Election Fraud Lockdown: No Discussion by Politicians, Forecasters and Media Pundits

Election Fraud Lockdown: No Discussion by Politicians, Forecasters and Media Pundits

Election Forecasters who predicted a Bush win in 2000 and 2004 were only “correct” because of a rigged recorded vote. Gore won the recorded vote by 540,000; he won the True Vote by 3 million. Kerry lost the recorded vote by 3 million; he won the True Vote by 10 million. The pattern continued in 2008. Obama won the recorded vote by 9.5 million; he won the True Vote by nearly 23 million.

Election forecasters measure their performance against a bogus recorded vote - not the True Vote. Election fraud is never mentioned as a factor. Inherent flaws exist in all election models. The implicit forecast assumption is that the official recorded vote will accurately reflect the True Vote; the election will be fraud-free. But fraud has been a major factor since 1988. Forecasters neglect the election fraud factor.

Recorded Vote = True Vote + Election Fraud

Prominent election forecasters discuss their methodologies in a special issue of the International Journal of Forecasting. The articles range from descriptions of diverse election forecasting models, such as those that use political futures markets and historical analysis, to those which evaluate the success of election forecasting in past elections.But none mention the subject of historical election fraud. Are they that clueless? Or are they afraid of jeopardizing their positions by daring to suggest that our “democracy” is a myth?

Consider this statement from the American Association of Public Opinion Research (AAPOR): “What is important to note is that at the close of Election Day, exit poll results are weighted to reflect the actual election outcomes. It is in this way that the final exit poll data can be used for its primary and most important purpose – to shed light on why the election turned out the way it did. That is, exit polls are just as important for the information they gather about the voters’ demographics and attitudinal predispositions towards the candidates and the campaign issues as they are for making the projections reported by news organizations on Election Night”.

So the purpose of the final exit poll is to get accurate demographic data by matching to the actual vote count. Is this the way to conduct statistical research? What if the vote count is corrupted?

Uncounted votes have steadily declined as percent of total votes cast - from 10.4% in 1988 to 2.7% in 2004. When added to the recorded vote in order to derive the total votes cast for the five elections from 1988-2004, the average Democratic unadjusted exit poll share is within 0.1% of the adjusted vote. The 2004 exit poll discrepancies were different in kind and scope from those of the prior four elections. Unlike 1988-2000, the 2004 discrepancies cannot be explained by uncounted votes alone.

The 2004 Election Calculator Model is based on the physical constraints of the 2000 recorded vote, after adjusting for mortality, uncounted votes and 2000 voter turnout in 2004. Vote shares are based on the 2004 National Exit Poll “Voted 2000” cross tab. The model indicates that Kerry won by 53.2-45.4% (66.9-57.1m). It proves that for Bush to obtain his 3.0m margin in 2004, he would have required 21.5% of returning Gore voters!

This article will discuss the following topics:

. Election 2004 Forecast Models: The Track Record

. The American Association of Public Opinion Research (AAPOR)

. Uncounted Votes and Exit Poll Discrepancies (1988-2004)

. Projection and Post-election Models: Monte Carlo Simulation vs. Regression Analysis

. Implausible: Returning Gore voters required for Bush’s 3.0m margin in 2004

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Election 2004 Forecast Models: The Track Record

The following election forecast models were executed 2-9 months before the 2004 election. All except one forecast that Bush would win the 2-party popular vote with an average 53.9% share which was 3% above the actual recorded vote. But according to the aggregate unadjusted state exit polls, Bush had 47.5%. Furthermore, except for Beck-Tien, the estimated popular vote win probabilities were incompatible with the forecast vote shares (they were too low). And none of the models attempted to forecast the electoral vote or even mentioned the possibility of election fraud.

Author Date Pick 2-pty Win Prob

Recorded 2-Nov Bush 51.2 Final

Beck-Tien 27-Aug Kerry 50.1 50

Abramowitz 31-Jul Bush 53.7 -

Campbell 06-Sep Bush 53.8 97

Wlezien 27-Jul Bush 52.9 75

Holbrook 30-Aug Bush 54.5 92

Lockabie 21-May Bush 57.6 92

Norpoth 29-Jan Bush 54.7 95

Compare the above projections to these pre-election poll and exit poll-based models.

TruthIsAll Election Model (11/01/04)

Assumption: Kerry wins 75% of undecided voters

State Kerry 51.8 99.9 Monte Carlo EV Simulation Win Probability: 4995 wins/5000 trials

National Kerry 51.6 94.5 Final 5 national polls average projection (2% MoE)

Exit Polls (2% MoE)

State Aggregate Kerry 52.5 99.1 Unadjusted WPE method

NEP 1 Kerry 51.9 96.9 Voted 2k, 12:22am (13047 respondents), 39/41 Gore/Bush weights

NEP 2 Kerry 52.9 99.8 Voted 2k, 12:22am, 37.6/37.4 adjusted weights

Election Calculator

True Vote Model

(TruthIsAll) Kerry 53.7 99.99 12:22am NEP, 125.7m votes cast; 1.22% annual 2000 voter mortality, 95% 2k voter turnout

The following article from two professors of political science describes the methodologies used by a number of 2008 election forecasters. None of the assembled articles discusses the historical evidence of election fraud or its likely impact on their forecasts for 2008.

Election Forecasters Preparing for Historic Election

Science Daily (June 23, 2008) — Anticipating what is likely to be one of the most interesting elections in modern history, University at Buffalo professor of political science James E. Campbell and Michael S. Lewis-Beck, professor of political science at the University of Iowa, have assembled the insights of prominent election forecasters in a special issue of the International Journal of Forecasting published this month.

Each of the articles demonstrates the challenges of election forecasting, according to Campbell, chair of UB's Department of Political Science, who since 1992 has produced a trial-heat-and-economy forecast of the U.S. presidential election. His forecast uses the second-quarter growth rate in the gross domestic product and results of the trial-heat (preference) poll released by Gallup near Labor Day to predict what percentage of the popular vote will be received by the major party candidates.

The articles range from descriptions of diverse election forecasting models, such as those that use political futures markets and historical analysis, to articles that evaluate the success of election forecasting in past elections. Two of the articles address a topic particularly pertinent to the 2008 presidential election: whether open seat and incumbent elections should be treated differently by election forecasters.

"One of the biggest misunderstandings about election forecasting is the idea that accurate forecasts must assume that the campaign does not matter," Campbell explains. "This is not true. First, one of the reasons that forecasts can be accurate is that they are based on measures of the conditions that influence campaigns. So campaign effects are, to a significant degree, predictable. Second, forecasters know that their forecasts are not perfect. Forecasts are based on imperfect measures and may not capture all of the factors affecting a campaign. Some portion of campaign effects is always unpredictable."

Though some campaign effects are unpredictable "the extent of these effects is usually limited," Campbell points out. In the historic contest between presumptive presidential nomineesBarack Obama and John McCain one thing is certain: "Forecasting this election will be more difficult than usual," Campbell says: "First, there isn't an incumbent. Approval ratings and the economy are likely to provide weaker clues to an election's outcome when the incumbent is not running. Second, Democrats had a very divided nomination contest and it is unclear how lasting the divisions will be. Third, many Republicans are not very enthusiastic about McCain and it is unclear how strong Republican turnout will be for him."

Of the six different forecast models described in the journal articles, only two have a forecast at this point. The other four will have forecasts between late July and Labor Day. The journal articles can be downloaded at sciencedirect.com. Below are brief descriptions:

In "U.S. Presidential Election Forecasting: An Introduction" journal co-editors Campbell and Lewis-Beck provide a brief history of the development of the election forecasting field and an overview of the articles in this special issue.

In "Forecasting the Presidential Primary Vote: Viability, Ideology and Momentum," Wayne P. Steger of DePaul University takes on the difficult task of improving on forecasting models of presidential nominations. He focuses on the forecast of the primary vote in contests where the incumbent president is not a candidate, comparing models using information from before the Iowa Caucus and New Hampshire primary to those taking these momentum-inducing events into account.

In "It's About Time: Forecasting the 2008 Presidential Election with the Time-for-Change Model," Alan I. Abramowitz of Emory University updates his referenda theory-based "time for a change" election forecasting model first published in 1988. Specifically, his model forecasts the two-party division of the national popular vote for the in-party candidate based on presidential approval in June, economic growth in the first half of the election year, and whether the president's party is seeking more than a second consecutive term in office.

In "The Economy and the Presidential Vote: What the Leading Indicators Reveal Well in Advance," Robert S. Erikson of Columbia University and Christopher Wlezien of Temple University ask what is the preferred economic measure in election forecasting and what is the optimal time before the election to issue a forecast.

In "Forecasting Presidential Elections: When to Change the Model?" Michael S. Lewis-Beck of the University of Iowa and Charles Tien of Hunter College, CUNY ask whether the addition of variables can genuinely reduce forecasting error, as opposed to merely boosting statistical fit by chance. They explore the evolution of their core model – presidential vote as a function GNP growth and presidential popularity. They compare it to a more complex, "jobs" model they have developed over the years.

In "Forecasting Non-Incumbent Presidential Elections: Lessons Learned from the 2000 Election," Andrew H. Sidman, Maxwell Mak, and Matthew J. Lebo of Stony Brook University use a Bayesian Model Averaging approach to the question of whether economic influences have a muted impact on elections without an incumbent as a candidate. The Sidman team concludes that a discount of economic influences actually weakens general forecasting performance.

In "Evaluating U.S. Presidential Election Forecasts and Forecasting Equations," UB's Campbell responds to critics of election forecasting by identifying the theoretical foundations of forecasting models and offering a reasonable set of benchmarks for assessing forecast accuracy. Campbell's analyses of his trial-heat and economy forecasting model and of Abramowitz's "time for a change" model indicates that it is still at least an open question as to whether models should be revised to reflect more muted referendum effects in open seat or non-incumbent elections.

In "Campaign Trial Heats as Election Forecasts: Measurement Error and Bias in 2004 Presidential Campaign Polls," Mark Pickup of Oxford University and Richard Johnston of theUniversity of Pennsylvania provide an assessment of polls as forecasts. Comparing various sophisticated methods for assessing overall systematic bias in polling on the 2004 U.S.presidential election, Johnston and Pickup show that three polling houses had large and significant biases in their preference polls.

In "Prediction Market Accuracy in the Long Run," Joyce E. Berg, Forrest D. Nelson, and Thomas A. Reitz from the University of Iowa's Tippie College of Business, compare the presidential election forecasts produced from the Iowa Electronic Market (IEM) to forecasts from an exhaustive body of opinion polls. Their finding is that the IEM is usually more accurate than the polls.

In "The Keys to the White House: An Index Forecast for 2008," Allan J. Lichtman of American University provides an historian's checklist of 13 conditions that together forecast the presidential contest. These "keys" are a set of "yes or no" questions about how the president's party has been doing and the circumstances surrounding the election. If fewer than six keys are turned against the in-party, it is predicted to win the election. If six or more keys are turned, the in-party is predicted to lose. Lichtman notes that this rule correctly predicted the winner in every race since 1984.

In "The State of Presidential Election Forecasting: The 2004 Experience," Randall J. Jones, Jr. reviews the accuracy of all of the major approaches used in forecasting the 2004 presidential election. In addition to examining campaign polls, trading markets, and regression models, he examines the records of Delphi expert surveys, bellwether states, and probability models.

_____________________________________________________________________________________

The American Association of Public Opinion Research (AAPOR)

This paragraph from the article says it all:

“What is important to note is that at the close of Election Day, exit poll results are weighted to reflect the actual election outcomes. It is in this way that the final exit poll data can be used for its primary and most important purpose – to shed light on why the election turned out the way it did. That is, exit polls are just as important for the information they gather about the voters’ demographics and attitudinal predispositions towards the candidates and the campaign issues as they are for making the projections reported by news organizations on Election Night”.

The purpose of the Final exit poll is to get accurate demographic data by matching to the actual vote count? Is this the way to conduct statistical research? What if the vote count is fraudulent? What is their Null Hypothesis? AAPOR refers to challenges facing exit pollsters, but they ignore the challenge of calculating the impact of election fraud on the recorded vote.

If the vote counts were accurate, the demographics would be correct. Since the recorded vote counts are bogus, so are the demographics. Assuming that the vote count is pristine is to immediately invalidate the demographics on which it is based. It's a very simple concept if you really want to do the best analysis possible to get at the truth: It's Basic Statistics 101.We need to analyze the raw, pristine, unadjusted exit poll data. One would assume that this august group would want to see it. But in their world, corruption is non-existent. They believe that the Recorded Vote is identical to the True Vote.

AAPOR also claims that: “An exit poll sample is not representative of the entire electorate until the survey is completed at the end of the day. Different types of voters turn out at different times of the day”. But they don’t mention the fact that Kerry led the exit polls from 4pm (8349 sampled voters) to 730pm (11027) and 1222am (13047) by a steady 51-47%. Or that uncounted votes are 70-80% Democratic and contribute significantly to the exit poll discrepancies.

AAPOR parrots the Reluctant Bush Responder (rBr) myth used by exit pollsters Edison-Mitofsky: “In recent national and state elections, Republicans have declined to fill out an exit poll questionnaire at a higher rate than Democratic voters, producing a slight Democratic skew”. But the 2004 Final Exit Poll indicated that Bush 2000 voters comprised 43% of the 2004 electorate (which was mathematically impossible) as opposed to 37% of Gore voters. And according to the E-M report, the highest exit poll refusal rates were in Democratic states. So much for the rBr myth.

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1988-2004: Uncounted Votes and Exit Poll discrepancies

Uncounted Votes have steadily declined as a percent of total votes cast - from 10.4% in 1988 to 2.7% in 2004. When added to the recorded vote in order to derive the total votes cast for the five elections from 1988-2004, the average Democratic unadjusted exit poll share is within 0.1% of the adjusted vote.

Comparing the adjusted vote to the aggregate exit poll and recorded vote (2-party exit poll in parenthesis):

Year Democrat Recorded Exit Poll Adjusted

Average share 46.9% 48.8% (52.7%) 48.9%

1988 Dukakis 45.6 46.8 (47.3) 48.7

1992 Clinton 43.0 45.7 (56.8) 45.7

1996 Clinton 49.2 50.2 (55.8) 51.4

2000 Gore 48.4 49.4 (51.4) 49.7

2004 Kerry 48.3 51.8 (52.3) 49.0

Look at this graph. In every one of the last five elections the unadjusted Democratic exit poll share exceeded the recorded vote. But which of the five stands out from the rest? The 2004 exit poll discrepancies were different in kind and scope from those of the prior four elections. Unlike 1988-2000, the 2004 discrepancies cannot be explained by uncounted votes alone.

There are some exit poll critics who claim that the large (5.4 WPE) 1992 exit poll discrepancy proves that 2004 exit poll analysis (7.1 WPE) which indicate that the election was stolen are "crap" and "bad science". After all, they say, there were no allegations of fraud in 1992. They fail to mention (or are unaware of) the fact that in 1992 Clinton beat Bush I by a recorded 43.6-38.0m (43.0-37.4%) but 9.4m votes were uncounted - and 70-80% were Democratic. When the uncounted votes are added, the adjusted vote becomes 50.7-40.3m (45.7-36.4%), which exactly matched Clinton’s unadjusted exit poll.

From 1988-2000, after the uncounted adjustment, there was a 0.85% average Democratic exit poll discrepancy and 2.9 WPE. In 2004, after the 3.4m uncounted vote adjustment, there was a 2.8% discrepancy and Bush's margin was reduced from 3.0m (62.0-59.0) to 1.3m (62.9-61.6). But uncounted votes were only one component of Election Fraud 2004. The Election Calculator Model determined that approximately 5m votes were switched from Kerry to Bush.

Recorded and Uncounted Votes

Recorded Dem Rep Other Dem Rep Other WPE Unctd Dem Rep

Avg 103.4 48.6 47.7 7.1 46.9% 46.0% 7.1% 3.76 7.5 5.6 1.9

2004 122.3 59.0 62.0 1.2 48.3% 50.7% 1.0% 7.09 3.4 2.6 0.9

2000 105.4 51.0 50.5 4.0 48.4% 47.9% 3.8% 2.01 5.4 4.0 1.3

1996 96.3 47.4 39.2 9.7 49.2% 40.7% 10.1% 1.93 8.7 6.5 2.2

1992 101.4 43.6 38.0 19.8 43.0% 37.4% 19.6% 5.40 9.4 7.0 2.3

1988 91.6 41.8 48.9 0.9 45.6% 53.4% 1.0% 2.38 10.6 7.9 2.6

Total Votes Cast vs. Exit Poll

Adj. Votes Dem Rep Other Dem Rep Other EP Diff EP2pty Unctd

Avg 110.9 54.2 49.6 7.1 48.9% 44.6% 6.5% 48.8% 0.1% 52.7% 7.0%

2004 125.7 61.6 62.9 1.2 49.0% 50.0% 1.0% 51.8% -2.8% 52.3% 2.74%

2000 110.8 55.0 51.8 4.0 49.7% 46.8% 3.6% 49.4% 0.3% 51.4% 4.86%

1996 105.0 54.0 41.4 9.7 51.4% 39.4% 9.2% 50.2% 1.2% 55.8% 8.31%

1992 110.8 50.7 40.3 19.8 45.7% 36.4% 17.9% 45.7% 0.0% 56.8% 8.48%

1988 102.2 49.8 51.5 0.9 48.7% 50.4% 0.9% 46.8% 1.9% 47.3% 10.37%

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Projection and Post-election Models: Monte Carlo Simulation vs. Regression Analysis

There are two basic methods used to forecast presidential elections:

1) Projections based on state and national polling trends which forecast the popular and electoral vote, updated frequently right up to the election.

2) Regression models based on historical time-series which forecast the popular vote, executed months before the election.

Polling models when adjusted for undecided voters and estimated turnout, are superior to regression models. Models which predicted a Bush win in 2000 and 2004 were technically "correct"; Bush won the recorded vote. But Gore and Kerry won the True vote. Except for the Election Calculator (below), all models assume that elections will be fraud-free.

Academics and political scientists create multiple regression models which utilize time-series data as relevant input variables: economic growth, inflation, job growth, interest rates, foreign policy, historical election vote shares, etc. Regression modeling is an interesting theoretical exercise but does not account for the daily events which affect voter psychology. Fraud could conceivably skew regression models and media tracking polls.

Statistical analyses provided by Internet bloggers concluded that BushCo stole the 2004 election. Their findings were dismissed by the media as "just another conspiracy theory" by the media right in Nov. 2004. A few of these “conspiracy fraudsters" were banned after posting on various liberal discussion forums. And even today, the most popular polling sites never discuss election fraud. But if the Democrats haven't raised the issue after two stolen elections, why should they expect the media to do it for them? Is there anyone who still truly believes that the elections were legitimate?

There has been much misinformation regarding electoral and popular vote win probability calculations. In the 2008 Election Model, the latest state poll shares are used to project the vote after adjusting for undecided voters. The model assumes the election is held on the day of the projection. The projections determine the probability of winning each state is used in simulating 5000 election trials. The expected electoral vote is a simple average; the probability of capturing at least 270 electoral votes is a simple ratio of the number of winning trials divided by 5000.

The probability of winning the popular vote is based on the aggregate state 2-party projected vote share and margin of error. These are input to the Excel normal distribution function. The probability (P) of winning the popular vote is P= NORMDIST (vote share, 0.50, MoE/1.96, True). The probability of winning the popular vote should be close to the probability of winning the electoral vote. In fact, if they are within a percentage point of each other, the state and national polls is a good test of the data and the methodology.

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2004 Election Calculator True Vote Model

Based on 2000 recorded vote adjusted for voter mortality, uncounted votes and turnout in 2004

Vote shares based on National Exit Poll (13047 respondents)

Kerry won by 53.2 - 45.4% (66.9 - 57.1 million)

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

25.62

20.4%

57%

41%

Gore

51.00

4.04

55.04

2.72

52.32

95%

49.70

39.5%

91%

Bush

50.46

1.08

51.53

2.48

49.06

95%

46.60

37.1%

10%

90%

Nader/other

3.96

0.27

4.23

0.21

4.02

95%

3.82

3.0%

64%

17%

19%

Total

105.42

5.38

110.80

5.41

105.39

100.13

125.74

100%

53.23%

45.39%

1.38%

125.74

66.94

57.07

1.74

Sensitivity Analysis

Kerry National Vote

Gore share of

Kerry share of

Uncounted in 2000

Gore Voter Turnout

Gore voters

Share of New voters (DNV in 2000)

53.2%

91.0%

93.0%

95.0%

97.0%

99.0%

53.2%

53.0%

55.0%

57.0%

59.0%

61.0%

95.0%

53.3%

53.6%

53.9%

54.2%

54.5%

95.0%

54.0%

54.4%

54.8%

55.2%

55.6%

85.0%

53.0%

53.3%

53.6%

53.9%

54.1%

93.0%

53.2%

53.6%

54.0%

54.4%

54.8%

75.0%

52.7%

52.9%

53.2%

53.5%

53.8%

91.0%

52.4%

52.8%

53.2%

53.6%

54.0%

65.0%

52.3%

52.6%

52.9%

53.2%

53.5%

89.0%

51.6%

52.0%

52.4%

52.8%

53.3%

55.0%

52.0%

52.3%

52.6%

52.9%

53.1%

87.0%

50.8%

51.2%

51.7%

52.1%

52.5%

Kerry Margin (millions)

9.87

91.0%

93.0%

95.0%

97.0%

99.0%

9.87

53.0%

55.0%

57.0%

59.0%

61.0%

95.0%

10.1

10.8

11.5

12.3

13.0

95.0%

11.8

12.8

13.8

14.9

15.9

85.0%

9.2

10.0

10.7

11.4

12.2

93.0%

9.8

10.8

11.9

12.9

13.9

75.0%

8.4

9.1

9.9

10.6

11.3

91.0%

7.8

8.8

9.9

10.9

11.9

65.0%

7.6

8.3

9.0

9.8

10.5

89.0%

5.8

6.9

7.9

8.9

9.9

55.0%

6.8

7.5

8.2

8.9

9.6

87.0%

3.8

4.9

5.9

6.9

7.9

Sensitivity Analysis

Kerry National Vote

Bush 2000 Voter

Kerry share of

Turnout in '04

Gore Voter Turnout in '04

Gore voters

Share of Bush voters

53.2%

91.0%

93.0%

95.0%

97.0%

99.0%

53.2%

8.0%

9.0%

10.0%

11.0%

12.0%

91.0%

53.4%

53.7%

54.0%

54.3%

54.5%

95.0%

54.1%

54.4%

54.8%

55.2%

55.6%

93.0%

53.0%

53.3%

53.6%

53.9%

54.2%

93.0%

53.3%

53.7%

54.0%

54.4%

54.8%

95.0%

52.7%

52.9%

53.2%

53.5%

53.8%

91.0%

52.5%

52.9%

53.2%

53.6%

54.0%

97.0%

52.3%

52.6%

52.9%

53.2%

53.4%

89.0%

51.7%

52.1%

52.4%

52.8%

53.2%

99.0%

51.9%

52.2%

52.5%

52.8%

53.1%

87.0%

50.9%

51.3%

51.7%

52.0%

52.4%

Kerry Margin (millions)

9.87

91.0%

93.0%

95.0%

97.0%

99.0%

9.87

8.0%

9.0%

10.0%

11.0%

12.0%

91.0%

10.3

11.0

11.7

12.5

13.2

95.0%

12.0

12.9

13.8

14.8

15.7

93.0%

9.4

10.1

10.8

11.5

12.3

93.0%

10.0

10.9

11.9

12.8

13.7

95.0%

8.4

9.1

9.9

10.6

11.3

91.0%

8.0

8.9

9.9

10.8

11.7

97.0%

7.5

8.2

8.9

9.6

10.4

89.0%

6.0

6.9

7.9

8.8

9.7

99.0%

6.5

7.3

8.0

8.7

9.4

87.0%

4.0

5.0

5.9

6.8

7.8

_____________________________________________________________________________________

Implausible: Returning Gore voters required for Bush’s 3.0m margin in 2004

Most likely scenario: 2000/2004 U.S. Vote Census estimates and the 12:22am NEP “Voted 2000” shares (13047 respondents):

Bush needed 21.5% of returning Gore voters to match his recorded vote!

Least likely scenario: Vote Census estimates and the Final NEP “Voted 2000” shares (13660 respondents):

Bush needed 18.1% of returning Gore voters to match his recorded vote!

Assumptions

1.22% annual voter mortality

95% of 2000 voters turned out to vote in 2004

Final NEP vote shares:

Uncounted votes included for 2000 and 2004: Bush required 18.1% of returning Gore Voters

Uncounted votes not included: Bush required 16.3% of returning Gore Voters

12:22am NEP vote shares:

Uncounted votes included for 2000 and 2004: Bush required 21.5% of returning Gore Voters

Uncounted votes not included: Bush required 20.0% of returning Gore Voters

Final NEP Voted 2000 shares

3.45m (2.74%) Uncounted in 2004, none in 2000

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

30.48

24.2%

54.0%

45%

Gore

51.00

0.00

51.00

2.59

48.41

95%

45.99

36.6%

82.3%

16.7%

Bush

50.46

0.00

50.46

2.36

48.10

95%

45.69

36.3%

91%

Nader

Other

3.957

0.00

3.96

0.19

3.76

95%

3.58

2.8%

71%

21%

Total

105.42

0.00

105.42

5.14

100.27

95.26

125.74

1.00

48.48%

50.68%

0.84%

125.74

60.96

63.73

1.05

No uncounted votes in 2000 and 2004

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

27.03

22.1%

54%

45%

Gore

51.00

0.00

51.00

2.59

48.41

95%

45.99

37.6%

82.7%

16.3%

Bush

50.46

0.00

50.46

2.36

48.10

95%

45.69

37.4%

91%

Other

3.96

0.00

3.96

0.19

3.76

95%

3.58

2.9%

71%

21%

Total

105.42

0.00

105.42

5.14

100.27

95.26

122.29

100%

48.48%

50.69%

0.83%

122.29

59.28

61.99

1.02

5.4m (4.86%) Uncounted in 2000, 3.45m (2.74%) in 2004

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

25.61

20.4%

54%

45%

Gore

51.00

4.04

55.04

2.72

52.32

95%

49.70

39.5%

80.9%

18.1%

Bush

50.46

1.08

51.53

2.48

49.06

95%

46.60

37.1%

91%

Nader/Other

3.96

0.27

4.23

0.21

4.02

95%

3.82

3.0%

71%

21%

Total

105.42

5.38

110.80

5.41

105.39

100.13

125.74

100%

48.47%

50.69%

0.84%

125.74

60.95

63.73

1.06

5.4m (4.86%) Uncounted in 2000, none in 2004

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

22.17

18.1%

54%

45%

Gore

51.00

4.04

55.04

2.72

52.32

95%

49.70

40.6%

81.3%

17.7%

Bush

50.46

1.08

51.53

2.48

49.06

95%

46.60

38.1%

91%

Nader/Other

3.96

0.27

4.23

0.21

4.02

95%

3.82

3.1%

71%

21%

Total

105.42

5.38

110.80

5.41

105.39

100.13

122.29

100%

48.48%

50.68%

0.84%

122.29

59.29

61.98

1.02

12:22am NEP Voted 2000 shares

5.4m (4.86%) Uncounted in 2000, 3.45m (2.74%) in 2004

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

25.62

20.4%

57%

41%

Gore

51.00

4.04

55.04

2.72

52.32

95%

49.70

39.5%

77.5%

21.5%

Bush

50.46

1.08

51.53

2.48

49.06

95%

46.60

37.1%

10%

90%

Nader/other

3.96

0.27

4.23

0.21

4.02

95%

3.82

3.0%

64%

17%

19%

Total

105.42

5.38

110.80

5.41

105.39

100.13

125.74

100%

47.90%

50.72%

1.38%

125.74

60.23

63.78

1.74

No uncounted votes in 2000 and 2004

2000 Recorded

2004 Calculated

Voted

Recorded

Uncounted

Cast

Deaths

Alive

Turnout

Voted

Weight

Kerry

Bush

Other

DNV

27.03

22.1%

57%

41%

Gore

51.00

0.00

51.00

2.59

48.41

95%

45.99

37.6%

79.0%

20.0%

Bush

50.46

0.00

50.46

2.36

48.10

95%

45.69

37.4%

10%

90%

Nader/other

3.96

0.00

3.96

0.19

3.76

95%

3.58

2.9%

64%

17%

19%

Total

105.42

0.00

105.42

5.14

100.27

95.26

122.29

100%

47.92%

50.71%

1.37%

122.29

58.60

62.01

1.68