RSS

Category Archives: 2011 Wisconsin Supreme Court & Recall Elections

2011 WI Supreme Court: Cumulative Vote Shares confirm the Stolen Election

2011 WI Supreme Court: Cumulative Vote Shares confirm the Stolen Election

Richard Charnin
Updated Feb.14, 2016

Look inside the books:  Proving Election Fraud –  now a $3.99 E-Book
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Reclaiming Science: The JFK Conspiracy
Compendium of Links to all of my posts
2004 Election Fraud: Confirmation of a Kerry Landslide
1988-2012 Presidential Elections: The Master Spreadsheet
1968-2012 Presidential True Vote Model
Cumulative Vote Shares: Indicators of Rigged Elections
Cumulative Vote Share Spreadsheet Reference

This is an update to a previous 2011 Wisconsin Supreme Court True Vote Analysis It referenced the analysis here.

Before discussing the CVS (below), a quick review: Kloppenburg (Independent) apparently won the election by 200 votes. But two days later, 14,000 votes were “found” in Waukesha County. Prosser (Republican) was declared the unofficial winner by 7,000 votes. The subsequent recount was a travesty. Scores of slit ballot bags and poll tapes dated a week before the election were uncovered. A stack of 50 consecutive Prosser ballots were found in Verona where Kloppenburg won 67% of the recorded vote – a zero probability.

The 2011 WI Supreme Court True Vote Model was enhanced to calculate the True Vote in all counties. It indicated that Kloppenburg won the election. Assuming a 50% turnout of both Obama and McCain voters, the recorded margin required implausibly low 81% Kloppenburg share of returning Obama voters while Prosser had 93% of returning McCain voters.

Assuming Kloppenburg actually had 88% of returning Obama voters and just 50% of 70,000 returning third-party and new voters, then she won by 99,000 votes with a 53.3% vote share. The Cumulative Vote Share analysis confirms the True Vote Model: Kloppenburg had 53.5% in the TVM and 52.2% at the CVS 25% mark.

As previously shown in the 2014 WI Governor and in the 2012 Recall election, CVS anomalies occurred in the largest counties where the average ward vote is higher than in smaller, rural (heavily GOP) counties. Overall, there was a 2.47% decline (37,000 votes) in Kloppenburg’s vote share from the 25% mark. But there was a 5.0% decline (42,000 votes) in the Top 10 counties in which 56% of the votes were cast. Kloppenburg gained nearly 9,000 votes in the 52 smallest counties, a confirmation that they were effectively ignored by the GOP.

In Milwaukee County,  Kloppenburg had 74% at the 26,000 vote mark but ended up with 57% at the final 228,000. The 17% decline meant that 38,000 votes were flipped to Prosser – a 76,000 decline in margin! She “lost” by 7,000. Click for the Milwaukee County CVS chart.

In Waukesha County, Kloppenburg’s vote share dropped 3,400 votes from 28.9% at the 25% mark to 25.2% at the final – a nearly 6,800 decrease in margin and close to the magical Waukesha vote adjustment which gave the election to Prosser.

Note that the declines (discrepancies) may actually be greater than above as they reflect changes from the 25% CVS mark – not from the start to the 25% count.

The results confirm previous counter-intuitive findings that Republicans consistently gain share in the most populated counties where precincts/wards are usually heavily Democratic. There were virtually no vote share changes in small, heavily Republican rural counties. In fact, Democrats and Independents often gain vote share from the 25% mark in these counties.

Kloppenburg lost 46,000 votes from the 25% mark in the largest 20 counties. She lost share in 15 of the largest 18 counties, but gained share in 37 of the smallest 54. She actually gained 7,000 votes in the smallest 52 counties. She lost 40,000 votes in Democratic counties in which she led at the 25% mark, but gained 1,000 votes in (Republican) counties in which she had less than 50%. Kloppenburg actually gained share in the smallest 52 counties.

As in recent WI Governor elections, vote share declines were highest in Milwaukee (11%, 25000 vote loss), Racine (15%, 7600), Waukesha (2.7%, 3300), Kenosha (10.3%, 3100) and Winnebago (5.6%, 2100).

Kloppenburg’s vote shares were higher in the smallest (0-50%) precincts compared to the largest (50-100%).
Milwaukee 64%> 50%
Brown 47 > 43
Kenosha 58 > 48
Racine 52 > 36
St. Croix 50 > 48
Waukesha 27 > 25
Winnebago 51 > 45

Once again, the evidence shows that Republicans steal elections in big urban counties that are strongly Democratic and ignore small rural counties where they are dominant.

Kloppenburg CVS by County Group Size
Counties Votes Final...25%...Change Votes
All 1,498,880 49.70% 52.17% -2.47% -36,995

01-10 840,510 51.13% 56.15% -5.02% -42,224
11-20 262,200 45.28% 46.60% -1.32%. -3,459
21-30 148,615 50.10% 47.15%. 2.95%.. 4,381
31-40. 94,724 48.54% 46.69%. 1.85%.. 1,749
41-50. 68,722 49.78% 48.85%. 0.93%…..638
51-72. 80,835 51.86% 49.48%. 2.38%…1,926

To appreciate the vote changes, think of the starting 10,000 votes as a poll with a 1% margin of error. Move the cursor over the CVS trend line to view the exact vote count and share.
Milwaukee County Steady 17% decline from 74% at 25,000 to 57% at the final 228,000.
Brown After leading at 10,000 votes, Kloppenburg’s share declines to 45% at the final 61,000.
Kenosha Steady, massive decline from 65% at 3,000 votes to 53% at the final 31,000.
Racine Strange decline from 60% at 10,000 votes to 45% at the final 51,000.
St. Croix Coincident shares all the way to the final 16,000. Was St. Croix legit?
Waukesha The biggest GOP stronghold, but is it this strong? Kloppenburg gained shares in smaller GOP counties, but not in Waukesha where her share declined from 32% at 10,000 votes to 26% at the final 125,000.
Winnebago Decline from 55% at 5,000 votes to 48% at the final 40,000.

 

Tags: , , , , ,

Wisconsin 2014 Governor: Cumulative Ward Voting Indicates Fraud

Wisconsin 2014 Governor: Cumulative Ward Voting Indicates Fraud

Richard Charnin
Dec.2, 2014
Updated: Dec.2, 2015

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

Index of Wisconsin Blog Posts

JFK Blog Posts
Probability/ Statistical Analysis Spreadsheets:
JFK Calc: Suspicious Deaths, Source of Shots Surveys;
Election Fraud: True Vote Models, State and National Unadjusted Exit Polls

A total of 2,382,055 votes were recorded:
Burke: 1,112,260 (46.69%)
Walker:1,242,413 (52.16%)
Other: 27,383 (1.15%)

The 2014 Wisconsin Governor Cumulative County vote share analysis for all units/wards is available in a spreadsheet for viewing. Vote shares are sorted by increasing ward size for each county. The graphs look strikingly similar to the equivalents in the 2012 recall (especially Milwaukee and Racine). This indicates that the 2012 vote theft strategy was repeated in 2014. If it worked in the recall, why change it? Cumulative vote graphs for the largest counties are located adjacent to the unit/ward vote counts.
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdEhqXzdlbUhZT1Vic3RSQmU2cUVkc3c&usp=sharing#gid=9

In Milwaukee County, Walker vote shares increased as a function of Unit/Ward size. The increase can be considered as evidence of fraud. One would expect that the lines would be nearly parallel after 90,000 votes. But even parallel lines could indicate constant fraud throughout the county. See the graph below.

Examination
 of 
precinct 
level 
data
 in 
US
 presidential 
elections 
reveals
 a 
correlation
 of
 large
 precincts 
and 
increased
 fraction
 of
 Republican
 votes.

Click to access 1410.8868.pdf

The counties that look the most suspicious by the upward slope of Walker shares in large units and wards are Ashland, Brown, Kenosha, Dane, Eau Claire, Jefferson, Milwaukee, Racine, Sheboygan, Winnebago, Waukesha. Of course, a flat line could indicate that fraud is uniform throughout the county.

The spreadsheet can be used as a reference. It will be be enhanced in the near future. Let me know what features you would like to see. View the voting data and graphics in the sheets: Adams-Menominee and Milwaukee-Wood.

Wisconsin 2014 Governor: Total State Cumulative Vote shares
Note how Walker’s vote share initially declines in the smallest wards and reverses trend as the size of wards increase (at the 1 million mark). This is counter-intuitive: Walker’s share should continue the downward trend since larger wards are generally in Democratic strongholds (Dane, Milwaukee..).

Cumulative Vote Shares
https://docs.google.com/spreadsheet/ccc?key=0AjAk1JUWDMyRdEhqXzdlbUhZT1Vic3RSQmU2cUVkc3c&usp=sheets_web#gid=12

County size
Burke had 55.9% in the TOP 15 counties at the 10% CVS mark. Added to the final recorded shares of the other 57 counties, Burke won the election by 52.0-46.9%.

Burke’s total vote dropped by 61,000  from the 25% mark.
Her share fell by 4.8% in the largest 15 counties , increased by 2.4% in the middle 15 and by 0.8% in the 15 smallest . This is a strong indicator of fraud in the biggest counties.

Democratic strongholds
Burke’s share fell by 6.5% in counties in which she was leading at the 25% cumulative vote mark. This is an indicator of fraud in Democratic strongholds.

Correlation
There was a -0.39 statistical correlation between the change in Burke’s total county shares and county vote size and a  corresponding -0.35 correlation in the Democratic-leaning counties (at least 50% at the 25% mark). The correlation is near zero in the middle 15 and smallest 15 counties. This is another indicator of fraud in the largest counties (primarily Milwaukee).

Democratic Vote Share Trend: 5 elections,  15 counties
Obama had 63% in the unadjusted 2008 WI exit poll and 56% recorded, closely matching his CVS share for 15 counties. The unadjusted exit polls are not available for 2010-2014.


Election........Votes. 25%...50%..100% Change
2008 Obama......1853.. 62.4 60.6 57.1... 5.3
2010 Feingold...1375.. 54.7 52.4 48.7... 6.0
2010 Barrett....1372.. 55.0 51.9 48.2... 6.8
2012 Barrett....1551.. 54.2 52.1 48.1... 6.1
2014 Burke......1511.. 54.0 52.2 48.5... 5.5

Burke.......Vote...25%..50%..75%..100%.....Correl Chg
Total.......2385..49.2 48.6 47.5 46.7.... -0.39 -2.6

Top 15......1573.. 53.5 52.0 50.2 48.6... -0.23 -4.9
Mid 15.......242.. 41.0 41.2 41.6 43.1.... 0.01 2.1
Low 15........73.. 43.5 42.6 42.7 43.7.... 0.11 0.2

Burke >50... 935.. 67.3 65.2 62.7 60.8.... -0.35 -6.5

County....... Vote..25 50 75 100% .................. %chg Votechg
Adams............ 8.. 46 46 47 46....................... 0 0.00
Ashland.......... 7.. 59 61 64 63....................... 4 0.26
Barron.......... 17.. 39 39 40 41....................... 2 0.34
Bayfield......... 8.. 54 57 57 61....................... 7 0.56
Brown.......... 100.. 46 44 42 41...................... -5 -5.01

Buffalo.......... 6.. 40 38 40 41....................... 1 0.06
Burnett.......... 7.. 39 41 41 40....................... 1 0.07
Calumet......... 21.. 29 34 34 34....................... 5 1.07
Chippewa........ 25.. 37 42 42 42....................... 5 1.23
Clark........... 11.. 36 34 34 34...................... -2 -0.23

Columbia........ 25.. 43 48 50 51....................... 8 1.98
Crawford......... 6.. 55 55 43 51...................... -4 -0.25
Dane........... 240.. 70 71 71 70....................... 0 0.00
Dodge........... 57.. 29 32 34 36....................... 7 3.98
Door............ 15.. 43 44 45 45....................... 2 0.30

Douglas......... 16.. 54 58 61 61....................... 7 1.10
Dunn............ 15.. 41 43 43 46....................... 5 0.76
EauClaire....... 42.. 49 52 52 50....................... 1 0.42
Florence......... 2.. 28 30 30 31....................... 3 0.06
FonduLac........ 43.. 34 37 36 35....................... 1 0.43

Forest........... 4.. 51 47 44 44...................... -7 -0.25
Grant........... 19.. 44 45 46 48....................... 4 0.77
Green........... 15.. 49 48 51 52....................... 3 0.46
GreenLake........ 7.. 30 28 30 31....................... 1  0.07
Iowa............ 11.. 55 54 55 56....................... 1  0.11

Iron............. 3.. 40 37 38 38...................... -2 -0.06
Jackson.......... 8.. 48 47 47 48....................... 0 0.00
Jefferson....... 36.. 43 39 38 39 ..................... -4 -1.43
Juneau........... 9.. 44 43 45 45....................... 1 0.09
Kenosha......... 56.. 60 57 52 48..................... -12 -6.78

Kewaunee......... 9.. 38 35 33 37...................... -1 -0.09
La Crosse....... 48.. 57 55 54 53 ..................... -4 -1.94
Lafayette........ 6.. 44 44 46 48....................... 4 0.25
Langlade......... 8.. 34 36 36 34....................... 0 0.00
Lincoln......... 12.. 42 43 43 42....................... 0 0.00

Manitowoc....... 34.. 30 34 37 37....................... 7 2.38
Marathon........ 57.. 34 37 38 38....................... 4 2.26
Marinette....... 15.. 35 34 36 38....................... 3 0.46
Marquette........ 6.. 44 41 42 42...................... -2 -0.13
Menominee........ 1.. 75 75 75 75....................... 0 0.00

Milwaukee...... 368.. 74 70 66 63 .................... -11 -40.48
Monroe.......... 15.. 38 38 31 42....................... 4 0.60
Oconto.......... 16.. 34 34 34 35....................... 1 0.16
Oneida.......... 17.. 48 43 43 42...................... -6 -1.02
Outagamie....... 74.. 42 42 41 39...................... -3 -2.22

Ozaukee......... 47.. 30 31 29 29...................... -1 -0.47
Pepin............ 3.. 40 39 42 42....................... 2 0.06
Pierce...........15.. 41 43 43 46....................... 5 0.75
Polk.............16.. 41 41 41 41....................... 0 0.00
Portage......... 30.. 45 50 51 50....................... 5 1.50

Price............ 7.. 39 38 40 42....................... 3 0.21
Racine.......... 80.. 62 55 48 45..................... -17 -13.60
Richland......... 7.. 55 51 49 48...................... -7 -0.49
Rock............ 58.. 55 55 56 56....................... 1 0.58
Rusk............. 6.. 40 36 37 39...................... -1 -0.06

Sauk............ 26.. 55 55 56 56....................... 1 0.26
Sawyer........... 7.. 41 41 43 44....................... 3 0.21
Shawano......... 17.. 37 34 35 34...................... -3 -0.51
Sheboygan....... 50.. 42 39 38 36...................... -6 -3.00
St. Croix....... 34.. 39 41 41 39....................... 0 0.00

Taylor........... 8.. 32 28 27 29...................... -3 -0.24
Trempeleau...... 11.. 48 49 48 46...................... -2 -0.22
Vernon.......... 12.. 47 48 48 50....................... 3 0.36
Vilas........... 11.. 34 35 38 37....................... 3 0.33
Walworth........ 40.. 36 36 34 35...................... -1 -0.40

Washburn......... 7.. 42 41 42 43....................... 1 0.07
Washington...... 66.. 26 25 24 23...................... -3 -1.98
Waukesha....... 203.. 30 29 28 27...................... -3 -6.09
Waupaca......... 21.. 32 34 34 36....................... 4 0.84
Waushara........ 10.. 38 35 37 37...................... -1 -0.10

Winnebago....... 69.. 46 46 46 44...................... -2 -1.38
Wood............ 31.. 36 38 40 41....................... 5 1.55

Compare the 2012 recall cumulative county vote trend analysis to 2014. https://richardcharnin.wordpress.com/2012/12/09/walker-recall-county-cumulative-vote-trend-by-ward-group/

https://docs.google.com/spreadsheets/d/1Fkvjx_XW-VuJ89WlTONZoSLiqj-T-8RakeV0_N9iqyQ/pubchart?oid=2140563995&format=interactive

 

Tags: , ,

Five Wisconsin Elections: A Pattern of County Unit/Ward Vote Share Anomalies

Five Wisconsin Elections: A Pattern of County Unit/Ward Vote Share Anomalies

Richard Charnin
Dec. 23, 2012
Updated: Aug.2, 2015

The purpose of this analysis is to determine if there were repetitive patterns in the cumulative county vote shares in five recent Wisconsin elections. The patterns are obvious; the county graphs are virtual duplicates.

This post is a work-in-process, but since the data tables and graphs are completed, I wanted to make them available while the analysis is ongoing.

The following counties appear most anomalous: Brown, Dane, Jefferson, Kenosha, La Croix, Milwaukee, Oneida, Ozaukee, Racine, Richland, Sheboygan, Trempealeau, Walworth, Washington, Waukesha and Winnebago.

Republican vote shares are increasing (lines slope upward) while Democratic shares decrease (slope downward) at the same rate. This is an indicator of likely vote switching.

Summary of Key Walker Recall Results
Walker won the recall by 171,000 votes (53.1-46.8%).

In 15 large counties, Barrett’s vote shares at 25%, 50% and 100% of the cumulative total were 54.2%, 52.1% and 48.1%, respectively. The counties had 1.51 million of the total 2.52 million recorded votes.

Milwaukee County is the largest and most anomalous. In the recall, Barrett had 63.3% of the total 396,000 votes. But he had 74.4% at the 25% mark, 70.4% at 50% and 66.5% at 75%. Looking at Barrett’s shares in terms of remaining votes, he had 59.4% of the final 75%, 55.9% of the final 50% and 53.0% of the final 25%. In other words there was a 21.4% decline in Barrett’s 74.4% vote share of the first 100,000 votes to 53.0% in the final 100,000 votes.

Barrett’s True Vote Model 54.4% share is within 0.2% of his 15 county cumulative share at the 25% mark. His total Wisconsin share (assuming an equal level of fraud in the other 57 counties) was 52.4%.

In the 15 counties, there was a 6.0% difference between Barrett’s 54.2% at the 25% mark and his final 48.1%. Adding 6.1% to Barrett’s official 46.3% total share, he had an estimated 52.4% Wisconsin True Vote share.

In the 15 counties, there was a 4.0% difference between Barrett’s 52.1% at the 50% mark and his final 48.1%. Adding 4.0% to Barrett’s official 46.3% total share, he had an estimated 50.3% Wisconsin True Vote share.

2008 Presidential Election
The cumulative vote analysis essentially confirmed the unadjusted exit poll. Obama won the WI recorded vote by 56.2-42.7%. He won the unadjusted exit poll 63.3-35.7%, a 7.1% increase over the recorded vote share.

In 15 of the largest counties, Obama’s vote shares at the 25%, 50% and 100% of the cumulative total were 62.4%, 60.6% and 57.1%, respectively. The counties had 1.85 million (62%) of the 2.98 million total recorded votes.

Democratic votes shares declined by an average of 6.0% from the 25% mark to the final recorded vote:


15 Wisconsin Counties
Democratic Vote Share Trend
15 Wisconsin Counties
(Votes in thousands)
.................... Percent of total vote

15 Counties Votes 25% 50% 100% Change
2008 Obama 1853 62.38% 60.59% 57.07% 5.31%
2010 Feingold 1375 54.70% 52.38% 48.69% 6.02%
2010 Barrett 1372 55.04% 51.86% 48.23% 6.81%
2012 Barrett 1551 54.24% 52.11% 48.14% 6.10%
2014 Burke 1511 53.96% 52.22% 48.50% 5.46%

The Spreadsheets
The following spreadsheets use data provided by GAB. Note that Milwaukee County is displayed at the top of the screen in each spreadsheet to illustrate the similar cumulative vote pattern in each of the four elections.

2014 Governor
https://richardcharnin.wordpress.com/2014/11/12/wisconsin-2014-governor-true-voteexit-poll-analysis-indicates-fraud/

2012 Walker recall (contains voting machine types for each county and municipality).

2010 Governor

2010 Senate

2008 Presidential

In the process of working on analysis of Wisconsin elections, I have developed a number of models and databases which are available online as Google Doc spreadsheets. They can be linked to from the following posts:

https://richardcharnin.wordpress.com/category/2011-wisconsin-supreme-court-recall-elections/

 

Tags: , , ,

Walker Recall: County Cumulative Vote Shares by Increasing Unit/Ward Size

Walker Recall: County Cumulative Vote Shares by Increasing Unit/Ward Size

Richard Charnin
Dec.18,2012
Updated: Oct.28, 2013

This is a cumulative vote trend analysis of the Walker Recall by increasing unit/ward vote counts. The data had already been included in The Walker Recall True Vote Database Model. Each county was sorted by size of Unit/Ward. Cumulative vote shares for Walker and Barrett were calculated and the graphs were generated.

The cumulative vote trend graphics is similar to Francois Choquette’s. analysis of the GOP Primaries and Prop.37.

Note the upward sloped lines for Walker in Milwaukee, Racine, Winnebago, Waukesha counties. The Law of Large numbers is violated; we would expect flat or slightly upward sloping lines for Barrett since Democratic shares are usually higher in larger urban wards than in smaller rural ones.

If the lines are flat or upward sloping for Walker, this is an indicator of vote miscount favoring Walker.

Examination
 of 
precinct 
level 
data
 in 
US
 presidential 
elections 
reveals
 a 
correlation
 of
 large
 precincts 
and 
increased
 fraction
 of
 Republican
 votes. 

Click to access 1410.8868.pdf

The Law of Large Numbers

As the vote count increases, the cumulative vote shares should hardly change (the lines should be nearly flat). But if they diverge, there must be some external factor causing it. It could very well be the FRAUD FACTOR.

Consider this baseball analogy. Why do batting averages fluctuate so greatly in the spring, but less and less as the season progresses? The Law of Large Numbers. Batting average= Total base hits/Total At Bats

Vote share for Walker= Walker Votes/Total Votes (but the Law of Large numbers was violated in the election)

The following counties appear to be the most anomalous: Brown, Milwaukee, Ozaukee, Racine, Richland, Shawano, Sheboygan, Walworth, Waukesha and Winnebago. Why would Barrett’s vote shares in Milwaukee County decline with increasing ward size? Presumably, larger wards are more Democratic than smaller wards. If anything, one would expect the lines to DIVERGE OR AT LEAST REMAIN PARALLEL – NOT CONVERGE.

The Wisconsin True Vote Model indicated that Barrett had 66.0% in Milwaukee compared to his 63.6% recorded share. In Brown, 52.2% vs. 40.0%, Racine 51.5% vs. 46.9%, Sheboygan 47.4% vs. 35.3%; Winnebago 53.5% vs. 43.6%.

Why would Barrett’s Milwaukee County cumulative BLUE vote shares decline while Walker’s RED shares slope upward? It’s a red flag which indicates vote miscounting.

Winnebago County

 

Tags: ,

The Walker recall: A correlation analysis of voting machines

The Walker recall: A correlation analysis of voting machines

Richard Charnin
Sept. 25, 2012

The purpose of this Walker recall voting machine analysis is to determine the effect of paper ballots, touch screens (DRE) and optical scanners on county and municipal vote shares.

Note that this analysis is not as complete as it should be. There is no breakdown of votes in locations where there were several types of voting machines. Only the voting machine percentages are available. The analysis will be updated when and if votes in each location by machine type are released.

We need the data in the same form as used in an analysis of Winnnebago County vote counts in which probabilities of vote share differentials between the two types of machines in the same unit/ward were calculated. Theoretically differences in the shares should have been minimal, say within 5%. But there were much larger discrepancies in a number of locations.

Using the municipal voting machine mix, there was a negative (-.24) correlation between Barrett’s county vote shares and corresponding percentage of total votes cast on DREs. Overall Barrett did better on paper ballots (.11) and optical scanners(.14). As the percentage of votes cast on DREs increased, so did Walker’s share.

The source of the data is the Wisconsin Government Accounting Board Form 190- Voting by Type of Equipment. I created this spreadsheet for the correlation analysis.

Of the 59 counties Walker won, 54 used touchscreens (DREs). But the majority of votes were cast on optical scanners.

In the 13 counties Barrett won, just five had DREs. These were the percentages of DRE votes: Iowa (76%), Eau Claire (21%), Kenosha (12%), Columbia (0.2%) and Milwaukee (0.5%). The total number of DREs was negligible in the counties.

Several correlations were calculated. The first set was to determine if there was a relationship between the municipal vote shares and the percentage of DRE votes cast in each municipality.

The correlation between votes cast on optical scanners and county vote size was 0.45. The larger counties used optical scanners almost exclusively. The correlations were -0.41 for DREs and -0.31 for paper ballots. DREs and paper ballots were mostly used in smaller counties.

In addition, correlation ratios measured the strength of the relationship between voting machines and county vote shares. Voters were encouraged to use DRE’s rather than paper ballots.

In the counties Walker won, Barrett’s vote shares were positively correlated to the percentage of paper ballots (.20) and to votes cast on DREs (0.17). His shares were negatively correlated to optical scanners (-0.21).

In the top ten Walker counties (highest vote shares), 85% of votes were cast on optical scanners, 10.7% on DREs. In the top ten Barrett counties, 96% of votes were cast on optical scanners, 1.2% on DREs.

In counties won by Walker, 76% of votes were cast on scanners, 18% on DREs.
In counties won by Barrett, 95% of votes were cast on scanners, 2.7% on DREs.

Winnebago County- Cumulative Vote Shares

 

Winnebago County Walker Recall: A Probability Analysis of Differences between Optical Scan and Touch Screen Vote Counts

Winnebago County Walker Recall: A Probability Analysis of Optical Scan and Touch Screen Vote Counts

Richard Charnin
Aug. 14, 2012
Updated: Oct.27, 2013

Three independent models analyzed the Walker Recall election in Winnebago County. This post focuses on a probability analysis of DRE vs. Optical scanners. Summaries and links to the Cumulative County Vote Share graphical analysis and the County/Muni True Vote Model are also included. The three models confirm the very high probability of fraud.

Assume that the votes cast on Optical scanners and Touch screens are given for a location (ward, precinct). All things being equal, the vote shares should be nearly identical. But if they are not equal, is the difference significant? And if the difference is significant, what is the probability that it would be due to chance?

Note that this is not an exit poll analysis. The probabilities are based on actual recorded votes.

The probability of the discrepancy is a function of the following:
1) number of optical scanners and touch screens
2) vote share percentages on each

If the optical scan ballots are hand-counted, we can calculate the number of touch screen votes and vote shares by subtraction. We can then determine if the difference in vote shares between the touch screens and optical scanner is significant.

This spreadsheet is a probability calculator for the discrepancy between optiscan and touchscreen vote shares in a given ward/precinct.

The Z-score is based on the bell-curve (normal distribution). Z determines the probability of the difference between the touch screen and optical scan vote shares. If
Z = 1.65, the probability is 95.2% that the difference between touch screen and optical scan vote shares was not due to chance. Election Fraud is likely.
Z = 1.96, the probability is 97.5%
Z = 2.33, the probability is 99.0%

Assume that in a given location, we have:
nv = total number of votes
ns = number of optical scan ballots
wv = Walkers total vote
ps = Walker’s vote share on optical scanners

Then we can easily determine
nt = number of TSX (touch screen) votes = nv – ns
pt = Walker’s TSX share

We can then calculate the probability of the difference in vote shares between the optical scanners and touchscreens:
1) Difference in vote shares: Diff = pt-ps
2) Standard error: Std = sqrt [ps*(1-ps)/ns + pt*(1-pt)/nt]
3) Z-score = ABS(Diff) / Std
4) Probability (Diff) = 2-2*NORMSDIST(Z)

The following table is based on the Winnebago County spreadsheet in the 2012 Wisconsin Recall True Vote Model. It shows that the large discrepancies between Opscan and TSX shares in the following locations could not have all been due to chance.

Model 1. Winnebago Muni DRE/Opscan Differential Vote Share Probability (Walker 2-party%)

Location.....Opscan...TSX.....Diff..ZS..Prob

Menasha(3,5,6).65.64% 60.23% -5.41% 1.89 5.82%
Neenah.........63.14% 73.21% 10.08% 1.67 9.45%
Poygan.........62.81% 72.66% 9.85% 2.68 0.74%
Rushford.......58.48% 65.92% 7.44% 2.10 3.59%
Utica..........66.67% 75.17% 8.51% 2.08 3.78%

Neenah(13-16)..60.32% 53.68% -6.63% 1.74 8.20%
Neenah(17-20)..51.31% 64.10% 12.79% 2.77 0.57%
Oshkosh(5).....42.73% 32.98% -9.75% 1.85 6.46%
Oshkosh(15)....50.76% 40.00% -10.76% 2.89 0.38%
Oshkosh(17)....40.48% 47.66% 7.18% 1.91 5.66%

Oshkosh(28A)...44.96% 52.58% 7.61% 1.88 5.98%
Oshkosh(29A)...60.63% 73.68% 13.06% 2.24 2.48%

Poygan Village Votes Pct
2-Party Total

Vote Count.... 662 100%
Optiscan...... 406 61.33%
TSX DRE....... 256 38.67%
Walker
Total Votes... 441 66.62%
Optiscan...... 255 62.81%
DRE TSX....... 186 72.66%

Z-Score....... 2.68
Probability... 0.74% (of 9.85% vote share discrepancy between Optiscan and DRE)

Model 2: Winnebago County Cumulative VoteShares
Note the statistically improbable increase in Walker’s share. https://richardcharnin.wordpress.com/2012/12/09/walker-recall-county-cumulative-vote-trend-by-ward-group/

Model 3: Winnebago True Vote (2-party)
Barrett won the True Vote with 53.5%, a 5000 vote margin.
Walker won the recorded vote with 56.4%, a 9000 vote margin.
Walker needed an implausible 29% of returning Obama 2008 voters to match his recorded vote. https://richardcharnin.wordpress.com/2012/07/24/the-walker-recall-municipal-database-a-true-vote-model/

2008... Share. Votes. Alive Turnout.Votes..... Mix. Barrett Walker Barrett Walker Margin

Obama...55.90% 48,137 45,971 80.00% 36,777.... 51.97% 89.93% 10.07% 33,075 3,702 29,372
McCain..44.10% 37,976 36,267 80.00% 29,014.... 41.00% 06.96% 93.04% 2,020 26,993 -24,973
New..................................4,976..... 7.03% 55.90% 44.10% 2,781 2,194 587
Total......... 86,113 82,238 80.00% 70,766

True Vote........................................... 53.52% 46.48% 37,876 32,890 4,986
Recorded Vote....................................... 43.60% 56.40% 30,885 39,881 -8,996

2012 votes / living 2008 voters:86.05%
2012 voters % of 2008: 82.18%
Est. votes flipped:6,991 18.46%

Sensitivity Analysis
Barrett won all 18 plausible voter turnout and vote share scenarios

2008 Voter turnout in 2012:77.00% 80.00% 83.00%
Required Walker % of Obama:30.08% 29.16% 28.31%

Voter Turnout.......................... Barrett share of
Obama McCain........................... Obama McCain
80% 80%................................. 90% 7%

......Barrett Share of Obama.................. Obama Turnout
Barrett 87.0% 90.0% 93.0%.......McCain...77.0% 80.0% 83.0%
%McCain....Barrett Share........Turnout......Barrett Share
9.96% 53.19% 54.75% 56.31%......... 77% 53.61% 54.28% 54.94%
6.96% 51.96% 53.52% 55.08%......... 80% 52.86% 53.52% 54.19%
3.96% 50.73% 52.29% 53.85%......... 83% 52.11% 52.77% 53.43%
...........Barrett Margin.....................Barrett Margin
9.96% 4,520 6,727 8,934............. 77% 5,112 6,051 6,990
6.96% 2,780 4,986 7,193............. 80% 4,047 4,986 5,925
3.96% 1,039 3,245 5,452............. 83% 2,983 3,921 4,860

Take the Election Fraud Quiz.

 

The Walker Recall Municipal Database: A True Vote Model

Walker Recall Municipality Database: A True Vote Model

Richard Charnin
7/24/2012
Updated: Oct.27,2013

The Recall True Vote Model is designed to be a data reference and forensic tool to uncover locations where fraud was likely. It contains voting data on a county, municipality and ward-by-ward basis.

The analysis shows that the election was very likely stolen. In order to achieve his 171,000 vote margin (53.1-46.3%) Walker’s required shares of returning Obama voters in many municipalities were implausible. The True Vote Model indicates that Barrett had a 53-54% True Vote share (2-party) and won the election by nearly 200,000 votes.

The model produces the following for 72 counties, nearly 1900 municipalities and over 3000 Wards/Units:
1) Recorded votes and True Vote estimates
2) Walker’s share of returning Obama voters required to match the recorded vote
3) Red-shift differential between the True Vote and recorded vote
4) Voter turnout as a percent of living 2008 voters
5) Recorded and True Vote Margin

The ‘Input’ sheet contains the True Vote model for analyzing the state, a county or municipality.

Default Assumptions
Barrett’s share of returning Obama voters is calculated automatically as an incremental partisanship adjustment to his assumed 90% total Wisconsin share.

For example, in Dane County, Barrett’s share of returning Obama voters is adjusted from 90% to 95%. In Waukesha, it is adjusted to 84%.

The default assumption that Barrett won 5% of returning McCain voters is conservative. According to the WI 2010 Exit Poll, Barrett had 7%.

Barrett’s share of voters who did not vote in 2008 is set to Obama’s share.

User can now set their own Barrett shares of returning Obama and McCain voters as defaults on the Input sheet (they were originally hard coded as 90% and 5%). In the 2010 Wisconsin Governor exit poll, Barrett had just 83% of Obama voters. I believe his actual share was better than that. He also had 7% of McCain voters. If Barrett’s share of McCain voters in the recall was 7%, Walker’s required share of returning Obama voters increases from 22% to 24%.

Each of the defaults can be overridden.

Sensitivity Analysis
The tables save the time and effort of asking “what-if” vote share and turnout assumptions change to calculate total vote shares and margins.

Consider these scenarios based on the following assumptions:
1-Equal 79% turnout of Obama and McCain voters
2-New voters are 11% of total 2012 electorate
3-Barrett wins 57% of New voters

Worst Case
Barrett has 87% of returning Obama voters and 4% of McCain voters
He has 52% and wins by 100,000 votes

Most Likely Base Case
Barrett has 90% of returning Obama voters and 7% of McCain voters
He has 54.7% and wins by 232,000 votes

Best Case
Barrett has 93% of returning Obama voters and 10% of McCain voters
He has 57.3% and wins by 366,000 votes

The “Muni” database worksheet is protected from user data entry.
The built-in assumptions:
– Barrett’s default share of Obama voters is 90%, as per the “input” sheet.
– His share of McCain voters is fixed at 7%.
– There is no breakout of new voters.

These are the steps in using the model to analyze a given municipality:
1. Scroll “Muni” to locate the county
2. Check the row number of the Municipality
3. Enter the row number in the ‘Input’ sheet

These articles are from Wisconsin blogger Dennis Kern:
http://freewisconsinblog.com/?p=20860
http://myplayfulself.com/wordpress/archives/12818

Earlier posts on the Walker Recall:
July 11: True Vote Model: Implausible Walker Vote Shares Required to match the vote.
June 9: Exit Pollsters: MO Never Changes
June 6: Final Exit Poll: Forced to Match the Recorded vote
May 24: Is the Past Prologue?
May 3: True Vote Model Analysis

Take the Election Fraud Quiz.

Winnebago County Cumulative Vote Shares

 

Tags: , , ,

The Walker Recall True Vote Model: Implausible Vote Shares Required to Match the Vote

The Walker Recall True Vote Model: Implausible Vote Shares Required to Match the Vote

Richard Charnin

July 11, 2012

This analysis uses the Wisconsin Recall True Vote Model (TVM) to calculate Walker’s share of Obama returning voters that were required to match the state/county recorded vote. It is further evidence that Walker’s recorded margin was implausible and that Barrett very likely won the election.

Walker won in 2010 by 124,638 votes with a 52.3% share. His margin improved in 2012: he won by 171,105 votes and had a 53.1% share.

This worksheet provides a comparative analysis of the 2010 and 2012 elections.

In the recall, Walker’s biggest vote margins (in thousands) were in these counties: Waukesha (96), Washington (36), Brown (21), Ozaukee (20) and Outagamie (18). His biggest margin increases were in Taylor, Trempealeau, Price, Outagamie and Clark. The biggest vote gains were in Waukesha, Milwaukee, Outagamie, Brown and Dane.

In 2008, Obama won Wisconsin with a 56.2% recorded share. But he had 63.3% in the unadjusted exit poll, far beyond the 2.5% margin of error. Although the exit poll is strong evidence that election fraud sharply reduced Obama’s True Vote, we will be conservative and use the recorded vote in this analysis.

It is important to note the sensitivity analysis tables in the TVM. They display vote shares and margins over a range of turnout and vote share assumptions around the base case.

The following DEFAULT assumptions can be overidden on the INPUT sheet:
1) Equal turnout rates for returning Obama and McCain voters.
2) Barrett’s share of returning Obama voters is estimated as an increment applied to his base case 90% Wisconsin share.
3) Shares of New voters are set to the estimated 2008 vote shares.
4) Barrett won 5% of returning McCain voters

The model calculates Walker’s share of Obama returning voters that were required to match the state/county recorded vote.
The “2010-2012” worksheet provides a comparative analysis of the 2010 and 2012 elections

KEY STATE AND COUNTY RESULTS
The very conservative assumption is that Obama’s recorded 56.2% share was his True share. But he did better than that. He had 63.3% in the unadjusted exit poll, therefore Barrett probably did better than his True Vote shown below.

Wisconsin
Obama had 56.2%.
Barrett had 47.1% and lost by 171,000 votes.
Walker needed 23% of returning Obama voters.
Barrett True Vote: 53.6%, 193k margin.

Waukesha
Obama had 36.4%.
Walker had 72.4% and won by 96,000 votes.
Walker True Vote: 65%, a 65k vote margin.
Walker needed 36% of returning Obama voters.

Milwaukee
Obama had 67.3%.
Barrett had 63.2% and won by 107,000 votes.
Barrett True Vote: 65%, 122k margin.
Walker needed 10% of returning Obama voters.

Dane
Obama had 72.8%.
Barrett had 69.1% and won by 99k votes.
Barrett True Vote: 71%, 110k margin.
Walker needed 8% of returning Obama voters.

Outagamie
Obama had 54.9%.
Barrett had 36.1% and lost by 48k votes.
Barrett True Vote: 52% and won by 4,000 votes.
Walker needed 38% of returning Obama voters.

Racine
Obama had 53.1%.
Barrett had 46.7% and lost by 5k votes.
Barrett True Vote: 50.3% and won by 1,000 votes
Walker needed 18% of returning Obama voters.

Rock
Obama had 63.8%.
Barrett had 55.7% and lost by 8k votes.
Barrett True Vote: 61% and won by 15,000 votes.
Walker needed 17% of returning Obama voters.

The Walker Recall County/Ward Database
This spreadsheet database was created to facilitate analysis. A data filter function let’s one quickly view Ward totals for a given county. In addition, vote shares are calculated and automatically sorted.

The data is available as an Excel spreadsheet from Wisconsin GAB. The file consists of 3500 Ward vote records. By itself, it is not very useful since the viewer must scroll through all the records to get the desired county – a time-consuming process.

Take the Election Fraud Quiz.

 

Walker Recall: The Exit Pollster’s MO Never Changes

Richard Charnin
June 9, 2012

The exit pollster’s MO never changes. In the recall, the pundits said it was “too close to call”. I’m quite sure that Barrett was winning, but the media knew the fix was in so they had to keep it close. They knew the actual exit poll numbers would not see the light of day. But they sure called it quickly for Walker, didn’t they?

https://richardcharnin.wordpress.com/2012/06/06/wisconsin-recall-the-adjusted-final-exit-poll-was-forced-to-match-an-unlikely-recorded-vote/

The pollster’s have had plenty of experience in adjusting exit polls to match the vote count.

In 2004, preliminary state exit poll numbers were downloaded from the CNN website by Jonathan Simon. Kerry led by 50-48%. The state polls were already in the process of being matched to the recorded vote. But Bush was winning the vote count – a massive divergence from the exit polls.

We later learned that Kerry led the National Exit Poll from 4pm to midnight. At 4pm (8349 respondents) he led by 51-48%. At 730 pm (11027 respondents) by 51-48%. At 1222am (13047) by 51-47%. But we didn’t see these numbers. They were not meant for public viewing.

The next day, the CNN and NYT websites showed that Bush won the National Exit Poll (13660) by 51-48% – matching the recorded vote. How did the final 613 National Exit Poll respondents enable Bush to flip the vote? The exit pollsters never could answer that one. After all, the flip was mathematically impossible.

The unadjusted 2004 exit polls (state and national) were not released until about a year ago, long after the damage was done. And guess what? Kerry actually won the 13660 respondents! He had 7064 (51.7%), Bush 6414 (47.0%), Other 182 (1.3%).

Someday, probably in 2022, we’ll get to see the unadjusted recall exit poll numbers. In the meantime, here’s the 2004 National Exit Poll Timeline that was “not meant for public viewing”.

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

And let’s not forget the 2000 selection. The media told us the election was close in Florida and nationwide. But they did not tell us that Gore won the
1) National Exit Poll (13,108 respondents)by 48.5%-46.3%, a 2.7 million margin.
2) 50 state exit polls (58,000 respondents)by 50.8%-45.5%, a 6 million margin.
3) Florida exit poll (1,816 respondents)by 53.4%-43.6%. a 500,000 vote margin.

The media myth is that
1) Gore won the national popular vote by 540,000 votes.
2) Bush won Florida by 537 votes.

All we know is that the Florida recount was halted by the Supreme Court.

Gore won exit polls in the following states – but lost all in the official vote.
He needed just ONE to win the election.
2000: AL AR AZ CO FL GA MO NC NV TN TX VA 

https://richardcharnin.wordpress.com/2011/11/21/unadjusted-state-exit-polls-indicate-that-al-gore-won-a-mini-landslide-in-2000/

– Republican recorded presidential vote shares exceeded the corresponding unadjusted exit poll shares in 226 (82.4%) of the 274 state elections for which there is exit poll data. One would normally expect approximately 137 (50%). The probability is virtually ZERO.

– The exit poll margin of error (described below) was exceeded in 126 (46%) of the 274 polls. The statistical expectation is that the margin of error (MoE) would be exceeded in 14 (5%). The probability is ZERO.

– 123 of the 126 exit polls in which the MoE was exceeded moved to the recorded vote in favor of the Republican (the “red shift”). Just 3 moved in favor of the Democrat (” the blue shift”). There is a ZERO probability that this one-sided shift was due to chance. It is powerful evidence beyond any doubt of pervasive systemic election fraud.

– The Republicans won the recorded vote in 55 states in which the Democrats won the exit poll. Conversely, the Republicans lost the recorded vote in just two states (Iowa and Minnesota in 2000) in which they won the exit poll. If the elections were fair, the number of vote flips would be nearly equal. The probability of this disparity is virtually ZERO.

Calculating the probabilties

The probability P that 55 of 57 exit polls would flip from the Democrats in the exit polls to the Republicans in the recorded vote is given by the Binomial distribution: P= 1-Binomdist(54,57,.5,true)
P= 1.13E-14 = 0.000000000000011 or 1 in 88 trillion!

The probability that the exit poll margin of error would be exceeded in any given state is 5% or 1 in 20. Therefore, approximately 14 of the 274 exit polls would be expected to exceed the margin of error, 7 for the Republican and 7 for the Democrat.

Given the relationship between the exit poll, margin of error and corresponding win probability, we compare the 274 state exit polls to the corresponding recorded votes. The Republicans did better in the recorded vote than in the exit polls in 226 (82.4%) of the 274 elections. The probability of this one-sided red-shift is 3.7E-31 or 1 in 2.7 million trillion trillion.

The MoE was exceeded in 123 exit polls in favor of the Republican – and just 3 for the Democrat. The simple Poisson spreadsheet function calculates the probability P:
P = 5E-106 = Poisson (123, .025*274, false)
P = 1 in 1.8 billion trillion trillion trillion trillion trillion trillion trillion trillion.
The probability is ZERO. There are 106 places to the right of the decimal!
P = .0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 000005

Listen to Stephen Spoonamore – a Republican computer security expert:
http://www.youtube.com/watch?v=ZAyEfovA404&feature=bf_prev&list=PL7EA9A7F25C837D23

 

Wisconsin Recall: The adjusted Final Exit Poll was forced to match an unlikely recorded vote

Richard Charnin
June 6, 2012
Updated: July 11

The media and the exit pollsters have done it again.

Before the first votes were posted, the media reported that based on the exit polls, the election was “too close to call”. But Walker won by 53.2-46.3%, a 173,000 vote margin. Assuming “too close to call” meant that the exit poll indicated a 50/50 split, then there was a significant 7% discrepancy between the unadjusted exit poll and the recorded vote. I believe that Barrett was actually leading the exit polls. Of course, we will never know until the unadjusted exit polls are released. In any case, what caused the unknown red shift?

According to the Wisconsin True Vote Model , Barrett was a likely 54-46% winner. Barrett should have won easily – assuming the caveat of a fair election. But the election was very likely stolen.

Forcing the exit poll to match the recorded vote

The Final Wisconsin adjusted exit poll (2547 respondents) indicated that Walker had 53.0% (see the NY Times link below). The 0.2% difference between the Final and the recorded vote was the result of the standard policy of forcing the unadjusted poll to match the vote.

The pollsters claim that the exit poll had a 4.0% margin of error. But they can’t mean the final adjusted poll because it is always forced to match the recorded vote within 0.5%.

Why did the media not provide the actual unadjusted exit poll demographics? Was it because they knew that they would have to adjust all the crosstabs to match a rigged recorded vote – and did not want the public to view the “adjustments”?

The Fraud Factor

And as is always the case, there was no mention of the fraud factor in the mainstream media. There never is. To the exit pollsters and the media, there is no such thing as election fraud.

The GOP employs overt voter disenfranchisement in plain sight by robocalling voters with false information and having election workers discourage voters from using paper ballots and vote on unverifiable touchscreen DREs. But we are supposed to believe that right-wing voting machine manufacturers would not stoop so low as to write malicious code to covertly flip votes in cyberspace.

In 2010, Walker “won” by 52.2-46.6%, supposedly due to low-Democratic turnout.
Was the election a prologue of the recall?

In the recall, Democrats turned out in droves, they wanted Walker gone. There was no way that the unpopular Governor would match, much less exceed, his 2010 vote – if the votes were counted as cast. But that is a quaint notion considering the overwhelming statistical evidence of systemic election fraud since 1988.

Implausible 2008 returning voters and 2012 vote shares

Obama had a 56.2% recorded share in Wisconsin and 63.3% in the unadjusted exit poll (2.4% margin of error). Assuming Obama had a 60% True Vote share, then to match the recall vote, Walker needed the following:
1) 81% of McCain and 71% of Obama voters turned out.
2) He needed to win 25% of Obama and 95% of McCain voters.
3) He needed 46% of new voters who did not vote in 2010. The 2012 exit poll indicates he had 45% and that new voters comprised 13% of the total vote.

In order to win by his recorded vote, Walker needed a 10% advantage in returning 2008 voters and a 20% advantage in net defections. That is highly implausible.

Exit poll oddities

1) A full 5% of voters were not white or black. But their vote is n/a.
2) Philosophy: 13% of liberals voted for Walker?
3) Party ID: 34% Democrat/ 35% Republican in a progressive state?
4) Labor: Just 62% voted for Barrett?
5) Obama preferred by 51-44%, yet Barrett lost the recall by 53.2-46.3%?
6) Barrett only got 81% of would-be Obama voters?
7)Turnout:47% of recall were returning Walker 2010 and 34% Barrett? That’s a 13% difference. In 2010 Walker “won” by 52.2-46.6%.
8) Urban vote: Barrett had just 62% in big cities?

Margin of error?

The pollsters indicate that there were 2547 exit poll respondents and that the margin of error (MoE) was +/-4%. Presumably, this includes a 30% cluster factor.

The adjusted poll had a zero MoE since it was forced to match the recorded vote. What is the point of mentioning a MoE if the exit poll is adjusted to match the recorded vote?

The pollsters must be referring to the unadjusted exit poll, but of course that is not for public viewing. In any case, the 4.0% MoE is too high, considering the number of respondents (n).

The simple formula is: MoE =.98/sqrt(n). Adding a 30% cluster factor, the theoretical MoE is 2.6%= 1.3*.98/sqrt(2547). So how did the pollsters come up with the 4.0% MoE?

If we had unadjusted exit poll data, the margin of error would be applied to determine the interval where the vote share would be expected to fall 95% of the time. That’s why unadjusted exit polls are necessary. The standard practice of forcing the exit poll to match the recorded vote implicitly assumes zero fraud, i.e. the recorded vote is identical to the True Vote. It never is.

The Ultimate Smoking Gun: Unadjusted state presidential exit polls (1988-2008)

July 11 Update: There are 274 state exit polls listed in the Roper archive for 1988-2008 (only 24 are listed for 1988). I originally used the True Vote Model to estimate the 26 missing 1988 exit polls. Dr. Bob Fitrakis, writing in The Free Press, referred to the earlier probability calculations in an excellent article: Wisconsin: None Dare Call it Vote Rigging

Here are the revised numbers, based on 274 exit polls:

– Republican recorded presidential vote shares exceeded the corresponding unadjusted exit poll shares in 232 (85%) of the 274 state elections for which there is exit poll data. One would normally expect approximately 137 (50%). The probability is virtually ZERO.

– The exit poll margin of error (described below) was exceeded in 135 (49%) of the 274 polls. The statistical expectation is that the margin of error (MoE) would be exceeded in 14 (5%). The probability is ZERO.

– 131 of the 135 exit polls in which the MoE was exceeded moved to the recorded vote in favor of the Republican (the “red shift”).  There is a ZERO probability that this one-sided shift was due to chance. It is powerful evidence beyond any doubt of pervasive systemic election fraud.

– The Republicans won the recorded vote in 55 states in which the Democrats won the exit poll. Conversely, the Republicans lost the recorded vote in just two states (Iowa and Minnesota in 2000) in which they won the exit poll. If the elections were fair, the number of vote flips would be nearly equal. The probability of this disparity is virtually ZERO.

Calculating the probabilties

The probability P that 55 of 57 exit polls would flip from the Democrats in the exit polls to the Republicans in the recorded vote is given by the Binomial distribution:

P= 1-Binomdist(54,57,.5,true)
P= 1.13E-14 = 0.000000000000011 or 1 in 88 trillion!

The probability that the exit poll margin of error would be exceeded is 5% or 1 in 20. Therefore, approximately 14 of the 274 exit polls would be expected to exceed the margin of error, 7 for the Republican and 7 for the Democrat.

Given the relationship between the exit poll, margin of error and corresponding win probability, we compare the 274 state exit polls to the corresponding recorded votes. The Republicans did better in the recorded vote than in the exit polls in 232 of the 274 elections. The probability of this one-sided red-shift is 9E-35 or 1 in 100 billion trillion trillion.

The MoE was exceeded in 131 exit polls in favor of the Republican – and just 4 for the Democrat. The simple Poisson spreadsheet function calculates the probability P:
P = 3.74E-116 = Poisson (131, .025*274, false)
P = .0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 00000374

1988-2008 Red-shift Summary (274 exit polls)
The following table lists the
a) Number of states in which the exit poll red-shifted to the Republican,
b) Number of states which red-shifted beyond the margin of error,
c) Probability of n states red-shifting beyond the MoE,
d) Democratic unadjusted aggregate state exit poll share,
e) Democratic recorded share,
f) Difference between Democratic exit poll and recorded share.

Year RS >MoE Probability.... Exit Vote Diff
1988 21.. 12... 2.5E-12..... 50.3 45.7 4.6 Dukakis may have won
1992 45.. 27... 1.1E-26..... 47.6 43.0 4.6 Clinton landslide
1996 44.. 19... 2.5E-15..... 52.6 49.3 3.3 Clinton landslide
2000 34.. 17... 4.9E-13..... 50.8 48.4 2.4 Gore win stolen
2004 42.. 23... 3.5E-20..... 51.1 48.3 2.8 Kerry landslide stolen
2008 46.. 37... 2.4E-39..... 58.0 52.9 5.1 Obama landslide denied

Total 232. 135. 3.7E-116…. 51.7 47.9 3.8 Exact match to the Nat Exit Poll

Note: 274 exit polls from 1988-2008
(24 in 1988, 50 in each of the 1992-2008 elections)

The conventional wisdom is very conventional – and very misleading:
http://www.huffingtonpost.com/2012/06/05/wisconsin-recall-vote_n_1572662.html

The NY Times Election site has the FINAL, adjusted exit poll crosstabs.
http://www.nytimes.com/interactive/2012/06/05/us/politics/wisconsin-recall-exit-polls.html