.. 0.62 48.5 47.1 -1.4.. 0.26 27.7 30.8 +3.1

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/2015/02/11/2014-florida-governor-election-fraud-cumulative-precinct-vote-shares/

https://richardcharnin.wordpress.com/2014/11/14/florida-2014-governor-true-voteexit-poll-analysis-indicates-fraud/

https://docs.google.com/spreadsheets/d/1SnErWihwCvq5puGw3sBF9E4jr585XV2NChqvxGObLAU/edit#gid=1124172858

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

https://docs.google.com/spreadsheets/d/11pw_YbGe9iidkziW1R8sks5HpseapEGpABD0l2Twebw/edit#gid=1325075119

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

Five 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

2012 GOP primaries

http://www.dailykos.com/story/2012/10/26/1150485/-Retired-NSA-Analyst-Proves-GOP-Is-Stealing-Elections#

]]>

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. This is 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 famous bank robber Willie Sutton was asked why he robbed banks. He replied: “That’s where the money is”. The Republicans know where to go to steal votes: the large urban counties where Democrats live.
**

https://docs.google.com/spreadsheets/d/1v6xm1XWdTYSEt5eXK1AegOY9iLx1ufseWVRzxQsz6N4/edit#gid=1776944924

The largest counties showed increasing Rauner vote shares as the cumulative precinct vote totals increased – a major red flag. View the cumulative vote share changes for all 102 counties at the 25, 50,75, 100% marks.

Quinn had a 52.7% share at the 25% mark which declined to 46.5% (228,000 votes)

His 58.6% share of the largest 15 counties dropped to 51.2% (206,000 votes).

His 33.1% share in the other 87 counties dropped to 30.4% (22,000 votes)

In the 15 middle counties, he had 31.8% which dropped to 30.3% (2,500 votes).

He had 30.7% in the 15 smallest counties which dropped to 29.1% (500 votes).

Quinn had at least 50% in just 6 counties at the 25% mark. His share dropped from 69.6 to 60.2% (122,000 votes).

The small counties were not where votes were stolen as there is little to gain and are strongly Republican to begin with.

But smaller counties are of interest. Here’s why.

Let’s assume that voters at the 25% mark are exit poll respondents. This is obviously not scientifically valid and just an approximation for this analysis. In fact, it may be conservative since smaller precincts are generally rural and Republican. In the 102 counties, 54 had deviations from -3% to +3% (within the standard exit poll margin of error).

– The margin of error was exceeded in 38 counties. In this group, 25 (66%) had at least 10,000 voters.

– Conversely, 53 counties were within the MoE. In this group only 20 (38%) had at least 10,000 voters. The 28% difference is a red flag – an indicator that the largest counties were fraudulent since they had the largest discrepancies.

Quinn’s share increased by 10,000 from the 25% mark in 23 strong, small GOP counties. Kane was the only large county (125,000 votes) where Quinn’s share increased from 32 to 37%.

It is clear that the largest counties were most likely fraudulent.

Let’s focus on the Top 15. Cook County is the largest with 1.34 million votes. Quinn’s share dropped from 73% to 65% (108,000 votes).

CVS percentage and vote changes (in thousands)

(25% mark to the final recorded vote)

Cook.......... 8% 108

Will......... 15% 29.4

St. Clair.... 15% 11.4

Peoria....... 15% 7.8

Rock Island.. 11% 7.2

Sangamon..... 10% 7.15

Macon......... 9% 3

Winnebago..... 9% 7.1

Lake.......... 6% 12.1

DuPage........ 3% 8.6

There is a -0.37 correlation between county size and Quinn’s vote share change from 25% to the final 100%. Quinn’s share goes down as county size increases.

In Democratic leaning counties (1.7 million votes), Quinn’s share declined from 69.6% to 60.2% (162 thousand votes).

The results confirm the CVS trend: GOP cumulative shares rise from the smallest to the largest counties, as shown in the graphs.

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 exit poll matched the recorded vote to within 0.4%. But it is standard procedure to adjust the poll to match the 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/

]]>

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.

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.

]]>

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.

]]>

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.

]]>

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.

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**

]]>

July 16, 2015

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

]]>