## 2016 Election: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Updated Sept. 19, 2017

Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016. View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast, least one million Democratic minority voters were disenfranchised via Crosscheckwhich eliminated voters with duplicate names from voter rolls. He claims that 7 million minority voters were disenfranchised.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count.

Let TV = True Vote; RV = Recorded vote
RV = TV + Fraud

Given the Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6

Election fraud components:
-Vote flipping on maliciously coded, proprietary voting machines and central tabulators
-Illegal voters (non-citizens)
-Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
Uncounted- 7 million: 85% for Clinton
Vote Flip- 5 million (net): 8% of Trump’s votes flipped to Clinton on voting machines and central tabulators.
Illegals- 2 million: 85% for Clinton
Trump wins by 3.7 million: 68.7-64.9 (48.6-46.0%)

Assume 12 million uncounted: 85% to Clinton
(2 million illegal, 5 million flip)
Trump still wins: 69.4-69.2 million (47.48-47.32%)

………..Total………Clinton….Trump……Other
Vote…..136.2……..65.7………62.9………7.6
Pct……,,100%..,….48.3%…..46.2%……5.6%

Illegal… 2.0…….  -1.70…..  -0.30…………0 non-citizens
Unctd…..7.0………5.95……..1.05…………0 disenfranchised
Flip……..5.0…….  -5.0……….5.0………….0 voting machine

Net……141.2……64.9…….68.7………7.6

Sensitivity Analysis (assume 7 million uncounted, 85% for Clinton)
Worst case (7% flip, 80% of illegals to Clinton):  Trump wins by 2.3 million
Base case: (8%  flip, 85% of illegals to Clinton): Trump wins by 3.7 million
Best case: (9% flip, 90% of illegals to Clinton): Trump wins by 5.2 million

http://www.pewhispanic.org/2016/01/19/millennials-make-up-almost-half-of-latino-eligible-voters-in-2016/ph_election-2016_chap1-chart-08/

 Total Clinton Trump Other Recorded vote 136.2 million 65.7 62.9 7.6 48.25% 46.17% 5.59% Illegal 2.0 -1.7 -0.3 0 Uncounted 7.0 5.95 1.05 0 Vote Flip 5.0 -5.0 5.0 0 Adjusted 141.22 64.9 68.7 7.6 46.0% 48.6% 5.4% 7.0 million uncounted 85% to Clinton Illegals to Clinton 80% 85% 90% Flip to Clinton Trump Vote 9% 69.20 69.30 69.40 8% 68.57 68.67 68.77 7% 67.94 68.04 68.14 Vote Flip Trump Vote 9% 49.00% 49.07% 49.14% 8% 48.56% 48.63% 48.70% 7% 48.11% 48.18% 48.25% Vote Flip Clinton vote 9% 45.61% 45.54% 45.47% 8% 46.06% 45.98% 45.91% 7% 46.50% 46.43% 46.36% Vote Flip Trump margin 9% 4.79 4.99 5.19 8% 3.53 3.73 3.93 7% 2.27 2.47 2.67

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Posted by on September 20, 2017 in 2016 election

## 2016 True Vote Sensitivity analysis: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Updated Sept. 19, 2017

Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016. View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast, least one million Democratic minority voters were disenfranchised via Crosscheck which eliminated voters with duplicate names from voter rolls. He claims that 7 million minority voters were disenfranchised.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count.

Let TV = True Vote; RV = Recorded vote
RV = TV + Fraud

Given the Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6

Election fraud components:
-Vote flipping on maliciously coded, proprietary voting machines and central tabulators
-Illegal voters (non-citizens)
-Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
Uncounted- 7 million: 85% for Clinton
Vote Flip- 5 million (net): 8% of Trump’s votes flipped to Clinton on voting machines and central tabulators.
Illegals- 2 million: 85% for Clinton
Trump wins by 3.7 million: 68.7-64.9 (48.6-46.0%)

Sensitivity Analysis (assume 7 million uncounted, 85% for Clinton)
Worst case (7% flip, 80% of illegals to Clinton):  Trump wins by 2.3 million
Base case: (8%  flip, 85% of illegals to Clinton): Trump wins by 3.7 million
Best case: (9% flip, 90% of illegals to Clinton): Trump wins by 5.2 million

http://www.pewhispanic.org/2016/01/19/millennials-make-up-almost-half-of-latino-eligible-voters-in-2016/ph_election-2016_chap1-chart-08/

 Total Clinton Trump Other Recorded vote 136.22 65.72 62.89 7.61 48.25% 46.17% 5.59% Illegal 2.0 -1.7 -0.3 0 Uncounted 7.0 5.95 1.05 0 Vote Flip 5.0 -5.0 5.0 0 Adjusted 141.22 64.9 68.7 7.6 46.0% 48.6% 5.4% 7.0 uncounted 85% to Clinton Illegals to Clinton 80% 85% 90% Flip to Clinton Trump Vote 9% 69.20 69.30 69.40 8% 68.57 68.67 68.77 7% 67.94 68.04 68.14 Vote Flip Trump Vote 9% 49.00% 49.07% 49.14% 8% 48.56% 48.63% 48.70% 7% 48.11% 48.18% 48.25% Vote Flip Clinton vote 9% 45.61% 45.54% 45.47% 8% 46.06% 45.98% 45.91% 7% 46.50% 46.43% 46.36% Vote Flip Trump margin 9% 4.79 4.99 5.19 8% 3.53 3.73 3.93 7% 2.27 2.47 2.67

Posted by on February 25, 2017 in 2016 election

## Confirmation: Bernie won California by at least 100,000 votes

Richard Charnin
July 10, 2016

On Election Day (6/7) Hillary led by 56.37 – 43.63%

According to Greg Palast: Bernie won CA by at least 100,000 votes. https://www.washingtonpost.com/news/post-politics/wp/2016/06/27/still-sanders-activists-cling-to-hope-of-flipping-california/

“They said, with 100 percent of precincts reporting, Hillary Clinton has won by 400,000 votes,” Palast said of the media. “Now, I want you to say this number with me: 1,959,900. That’s the number of ballots that were not yet counted. How do you say an election’s over when there are 2 million ballots left to count?”

According to Palast, those ballots had the potential to flip the election. Based on a call to the secretary of state’s office, he estimated that all of the outstanding ballots were from “no party preference” voters; based on a pre-primary poll, he estimated a 40 percentage point margin for Sanders among those ballots.

“Bernie Sanders got at least 1.25 million votes from that pile,” Palast said. “The good news is that Bernie won California. … If you count every ballot, Sanders would win by 100,000.”

J.T. Waldron  writes at http://electionnightmares.com/archives/564

As John Brakey states, “Elections are only as strong as their weakest link”.

Despite California counting only 65% of the ballots on election day, media outlets like Politico and The New York Times ceased from covering the rest of the count, which leaves its audience assuming a literal interpretation of “100% of the precincts reporting”, but that statement does not mean all the votes are counted. It only means precinct ballots from all of the precincts have been counted, but there are many vote-by-mail and provisional ballots that have yet to be included in this total.

In fact, the cumulative count in days following California’s election day proved to be riveting to many Sanders supporters who were watching the Sanders deficit shrink. Brakey assesses the sudden shift:

On election night, shortly after 8:00 PM, the first results were released and they were 99% vote-by-mail ballots. The numbers showed Hillary Clinton with a decisive lead over Bernie Sanders by 25.94% points. Clinton received 62.56% to Sanders 36.63% with 1.52 million vote-by-mail ballots.

By early the next morning, another 1.94 million ballots were counted. Clinton received 50.73% and Sanders got 48.47%, but those numbers are deceiving. On election day, 718,869 voters were forced to vote a provisional ballot which, in my estimate, are 80% Democratic voters with at least 60% going to Sanders. This would be enough to flip the ‘precinct vote’ to Sanders, who would get 52% over Clinton’s new total of 47%. This spread more accurately reflects the pre-election polling numbers.

California primary early vote by mail exit poll

Election Justice USA asserts that a Capitol Weekly early-voter exit poll conducted across the state of California yielded a 23 percent discrepancy in Los Angeles vote-by-mail ballots compared to the actual results. During the polling of the early round of mail-in voters, Hillary Clinton had a lead over Bernie Sanders in the Los Angeles area that was less than 10 percent. Election Justice USA, a voter advocacy non-profit organization, says that the discrepancy is significant enough to demand a hand audit of the early mail-in ballots.

“The discrepancy cannot be easily explained by demographic factors: the results of the Capitol Weekly exit poll were weighted by age and race. Moreover, the exit poll had 21,000 respondents, and was praised–prior to election night–by mainstream elections journalists, including Nate Cohn of the New York Times. While no exit poll can prove fraud, a significant exit polling discrepancy such as this constitutes cause for alarm, especially one of this magnitude. It’s also sufficient cause for immediate action: voters should bring pressure to bear on officials and demand an expanded hand audit.”

Cumulative Vote Share (CVS) analysis

When California county votes are sorted and cumulated from smallest to largest counties,  they confirm the likelihood of fraud. In virtually every CVS analysis, the establishment candidate (Clinton) gains vote share in the larger counties . One would intuitively expect that  the progressive candidate (Sanders) would gain share in the vote-rich urban and suburban counties. The fact that Sanders does well in small  (conservative) counties but not as well in large counties is further indication of voter suppression, ballot destruction and vote flipping.

Simple California Vote share Model

Assume the following.
a) Party-ID: 57% Independents vs. 43% Democrats
(estimated based on 2014-2016 surveys)
b) Sanders won 70% of Independents

Result:
Clinton needed an implausible 85% of Democrats to match her 53.5% share.

Party-ID….PCT…… Sanders….Clinton
IND……… 57.0%….. 70.0%….. 30.0%
DEM…….. 43.0%…….15.3%….. 84.7%
Total…….100.0%….. 46.5%….. 53.5%
Recorded……………. 46.5%….. 53.5%

Sensitivity Analysis- What if Clinton had 65% of Democrats?
Sanders would have won by 55-45%.

………………………..Sanders% IND
Sanders…….. 55% 60% 70% 75% 80%
% DEM……… Sanders Vote share
45%………….. 51% 54% 59% 62% 65%
40%………….. 49% 51% 57% 60% 63%
35%………….. 46% 49% 55% 58% 61%
30%………….. 44% 47% 53% 56% 59%
25%………….. 42% 45% 51% 54% 56%

Covert Shredding of Provisional Ballots

A San Diego County Registrar insider claims that hundreds of thousands of California Democratic primary provisional ballots were illegally destroyed   in a covert shredding operation.  A consignment of boxes was delivered to the San Diego Registrar’s Office at 5600 Overland Ave in the morning and an “oversized shredding van” arrived minutes later and took the boxes away. The boxes were carried from the building to the vehicle by men she had never seen before wearing dark blue overalls.

White-out Erasing of Sanders Ballots

Election monitors in San Diego   have captured film of ballots which have been tampered with white-out erasing only Sanders votes, sometimes with part of Bernie Sanders’ first name obscured as well. In the film, a monitor reports that almost half the ballots in the box of ballots she witnessed had been so altered, always against Sanders. The mainstream media has yet to report on the startling discovery.

After the Illinois Democratic primary in March, a citizens’ watchdog group monitoring an audit of the votes says they witnessed vote totals being tampered with to benefit Hillary Clinton.

In other video captured by citizen reporters and election monitors in San Diego, an election official attempts to keep monitors away from the windows of a room where “provisional” ballots are being counted by officials. They  were cast mostly by independent voters in the primary. At one point an election monitor, a woman, is told by an official to keep her voice down. The election monitor questions what the officials seen through the glass in an off-limits room are doing in the back. The woman tells the official that “you guys are violating the election code, and I’m not going to shut up about it.”

In a follow up interview, Charlie Loomis, the IT manager,  confirms that it is indeed white-out that can be seen on the ballots, and that the ballots are being “manipulated.” The IT manager goes on to say that, as a San Diego official, he has no control over this; the white-outs are a result of Democratic party rules on how these  provisional ballots must be processed.  Loomis said he has “nothing to do with” those rules. He did indicate, however, that after the white-out process, the ballots are “run through the scanner again.”

 Date Range Votes HRC Sanders HRC Sanders Elec Day June 7 early 1,520,626 951,304 557,005 62.56% 36.63% June 7 late 1,949,824 977,447 945,080 50.73% 48.47% Elec Day Total 3,470,450 1,928,750 1,502,085 55.58% 43.28% June 8-23 Vote by Mail 1,313,293 645,090 652,707 49.12% 49.70% June 7-23 Total 4,783,743 2,573,840 2,154,792 53.80% 45.04% June 9-23 Provisionl 301,824 120,247 179,163 39.84% 59.36% Est Provis. 100,000 33,280 66,000 33.28% 66.00% NPP 995,000 288,550 706,450 29.00% 71.00% Total 1,396,824 442,077 951,613 31.65% 68.13% Total 6,180,567 3,015,917 3,106,404 48.80% 50.26% 90,488 1.46% Update Brakey Estimated 6,180,567 6/7 EDay Counted 3,470,450 Unctd 2,710,117 7/7 Unctd Counted 2,353,152 Remaining Unctd 356,965 Missing 686,210 7/7 Unctd+ missing 1,043,175 75% Sanders 782,381 Uncounted + missing 25% Clinton 260,794 Uncounted + missing Sanders gain 521,588 Clinton margin 426,665 on June 7 Sanders margin 94,922 on July 7 Greg Palast Sanders margin 100,000

Posted by on July 10, 2016 in 2016 election

## Democratic Primary True Vote Model: Sanders has 52%

Democratic Primaries True Vote Model: Bernie has 52%

Richard Charnin
Updated: July 21, 2016

This model estimates Sanders’ True Vote. The base case estimate is that Sanders had 52% of the total vote in primaries and caucuses.

It is important to note that Sanders’ exit poll share exceeded his
1) recorded share  in 24 of the 26 primaries. The probability is 1 in 190,000.
2) recorded share by greater than the margin of error in 11 primaries. The probability is 1 in 77 billion.

Is the exit poll shift to Clinton just pure luck? Or is something else going on?

TRUE VOTE MODEL BASE CASE ASSUMPTIONS

1.Sanders won the caucuses with 63.9%
2.  10% of voters  were disenfranchised  (voter rolls, provisional ballots, etc.) .
3. Sanders won 70% of uncounted votes
4. 15% of Sanders’ votes flipped to Clinton.

Sensitivity analysis tables display the effects of  flipped votes and uncounted provisional ballots  over a range of assumptions.

 Sanders NATIONAL VOTE Sensitivity Uncounted Ballots 70% of Uncounted Votes to Sanders 5% 10% 15% Machine counted Votes Flipped to Sanders Sanders Total Share 20% 51.7% 52.5% 53.2% 15% 51.2% 51.88% 52.6% 10% 50.6% 51.3% 52.0%

CALIFORNIA

Assuming a) 30% of California voters were disenfranchised, b) Sanders had 75% of provisional ballots, c) 10% of votes were flipped,  Sanders won CA with a 55% share.

On Election Day, Clinton led Sanders 56.4-43.6%.  Sanders leads in votes counted since ElectionDay by 52.3-47.7% .  This indicates that approximately 15% of Sander’s machine votes were flipped to Clinton.  Sanders  late vote share exceeded his Election Day share in every CA county. Greg Palast explains why Bernie won California.

Simple California Vote share Model

There was no exit poll, so let’s assume the following.
a) Party-ID: 57% Independents vs. 43% Democrats
(estimated based on 2014-2016 surveys)
b) Sanders won 70% of Independents

Result:
Clinton needed an implausible 85% of Democrats to match her 53.5% share.

Party-ID….PCT…… Sanders….Clinton
IND……… 57.0%….. 70.0%….. 30.0%
DEM…….. 43.0%…….15.3%….. 84.7%
Total…….100.0%….. 46.5%….. 53.5%
Recorded……………. 46.5%….. 53.5%

Sensitivity Analysis

What if: Clinton had 65% of Democrats?
Sanders would have won by 55-45%.

Assume Independents 57% vs. 43% Democrats
………………………..Sanders% IND
Sanders…….. 55% 60% 70% 75% 80%
% DEM……… Sanders Vote share
45%………….. 51% 54% 59% 62% 65%
40%………….. 49% 51% 57% 60% 63%
35%………….. 46% 49% 55% 58% 61%
30%………….. 44% 47% 53% 56% 59%
25%………….. 42% 45% 51% 54% 56%

 Clinton Sanders Margin TOTAL RECORDED 53.47% 46.53% -6.95% TRUE VOTE 48.34% 51.66% 3.32% CAUCUS Clinton Sanders Clinton Sanders Margin 36.1% 63.9% 36.1% 63.9% 27.8% IA 50.1% 49.9% 50.1% 49.9% -0.3% NV 52.7% 47.3% 52.7% 47.3% -5.3% CO 40.6% 59.4% 40.6% 59.4% 18.8% MN 38.4% 61.6% 38.4% 61.6% 23.3% KS 32.3% 67.7% 32.3% 67.7% 35.5% NE 42.9% 57.1% 42.9% 57.1% 14.3% ME 35.6% 64.4% 35.6% 64.4% 28.7% ID 22.0% 78.0% 22.0% 78.0% 56.0% UT 20.7% 79.3% 20.7% 79.3% 58.6% AK 18.4% 81.6% 18.4% 81.6% 63.3% HI 30.1% 69.9% 30.1% 69.9% 39.8% WA 27.1% 72.9% 27.1% 72.9% 45.7% WY 45.3% 54.7% 45.3% 54.7% 9.4% ND 28.5% 71.5% 28.5% 71.5% 43.0% EXIT POLL UNCTD ADJUST Clinton Sanders Clinton Sanders Margin Total 53.99% 46.01% 53.05% 46.95% -6.09% VT 13.0% 87.0% 12.6% 87.4% 74.9% NH 39.6% 60.4% 38.7% 61.3% 22.6% WI 37.0% 63.0% 36.1% 63.9% 27.8% NC 56.3% 43.7% 55.4% 44.6% -10.8% FL 64.0% 36.0% 63.1% 36.9% -26.1% SC 68.7% 31.3% 67.8% 32.2% -35.7% OH 51.9% 48.1% 51.0% 49.0% -1.9% MI 46.8% 53.2% 45.9% 54.1% 8.2% VA 62.4% 37.6% 61.6% 38.4% -23.1% MS 83.4% 16.6% 82.9% 17.1% -65.7% GA 65.7% 34.3% 64.9% 35.1% -29.7% TX 61.5% 38.5% 60.6% 39.4% -21.2% IL 48.8% 51.2% 47.9% 52.1% 4.2% IN 44.6% 55.4% 43.7% 56.3% 12.6% PA 54.7% 45.3% 53.8% 46.2% -7.5% NY 52.0% 48.0% 51.0% 49.0% -2.1% MA 46.7% 53.3% 45.8% 54.2% 8.4% CT 51.6% 48.4% 50.7% 49.3% -1.4% AZ 37.0% 63.0% 36.1% 63.9% 27.8% AL 73.2% 26.8% 72.4% 27.6% -44.8% TN 63.2% 36.8% 62.3% 37.7% -24.6% AR 66.0% 34.0% 65.2% 34.8% -30.3% MD 65.6% 34.4% 64.8% 35.2% -29.5% MO 48.1% 51.9% 47.2% 52.8% 5.7% OK 47.8% 52.2% 46.8% 53.2% 6.3% WV 39.9% 60.1% 39.0% 61.0% 22.0% NO EXIT POLL UNCTD / FLIPPED ADJUST Clinton Sanders Clinton Sanders Margin Total 54.96% 45.04% 45.77% 54.23% 8.45% CA 54.22% 45.78% 44.62% 55.38% 10.76% KY 50.2% 49.8% 41.5% 58.5% 16.9% MT 46.6% 53.4% 38.8% 61.2% 22.5% NJ 63.2% 36.8% 51.5% 48.5% -3.1% NM 51.5% 48.5% 42.6% 57.4% 14.9% SD 51.0% 49.0% 42.2% 57.8% 15.7% LA 75.4% 24.6% 61.0% 39.0% -22.0% DE 60.4% 39.6% 49.4% 50.6% 1.2% RI 44.1% 55.9% 36.8% 63.2% 26.4% OR 43.3% 56.7% 43.3% 56.7% 13.3% DC 79.5% 20.5% 64.2% 35.8% -28.4%

Based on the following table of 25 Democratic primary exit polls (assuming confirmation that the WI and CT  polls exceeded the MoE), the probability P that at least 12 would exceed the MoE is
P= 2.30E-13  or 1 in 4.3 trillion.
P= 1-binomdist (11,25,0.025,true)

Posted by on June 19, 2016 in 2016 election, Uncategorized

## 2014 NC Senate: Election models indicate that it was likely stolen

Richard Charnin
Jan. 28, 2016

Election Models indicate that the 2014 North Carolina senate election was likely stolen.
Willis (R) defeated Hagan (D) by 45,000 votes (48.8-47.3%).

I. True Vote Model

Given: Obama lost NC in 2012 by 92,000 recorded votes (50.4-48.4%).
Hagan wins by 17,000 votes (48.5-47.9%)

Assume Obama won the True Vote by 185,000 votes (51.4-47.4%),
Hagan wins by 155,000 votes (50.9-45.5%)

Base Case Assumptions
Assume Obama won in 2012 by 51.4-47.4%.

1) 60% turnout of Obama and Romney voters,
2) Hagan had 92% of returning Obama voters
3) Willis had  90% of Romney voters
4) Hagan had 47% and Willis 45% of voters who did not vote in 2012.
Hagan  wins by 155,000 votes: 50.9-45.9%

Sensitivity analysis I: Returning vote shares

Worst case scenario: Hagan has 88% of returning Obama and 5% of Romney voters.
Hagan loses by 4,000 votes with 48.1%.

Best case scenario: Hagan has 96% of Obama and 9% of Romney voters.
Hagan wins by 314,000 votes with 53.6%.

Sensitivity analysis II: 2012 voter turnout in 2014

Worst case scenario: 58% of Obama and 62% of Romney voters return in 2014.
Hagan wins by 81,000 votes with 49.6%.

Best case scenario: 62% of Obama and 58% of Romney voters return in 2014.
Hagan wins by 230,000 votes with 52.1%.

II. Voter Turnout Model

Party registration: Democrats 41.7%- Republicans 30.4%- Independents 27.8%
Exit Poll Party-ID: Democrats 36.0%- Republicans 35.0%- Independents 29.0%
Party-ID was adjusted to force a match to the recorded vote

Assumptions:
Party Registration split
61% of Democrats and 61% of Republicans turned out.
Hagan wins by 50.9-45.4% (161,000 votes).

III. Uncounted Vote Model

Given: 260,000 of 3.17 million votes cast were uncounted.
Hagan wins by 206,000 votes (51.6-45.1%)

Reclaiming Science: The JFK Conspiracy
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll
LINKS TO WEB/BLOG POSTS FROM 2004

Election Fraud Overview

Posted by on January 28, 2016 in 2014 Elections, Uncategorized

## KS 2014 Governor Election: Four models indicate fraud

Richard Charnin

Jan. 27, 2016

Four election models indicate that the 2014 Kansas governor election may have been stolen. Brownback (R) defeated Davis (D) by 33,000 votes (49.9-46.1%).

I Cumulative Vote Shares

PhD Mathematician Beth Clarkson has sued for the KS poll tapes

Clarkson has found that computer-reported results from larger precincts in the state, with more than 500 voters, show a “consistent” statistical increase in votes for the Republican candidates in general elections (and even a similar increase for establishment GOP candidates versus ‘Tea Party’ challengers during Republican primaries). Those results run counter to conventional political wisdom that Democrats perform better in larger, more urban precincts.

II True Vote Model

Obama lost Kansas in 2012 by 252,000 recorded votes (59.7-38.0%).

Base Case Assumptions
1) 66% turnout of Obama and Romney voters,
2) Davis had 93% of returning Obama voters
3) Brownback had  78% of Romney voters
4) Davis had 50% and Brownback 40% of voters who did not vote in 2012.

Base Case Scenario: Davis wins by 1,000 votes: 48.1-48.0%
Note: Obama had 42% in the final pre-election poll. If Obama’s True Vote was 41%,  then Davis won the True Vote by 50-46%.

Sensitivity analysis I: Returning vote shares

Worst case scenario: Davis has 89% of returning Obama and 17% of Romney voters.
Davis loses by 40,000 votes with 45.7%.

Best case scenario: Davis has 97% of Obama and 21% of Romney voters.
Davis wins by 41,000 votes with 50.5%.

Sensitivity analysis II: 2012 voter turnout in 2014

Worst case scenario: 64% of Obama and 68% of Romney voters return in 2014.
Davis loses by 15,000 votes with 47.1%.

Best case scenario: 68% of Obama and 64% of Romney voters return in 2014.
Davis wins by 17,000 votes with 49.0%.

III Voter Turnout Model

Exit Poll Party-ID: Democrats 25%- Republicans 47%- Independents 28%
Party registration: Democrats 24.3%- Republicans 44.1%- Independents 31.6%
62.7% of registered voters turned out.
Assumptions: 62.7% of Democrats and 62.7% of Republicans turned out.

Davis wins by 48.1-48.0%
To match the recorded vote, Brownback needed 13% of  Democrats, 79% of Republicans and 38% of Independents.

IV Uncounted Vote Model

Given: 113,000 of 962,000 votes cast were uncounted.
Davis wins by 62,000 votes (51.2-44.8%)

Look inside the books:
Reclaiming Science: The JFK Conspiracy
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll
LINKS TO WEB/BLOG POSTS FROM 2004

Posted by on January 27, 2016 in 2014 Elections, Uncategorized

## 2014 Election Fraud: Four statistical models

2014 Election Fraud:  Four Statistical Models

Richard Charnin
Dec. 28, 2015
Updated: Jan.18, 2016

This post reviews the following statistical models which strongly indicate fraud in the 2014 Governor elections:
1) True Vote Model, 2) Cumulative Vote Shares, 3) Voter Registration vs. Exit Poll Party-ID, 4) Uncounted Votes Cast (Census).

Democratic Vote Shares: Statistical Summary

 Dem Vote% CVS TVM Census D/R ExitP D/R Registered KY 43.8 49.1 48.0 49.3 na 54-39 MD * 47.2 52.9 56.4 59.1 na 55-26 WI 46.7 50.2 51.6 51.3 36-37 43-41 FL 47.1 51.1 49.7 50.9 31-35 39-35 IL 46.4 54.4 54.2 54.8 43-30 47-35 MA 46.6 55.9 55.6 56.5 na 35-11 CO 49.1 na 50.7 53.1 28-32 31-33 GA 44.8 na 48.2 52.2 35-37 39-43 KS 46.1 na 48.3 52.0 25-48 24-44 ME 43.3 na 51.5 52.3 30-31 33-27 MI 46.8 na 52.4 54.3 39-30 44-37 OH 32.9 na 37.7 41.7 32-36 41-42

Cumulative Vote Shares

The CVS method  uses actual precinct votes in each county. The data is sorted by  precinct size. The votes and shares are accumulated and  displayed graphically. Typically, in the biggest counties, Democratic shares peak at the 10% CVS mark and decline at the final 100% (recorded vote). This is counter-intuitive because a) the most populous counties are in urban locations which are strongly Democratic and b) as the number of votes are accumulated, the Law of Large Numbers (LLN) should result in a Steady state of equibrium in which Democratic and Republican vote shares are nearly constant.

Click these links to view the summary 2014 CVS analysis (each has a  link to the precinct votes for each county):  IL  FL  WI  MD MA  KY

The True Vote Model (TVM)

The 2012 presidential election is used as a basis for returning 2014 voters. There are two options for estimating  returning voters: the Recorded Vote and estimated True Vote.

The TVM closely matched the CVS in all governor elections except  for Maryland.  Hogan(R) won the recorded vote by 51.0- 47.2%, a 66,000 vote margin.  Brown(D) won the True Vote by 56.4-41.9%, a 251,000 margin. The CVS analysis understates Brown’s vote since precinct votes were provided only for Election Day; early, provisional and absentee precinct voting were not included. This omission dramatically reduced  Brown’s CVS since he had 54% of the excluded votes.  Click these links to view the 2014 Governor True Vote Model:  MD  IL  FL  WI  KY MA ME  OH KS MI GA CO

https://www.census.gov/data/tables/time-series/demo/voting-and-registration/p20-577.html

In each election cycle, the Census Bureau interviews 60,000 households nationwide to estimate  how many were registered and voted in each state.The national margin of error (MoE) is 0.3% for 60,000 respondents at the 90% confidence level. The MoE is approximately 2% for each state.

In 2014, 92.2 million votes were cast  but just 78.8 million recorded. The 13.4 million discrepancy (14.5%) was greater than in any presidential election. What is going on here?

In every one of the 1968-2012 presidential elections,  votes cast exceeded the recorded vote. The percentage of uncounted votes has declined steadily since 1988, from 10.4% to 2.9% in 2012. Uncounted votes peaked at 10.6 million in 1988 and declined to near zero in 2008. Approximately 75% of uncounted votes were Democratic (50% in minority locations).

The recorded vote was adjusted  to total votes cast by adding the uncounted votes. The majority (60-75%) of uncounted votes were  assumed to be Democratic, based on the historical fact that approximately 50% of uncounted votes are in minority locations.

True Vote (est.) = Recorded Vote + Uncounted vote

 Cast Recorded Diff Pct 1968 79.0 73.0 6.0 7.6% 1972 85.8 77.7 8.1 9.4% 1976 86.7 81.5 5.2 6.0% 1980 93.1 86.6 6.5 7.0% 1984 101.9 92.7 9.2 9.0% 1988 102.2 91.6 10.6 10.4% 1992 113.9 104.4 9.5 8.3% 1996 105.0 96.4 8.6 8.2% 2000 110.8 105.6 5.2 4.7% 2004 125.7 122.3 3.4 2.7% 2008 131.1 131.4 -0.3 -0.2% 2012 132.9 129.1 3.8 2.9% 2014 92.2 78.8 13.4 14.5%

Political Party Strength

The simplest measure of  party strength in a state’s voting population is the breakdown-by-party totals from its voter registration statistics from the websites of the Secretaries of State or the Boards of Elections. As of 2014, 28 states and the District of Columbia allow registered voters to indicate a party preference when registering to vote.

In 2014,  the party voter preference/registration split was 40.5D-35.3R-24.2I.  The  2014 National Exit Poll indicated a 35D-36R-28I Party-ID split in forcing a match to the recorded vote (Dem 46.2-Rep 52.9%). Assuming the voter registration split and the Party-ID vote shares, the Democrats  and Republicans were essentially tied.

The registered voter split for the 12 Governor elections in this analysis was 40.6D-34.4R-24.4I, a very close match to the national split.

These 22 states do not allow party preference in voter registration:
Alabama,Arkansas, Georgia, Hawaii, Idaho, Illinois, Indiana, Michigan, Minnesota, Mississippi, Missouri, Montana, North Dakota, Ohio, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington and Wisconsin.

The partisan “demographics”  were obtained from the state’s party registration statistics (in late 2014 whenever possible). For the 22 states that don’t allow registration by party, Gallup’s annual polling of voter party identification is the next best metric of party strength.

National Exit Poll 2014 Party-ID (match recorded vote)

 Party-ID Split Dem Repub Ind Dem 35% 92% 7% 1% Rep 36% 5% 94% 1% Ind 29% 42% 56% 4% Total 100% 46.2% 52.5% 1.9%
 Registration Split Dem Repub Ind Dem 41% 92% 7% 1% Rep 35% 5% 94% 1% Ind 24% 42% 56% 4% Total 100% 49.6% 48.7% 1.7%

Matching the Recorded and True Vote using the Party Registration split

To match the recorded vote,  an implausibly low  percentage of Democrats had to have voted for the Democratic candidate. Note the difference between  the percentage of Democrats required to match the recorded vote and True vote shares. Democratic and Republican candidates usually win approximately 90-92% of  registered Democrats and Republicans, respectively.

Percentage Share of Registered Democrats Required to Match

 Match Recorded Vote True      Vote Match Recorded True Vote MA 58.5 83.9 ME 66.6 92.0 MD 68.9 84.9 OH 53.5 92.1 KY 72.4 81.7 KS 86.1 95.5 WI 88.8 94.7 MI 75.5 88.1 FL 81.3 88.1 GA 87.7 93.3 IL 83.0 91.0 CO 88.6 93.8

KENTUCKY

Conway (D) lost the recorded vote  by 52.5-43.8% despite the fact that the Democrats led 53.4-38.8% in voter registration. Bevin needed an implausible 24.6% of Democrats to match the recorded vote. Assuming just 81.7% of Democrats voted for Conway, he won by 48.8-47.5%, closely matching the CVS and True Vote.

According to the 2014 Census, 1.525 million total votes were cast in 2014.  In 2015, Conway won by 49.3-47.0% – assuming he had 60% of an estimated 50,000 uncounted votes.

Of 2,298,000 registered  voters, 974,000 (42.4%) voted. If  39% of Democrats voted and Conway had 88%, he won by 49.0-47.3%. If 43% of Democrats voted, he won by 54-42.3%.

 Party Reg Split Conway Bevin Curtis Democrat 53.4% 72.4% 24.6% 3% Republican 38.8% 4% 92% 4% Other 7.8% 46% 47% 7% Recorded 100% 43.8% 52.5% 3.7% Votes (000) 974 427 511 36

 Party Reg Split Conway Bevin Curtis Democrat 53.4% 81.7% 15.3% 3% Republican 38.8% 4% 92% 4% Other 7.8% 46% 47% 7% True Vote 100% 48.8% 47.5% 3.7% Votes (000) 974 475 463 36

 Votes Cast Total Conway Bevin Curtis Recorded 974 426.6 512.0 36.0 Uncounted (est) 50 30.0 18.2 1.9 2014 Census 1024 505 481 38 Adj. Share 49.3% 47.0% 3.7%

 Party Reg Split Conway Turnout 39% 41% 43% Democrat 53.4% 88% 18.3% 19.3% 20.2% Repub 38.8% 6% 0.9% 1.0% 1.0% Other 7.8% 50% 1.5% 1.6% 1.7% Conway 49.0% 51.5% 54.0% Bevin 47.3% 44.8% 42.3%

MARYLAND

Hogan (R) won the recorded vote by 51.0-47.3%. Brown(D) had 53.7% of early votes, 45.3% on Election Day and 54.5% of absentee and provisional ballots. Precinct votes on touchscreens and optical scanners were provided for Election Day only.

When 295,000 uncounted votes are added to the recorded vote, Brown is the winner by 51.2-47.0% .  When  Election Day CVS  at the 10% mark is added to the 390,000 early, absentee and provisional votes, Brown is a  52.9-45.5% winner.

 Census Votes (000) Total Brown Hogan Other True 1,733 977 726 30 Adjusted (Unctd) 295 221 68 5 Total Census 2,028 1,198 794 35 Share 59.1% 39.2% 1.7%

 Voter Reg Pct Brown Hogan Other Democrat 54.9% 84.9% 13.1% 2.0% Republican 25.7% 4% 95% 1.0% Other 19.4% 45% 53% 2.0% Share 100% 56.4% 41.9% 1.7% Total 1,733 977 726 30
 Adjusted Total Brown Share Hogan Share Other Share Early 306 164 53.7% 137 44.8% 4.5 1.5% Election Day 1,342 608 45.3% 711 52.9% 23.5 1.7% Absentee/prov 85 46 54.5% 37 43.4% 1.8 2.1% Recorded 1,733 819 47.25% 884 51.0% 30 1.7% CVS adj Early/abs/prov 390 210 53.9% 174 44.5% 6 1.6% CVS @ 10% 1,391 693 52.5% 603 45.7% 23 1.7% Adj. Total 1,709 904 52.9% 777 45.4% 29 1.7%

Notes:- Beth Clarkson, a PhD in statistics, did an analysis of 2014 cumulative vote share anomalies: How Trustworthy are Electronic Voting Systems in the US

– A statistical study by G.F. Webb of Vanderbilt University reveals a correlation of large precincts and increased fraction of Republican votes:Precinct  Size  Matters: ­ The  Large  Precinct  Bias  in  US  Presidential  Elections

– Francois Choquette and James Johnson exposed anomalies in the 2012 primaries:2008/2012 Election Anomalies, Results, Analysis and Concerns

– Kathy Dopp is a mathematician and an expert on election auditing. She has written a comprehensive analysis of the 2014 elections:
Were the 2014 United States Senatorial and Gubernatorial Elections Manipulated?  Dopp wrote:
Is it possible electronic vote-count manipulation determines who controls government in the United States? The probability that the disparities between predicted and reported 2014 election vote margins were caused by random sampling error is virtually zero. A method for extending and simplifying fuzzy set qualitative comparative analysis (FsQCA)’s measure for necessity reveals that lack of effective post-election audits is a necessary condition for the occurrence of high levels of disparity between statewide polls and election results. Maryland’s 2014 gubernatorial contest is consistent with an explanation of vote miscount having altered its outcome.  An analysis of Maryland’s partisan voter registration, turnout, and vote data by ballot type statistically confirms vote miscount as an explanation for its unexpected outcome.

Maryland, Illinois, Florida, and Kansas gubernatorial contests exhibited sufficient disparities between polls and election results (PED) to alter election outcomes; all used inauditable voting systems or failed to conduct post-election audits (PEA)s. Vermont’s PED was within one percent of sufficient to alter its outcome. In Nevada, Tennessee, New York, Ohio, and South Dakota PED were large but smaller than winning margins.

Kansas and North Carolina senatorial contests exhibited sufficient PED to alter election outcomes and no audits were conducted. Virginia’s PED was within one percent of sufficient to alter its outcome. In Arkansas, Wyoming, Tennessee, Kentucky, and Nebraska PED magnitude were large but smaller than winning margins.

A case study of Maryland’s unexpected 2014 gubernatorial outcome affirms there is, as yet, only an explanation of vote manipulation consistent with the statistical disparity patterns in Maryland’s pre-election poll predictions, and its partisan voter registration, turnout and vote data by ballot type.

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Look inside the books:
Reclaiming Science: The JFK Conspiracy
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Exit Poll

A Collage of Election Fraud Graphics

LINKS TO WEB/BLOG POSTS FROM 2004