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Updated, Expanded and Corrected Affidavit Version: U.S. 2016 Unadjusted Exit Poll Discrepancies Fit Chronic Republican Vote – Count Rigging, not Random Statistical, Patterns

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This is an unused affidavit that includes data and analysis that corrects, updates, and expands on the data used in an earlier article: http://www.cpegonline.org/wp-content/uploads/2017/08/Unexplained-Patterns-in-2016-and-Earlier-U.S.-Elections.pdf (also on ResearchGate). This affidavit was part of a legal effort to force an official investigation of the 2016 U.S. General Election official results before they were certified by Congress. However, the effort was dropped when it became clear that the proposed law suit would not even make it to first base, as it would be struck down by the Courts due “to lack of standing” without support by a major party candidate. The campaign of Hilary Clinton showed no inclination to support such an effort.
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1
Ron Baiman
Aug. 8, 2017
Updated, Expanded and Corrected Affidavit Version: U.S. 2016 Unadjusted Exit Poll
Discrepancies Fit Chronic Republican Vote Count Rigging, not Random Statistical, Patterns
1
1. I, ____________________after being first sworn,
state
that
the following is
true based upon my own personal knowledge:
2. My name is Ron Paul Baiman. I reside at 205 S. Humphrey Ave, Oak Park, Illinois. I am
currently employed as Assistant Professor of Economics in the Graduate Business
Administration Department of Benedictine University, Lisle, IL. I hold a Ph.D. in
Economics, received in 1992 from the New School University, New York, New York. My
professional fields are economics and statistics. I have taught numerous college statistics
courses and worked as a statistical data analyst for both the private and public sectors. My
Curriculum Vita is attached as Exhibit A.
3. I have reviewed the following information in connection with this affidavit:
1. Time stamped CNN screen shots of “unadjusted exit poll data” (UEP), as explained in
points 6 - 9 below, for the 2016 general election, for the Presidential races from Theodore
de Macedo Soares for the 28 states where Presidential exit polls where conducted, and for
the Senate races from the 21 states where exit polls were conducted. UEP Senate race data
for MO is from a CNN screen shot captured by Jonathan Simon. Time stamped screen
shots for the 2016 Presidential general election are here.
2
Time stamped screen
1
This Affidavit was drafted in December 2016 at the request of a group of Attorney’s, many of whom involved in the Green
Party recount effort, who were planning to request an emergency injunction against Electoral College certification. The
effort was abandoned when it became clear that it would be futile without support from a “major party candidate,” i.e. the
Clinton campaign. This data and analysis expands on, and corrects and updates, data published in an earlier 12/10/2016
article: “U.S. 2016 Unadjusted Exit Poll Discrepancies Fit Chronic Republican Vote - Count Rigging, not Random
Statistical, Patterns”.
2
These were kindly provided by Theodore de Macedo Soares of www.tdmsresearch.com author of this exit polling article:
http://tdmsresearch.com/2016/11/10/2016-presidential-election-table/ .
2
shots for the 2016 Senate general election are here.
3
2. Official 2016 Election vote counts (VC) for the U.S. President and U.S. Senate from CNN
website downloaded Dec. 12 and Dec. 13, 2016 for all the states where exit polls were
conducted.
a. Sample sizes for final adjusted exit polls (AEP) by state for President
and Senate from the CNN website, downloaded Dec. 14, 2016.
4. In the following I will show that:
a. There was a one-sided “red shift” or margin of victory VC shift for
Presidential candidate Donald Trump relative to his UEP margin of
victory, in the 26 out of the 28 states where exit polls were
conducted. The odds for such a one-sided VC shift for Trump in
multiple states occurring as result of random sampling, or statistical,
error, is a nearly impossible 1 in 710,147.
b. This included statistically significant reduced VC relative to UEP for
Presidential candidate Hillary Clinton that was likely to happen by
chance less than 5% of the time occurred in seven states: MO, OH,
NJ, PA, UT, ME, and NC. For example:
c. Candidate Clinton’s reported PA VC of 47.6% was below the lower
end of a 95% confidence CI, showing a statistically significant VC
discrepancy with her UEP that would be expected to occur by chance
3
As is noted in the text, the MO general election Senate race screen shot was generously supplied by Jonathan Simon,
Director and Co-Founder of the Election Defense Alliance.
3
only 1.1282% of the time, or with less than a 1 in 85 chance.
d. This pattern of pervasive “red shift” also included statistically
significant VC increases for candidate Trump relative to his UEP
that were likely to happen by chance less than 5% of the time in OH,
NC, MO, IA, NJ, GA, WI, ME, FL, PA, IN, SC, NV, NH, UT, CO,
and AZ. Of these, highly significant VC shifts for Trump were
concentrated in the battleground or deep red states of: OH, NC, MO,
IA, GA, WI, and FL. As is explained below, the UEPs for FL and MI
are likely to be at least partially adjusted and thus not true UEPs.
The 2 out of 28 cases of UEP VC deviations against Trump in MN
and NY were not statistically significant. For example:
e. For example, the official WI VC of 47.8% for candidate Trump was
above the upper end of a 95% confidence interval (CI) around the
UEP for Trump in WI showing a statistically significant VC
discrepancy that would be expected to occur by chance only 0.163%
of the time, or less than a 1 in 614 chance.
f. The official NC VC of 47.8% for candidate Trump was above the
upper end of a 95% CI around the UEP for Trump in NC, showing a
statistically significant VC discrepancy with his UEP that would be
expected to occur by chance only 0.0055% of the time, or less than a
1 in 18,073 chance.
4
g. The official FL VC of 49.1% for candidate Trump was above the
upper end of a 95% CI around the UEP for Trump in FL, showing a
statistically significant VC discrepancy with his UEP that would be
expected to occur by chance only 0.3872% of the time, or less than a
1 in 258 chance, and as is noted below this is most likely an
underestimate of the odds as the FL UEP was probably already
partially adjusted to match the VC due to state of FL covering two
time zones.
h. Consistent and statistically unsupportable “red shift” in 19 out of 21
Senate races for which Senate race exit polls were conducted. The
odds of the Democratic candidate UEP being greater than his or her
VC in 19 out of 21 Senate races due to statistical random sampling
error are less than 1 in 9,986.
i. VCs were lower than UEP for Democratic Senate candidates in 19
out of the 21 states for which Senate exit polls were conducted. The
odds of this occurring again being 1 in 9,986. These 19 states
include three key competitive races in MO, WI, and PA where the
VC was lower than the UEP by a statistically significant margin and
the Democratic candidate would have won based on the UEP but lost
in terms of VC. All of the statistically significant deviations except
one in CA are against the Democratic Senate candidate and the VC
5
increase over UEP in CA would not have affected the outcome of
that race. For example:
j. MO Democratic Senate Candidate Kander received a 52.3% UEP
share but received 46.2% in the official VC. This would be expected
to occur by chance only 0.01% of the time, or less than a 1 in 11,082
chance. WI Democratic Senate candidate Feingold received 50.7%
in his UEP but a 46.8% official VC share. This would be expected to
occur by chance only 0.05% of the time, or with less than a 1 in
1,975 chance.
k. PA Democratic Senate candidate McGinty received a 50.0% UEP
but an official VC share of 47.2%. This would be expected to occur
chance 1.66% of the time, or with less than a 1 in 60 chance.
l. VCs greater than UEPs for Republican Senate candidates in 16 out
of 21 races for which Senate race exit polls were conducted. The
odds of this occurring due to random sampling error are less than 1
in 103. There are no cases of statistically significant shift against the
Republican Senate candidate.
m. I conclude that it is nearly impossible to think of a plausible
statistical, or innocent exit poll error, rationale for the one-sided “red
shift” UEP discrepancy patterns, with the most highly significant
discrepancies occurring in key battle ground and deep-red states, in
6
the 2016 U.S. general election. These repeated patterns of exit poll
discrepancies with official vote counts are in practice, statistically
impossible, but plausible from a political or election security
standpoint. In other words, the only plausible explanations are how
votes are counted, not counted, or miscounted by partisan and
largely unmonitored and unregulated election officials, or other
external violators of election security, such as domestic or foreign
hackers.
5. Executives of the polling company Edison Research that conducts the exit polls for
the mainstream media consortium in the U.S. have repeatedly confirmed that
published Edison adjusts actual exit polling data to be consistent with official vote
counts. For example, Joe Lenski, CEO of Edison research, is quoted in a Pew
Research article as saying:
6. “We will know shortly after the polls close,” Lenski said. “We’ll have individual
precinct results from all the locations where we conducted interviews, so we’ll
know how much understatement or overstatement for the candidates we have. Our
calls are based on all the information we have at the time exit polls, returns from
sample precincts and county results from AP and we may re-weight the exit poll
results later in the evening to match the vote estimates by geographic region.”
7. The rationale for this adjustment is the blanket assumption made by the mainstream
media and establishment politicians that U.S. officials returns could not possibly be
systemically wrong by anywhere near the magnitude of the unadjusted exit poll
7
deviations that have been occurring in U.S. elections since 1988. This is the case
even though, as will be shown below, attempts to explain these large and systemic
deviations as resulting from large-scale and one-sided exit poll error have been
repeatedly disproven by the data, for example for the 2004 election and for pre-
election polls in 2016 election.
8. Accordingly, in this paper, will analyze “unadjusted exit poll” (UEP) results that
have captured by screen shots of exit polls publicized as soon as possible
immediately right before or after closing of state election polls. These UEP results
are the best real exit poll data that we have in the U.S. as Edison does not release
UEP results in any other fashion.
4
9. From each screen shot I have calculated the Democratic President and Senate
candidate UEP results for each state by multiplying the overall male share by the
male Democratic candidate vote share and adding it to the overall female vote share
by female Democratic candidate vote share. Similarly I have calculated UEP results
for the Republican candidates for President and Senate. These are presented in
Exhibits G for the President and Exhibit H for the Senate races.
10. Exhibit G also includes calculations of the ratio between UEP sample sizes and
final adjusted exit poll (AEP) samples sizes by state for the Presidential race by
state. As can be seen in Column Q, this ratio is less than 85% in only five of the 28
states (IL, ME, NH, NJ, and NM) for which Presidential exit polling was
conducted. I conclude that for the purposes of this analysis the screen shots of UEP
4
It is important to note, as Jonathan Simon has pointed out, that though as far as we know these are the best
UEP data available, in some or all cases they may already have been adjusted to match official results. This
is almost certain in states like Florida and Michigan that cross time zones so that first exit poll results are not
posted until an hour after polls in a large portion of the state have already closed.
8
results are from sufficiently large sample sizes relative to the final AEP, to be
statistically representative of complete sample UEPs for most of the states for
which Presidential exit polling was done.
11. Exhibit H also includes calculations of the ratio between UEP sample sizes and
final adjusted exit poll (AEP) samples sizes by state for the Senate race by state.
As can be seen in Column Q, this ratio is less than 84% in only two of the 21 states
(IL and UT) for which Senate exit polling was conducted. I conclude that for the
purposes of this analysis the screen shots of UEP results are from sufficiently large
sample sizes relative to the final AEP, to be statistically representative of complete
sample UEPs for most of the states for which Senate exit polling was done.
12. Exhibit I provides analysis of 2016 Presidential UEP “red shift”. “Red Shift” is the
increase in Republican candidate official vote count (VC) margin of victory over
UEP margin of victory, in other words, the discrepancy between the exits polls and
the office vote count.
5
Exhibit I shows states ordered by red shift magnitude. In the
2016 presidential election, there was an overwhelmingly one-sided shift to the
Republican candidate. In 22 out of the 28 states where exit polls were conducted,
and UEP data were captured, the Trump VC exceeded Trump's UEP numbers.
13. Assuming the exit polls were unbiased, the chance of “red shift” for any one state
would be 50% or 0.5. The odds of negative red shift that Trump's official vote
count would be better than his exit polls -- in 22 out of 28 such state UEP results
would then be 1 in 713. Those are the odds of getting 22 heads in 28 coin tosses.
(See cell 5K.)
5
In Exhibit I and later tables, red shift (column I) is defined as the negative percentage value of (Hillary VC
-Trump VC) (Hillary UEP Trump UEP).
9
14. A “shy Trump” voters, explanation has been proffered for the widespread
statistically significant and one-sided deviations of official vote counts from pre-
election polls and again disproven by the data. Similarly, the notion that an
unforeseen surge in Trump voters that was not taken into account by pre-election
polls or the exit pollsters in assigning weights necessary to derive state level exit
poll results from precinct exit poll samples, was the problem, is not consistent with
UEP data from the 2016 primary elections that shows no consistent UEP bias in the
Republican primary. If anything one would expect that surges in Trump voters that
were unforeseen by the exit pollsters would be a greater problem in the primary
when Trump was initially still viewed as a marginal candidate, and the most
committed Trump voters were voting. The “Trump surge” or “Trump Shyness”
explanation is even more questionable in light of highly significant exit poll
discrepancies particularly in battleground and deep red states and not consistent
across states. Perhaps an argument could be made for greater turnout efforts in
battleground states, but why would the discrepancies arise occur in deep red states
where Trump was most likely going to win anyway? And if Trump supporters were
generally hyper-motivated, or covert, why were there not similar “Trump surges” or
“Trump Shyness” in UEP response in other states like New York where one would
expect the social stigma of identifying as a Trump supporter would be greater?
15. Voting integrity organizations have repeatedly requested precinct UEPs and official
counts so that analysis that would be unaffected by precinct weights could be
conducted. The American Association for Public Opinion Research (AAPOR) code
of ethics disclosure standards that specify that the geographic location of the
10
population sampled should be disclosed. Nevertheless, requests for precinct
information, including my own, have been ignored or denied by Edison. The
reason given for these nondisclosures is that such information is, despite its obvious
vital public importance, proprietary private information. Disclosure is refused even
though the UEP and official vote count margins are all that is needed and could be
provided without disclosing the exact locations of exit polled precincts.
16. Moreover, the one case in in Ohio in 2004, where precinct level exit polls and vote
counts were inadvertently obtained, precinct level analysis revealed statistically
highly significant discrepancies that were not a result of state level weighting. In
addition, follow-up direct investigation of polling books and central tabulators from
the 2004 election in Miami County, Ohio revealed numerous discrepancies between
voters and official vote numbers and central tabulator miscounting acknowledged
by the Republican County Election Board Director. This demonstrated that
statistically significant discrepancies between UEPs and VCs in U.S. elections have
been tied to proven election irregularities. There is no statistical or logical basis
why these should not be investigated.
17. Investigation is recommended for foreign elections by the U.S. State Department
when UEP discrepancies with official vote counts like those appearing in the U.S.
2016 election, such as those in Wisconsin, Pennsylvania, and Florida. . In 2015, the
U.S. Agency for International Development published a pamphlet entitled:
Executive Summary: Assessing And Verifying Election Results, A Decision-
Maker’s Guide To Parallel Vote Tabulation And Other Tools. It had this to say on
exit polls and detecting election fraud in overseas elections: It notes under the
11
heading “Detect Electoral Fraudthat: “Other tools, such as exit polls and election
forensics techniques, can highlight anomalies in results that may suggest
irregularities in the voting or counting process.” (p. 11). Eric Bjornlund and Glenn
Cowan’s 2011 pamphlet Vote Count Verification: a User’s Guide for Funders,
Implementers and Stakeholders,” produced by Democracy International for the US
Agency for International Development (USAID), outlines how exit polling is used
to ensure free and fair elections.“U.S-funded organizations have sponsored exit
polls as part of democracy assistance programs in Macedonia (2005), Afghanistan
(2004), Ukraine (2004), Azerbaijan (2005), the West Bank and Gaza Strip (2005),
Lebanon (2005), Kazakhstan (2005), Kenya (2005, 2007), and Bangladesh
(2009),among other places,” the pamphlet states.
18. Though “red shift” is a measure of overall candidate VC versus UEP margin of
victory, it is difficult to analyze statistically as candidate voting shares are not
independent of each other. In a two way race vote shares would be exact
complements and “red shift” would be exactly twice the size of each candidate’s
VC versus UEP deviation. With third party candidates in the race, the vote share
relationship between the two major party candidates will not be exactly
determinate. Standard statistical analysis of the difference of two independent
proportions is thus not applicable.
19. The most appropriate and practical method to analyze such proportions is separate
single proportion analysis of each major candidate’s VC versus UEP vote share.
The analysis is a standard single proportion deviation analysis of official vote count
share deviation from UEP share. The only adjustment is a 30% “clustered
12
sampling” increase in the random standard deviation estimate due to the fact that
though exit poll samples are approximately random samples of precincts responses
are geographically clustered as they come from precincts selected by pollsters to be
representative of the state See p. 9, footnote 22 of this. A direct empirical test of this
adjustment factor by Theodore De Macedo Soares for the 2016 Republican primary
results that (unlike the 2016 Democratic primary results that exhibit highly
significant non-random error bias against Sanders) show that that by increasing the
standard statistical margin of error by 32% random exit poll error from all factors is
reduced to the standard statistically acceptable level of below 5%.
6
20. (Exhibit P) below shows the results of this analysis for Clinton UEP minus VC
shares. Column D shows VC minus UEP percentage for Clinton so that a positive
percentage indicates that Clinton’s vote count was less than her UEP share.
Column G is the sample standard deviation (SD) estimated to be 30% larger than
the standard random sample standard deviation after the cluster sampling
adjustment. Column H gives the “Z-Score,” or number of SD’s, of the UEP VC
deviation. Column I gives one-tailed P-Values (on either side of the distribution) for
each state assuming a standard normal population with a mean equal to the UEP for
Clinton and SD estimated in Column G. The P-value for each state is the random
statistical probability of the Clinton VC given the Clinton UEP for that state. The
lower the P-Value the less likely it is that the VC would occur as a result of random
6
Note that Soares uses standard statistical analysis to calculate the Margin of Error (MOE) of the
“red shift” based on this reference. However, as this MOE is roughly twice the Margin of Error for
one poll that I use, the 30% factor remains applicable for the more conservative MOE calculation
that I use.
13
chance. P-values less than 5% are considered statistically significant as they
indicate a 5% or less random chance that the VC share would be this different from
the UEP share. Column J presents the same information (one divided by P-Value)
in terms of the odds of VC share occurring given the UEP share. Columns K and L
give the lower and upper bounds of the 95% confidence interval, or the range of VC
values that have a 95% probability of occurring, given the Clinton UEP result.
Since this is a two-tailed confidence internal, only VCs with P-values of 2.5% or
less will be outside of this confidence interval.
21. As can be seen in Exhibit P, statistically significant VC reductions from Clinton’s
UEP shares (with P-Value of less than 5%) occurred in MO, OH, NJ, PA, UT, ME,
and NC. The analysis thus shows that Clinton suffered statistically significant VC
reduction relative to UEP share in a small number of battle ground states (OH, MO,
PA, and NC), the deep-red state of UT, and NJ, a state with a Republican Governor
and Trump ally. ( As discussed above, that UEPs for FL and MI are likely to be
partially adjusted and thus not true UEPs). OH in particular has a long history
dating back at least to 2004 of faulty official vote count reporting, for example the
documented inconsistencies and miscounting in Miami County noted above. Note
that Exhibit P shows some evidence of statistically significant (below 5% P-value)
Democratic UEP VC discrepancy for Clinton in the deep blue state of NY, but as
can be seen from the odds in Column J, the level of significance is much smaller
than the pervasive discrepancies against Clinton in multiple states noted above.
22. Exhibit Q below illustrates the Clinton UEP PA analysis conveyed in Exhibit 2, line
8. The normal distribution bell curve is centered around Clinton’s PA 50.5% UEP
14
share and has a 1.3% SD (or approximate “width”) as calculated in Figure 2. Based
on this SD, the 95% Confidence Interval (CI) displayed in the graph ranges from
48% to 53% as shown in Figure 2. This indicates that there was a 95% chance that
Clinton’s PA VC would fall within this range due to statistical sampling error. The
blue area over the CI under the bell curve distribution contains 95% of the total area
under the bell curve. As shown in Exhibit Q Clinton’s reported PA VC of 47.6% is
below the lower end of the CI, showing a statistically significant VC discrepancy
with her UEP that would be expected to occur by chance only 1.1282% of the time,
or less than a 1 in 85 chance.
23. Exhibit R shows the results of this analysis for Trump UEP minus VC shares.
Column D shows VC minus UEP percentage for Trump so that a negative
percentage indicates that Trump’s vote count was greater than his UEP share.
Column G is the sample standard deviation (SD) estimated to be 30% larger than
the standard random sample standard deviation after the cluster sampling
adjustment. Column H gives the “Z-Score,” or number of SD’s, of the UEP VC
deviation. Column I gives one-tailed P-Values (on either side of the distribution) for
each state assuming a standard normal population with a mean equal to the UEP for
Clinton and SD estimated in Column G. The P-value for each state is the random
statistical probability of the Clinton VC given the Clinton UEP for that state. The
lower the P-Value the less likely it is that the VC would occur as a result of random
chance. P-values less than 5% are considered statistically significant as they
indicate a 5% or less random chance that the VC share would be this different from
the UEP share. Column J presents the same information (one divided by P-Value)
15
in terms of the odds of VC share occurring given the UEP share. Columns K and L
give the lower and upper bounds of the 95% confidence interval, or the range of VC
values that have a 95% probability of occurring, given the Clinton UEP result.
Since this is a two-tailed confidence internal, only VCs with P-values of 2.5% or
less will be outside of this confidence interval.
24. As can be seen in Exhibit R, statistically significant VC discrepancies with Trump
UEP shares (with p-value less than 5%) occurred in OH, NC, MO, IA, NJ, GA, WI,
ME, FL, PA, IN, SC, NV, NH, UT, CO, and AZ (as noted in footnote 1 UEPs for
FL and MI are likely to be at partially adjusted and thus not true UEPs). In all of
these states Trump’s VC was greater than his UEP by a statistically significant
margin. Note that though there were UEP VC deviations against Trump in MN
and NY these were not statistically significant. Most of the most highly significant
VC shifts for Trump were concentrated in the battleground or deep red states: OH,
NC, MO, IA, GA, WI, and FL suggesting that these “errors” were not random but a
result of how the VC was counted.
25. Unlike the overall VC shift against Clinton, the odds for such a one-sided VC shift
for Trump in multiple states occurring as result of random sampling, or statistical,
error, is a nearly impossible 1 in 710,147, as shown in Exhibit R cell 5M using a
calculation similar to that used for cell 5J in Exhibit I.
26. Exhibit S below illustrates the Trump UEP WI analysis conveyed in Exhibit R, line
11. The normal distribution bell curve is centered around Trump’s 44.3% WI UEP
share and has a 1.2% SD (or approximate “width”) as calculated in Exhibit R.
Based on this SD the 95% Confidence Interval (CI) displayed in the graph ranges
16
from 42.0% to 46.6% as shown in Exhibit S. This implies that there was a 95%
chance that Trump’s WI VC would fall within this range due to statistical sampling
error. The blue area over the CI under the bell curve distribution contains 95% of
the total area under the bell curve. As shown in Exhibit S Trump’s reported WI VC
of 47.8% is above the upper end of the CI, showing a statistically significant VC
discrepancy with his UEP that would be expected to occur by chance only 0.163%
of the time, or less than a 1 in 614 chance.
7
27. Exhibit T below illustrates the Trump UEP NC analysis conveyed in Exhibit R, line
6. The normal distribution bell curve is centered around Trump’s 46.5% NC UEP
share and has a 1.0% SD (or approximate “width”) as calculated in Exhibit 4 4.
Based on this SD the 95% Confidence Interval (CI) displayed in the graph ranges
from 44.5% to 48.5% as shown in Figure R. This implies that there was a 95%
chance that Trump’s NC VC would fall within this range due to statistical sampling
error. The blue area over the CI under the bell curve distribution contains 95% of
the total area under the bell curve. As shown in Exhibit T Trump’s reported NC VC
of 47.8% is above the upper end of the CI, showing a statistically significant VC
discrepancy with his UEP that would be expected to occur by chance only 0.0055%
of the time, or less than a 1 in 18,073 chance.
8
28. Exhibit U below illustrates the Trump UEP FL analysis conveyed in Exhibit 4, line
13. The normal distribution bell curve is centered around Trump’s 46.4% FL UEP
share and has a 1.0% SD (or approximate “width”) as calculated in Figure 4. Based
on this SD the 95% Confidence Interval (CI) displayed in the graph ranges from
7
Odds in Exhibit R are conservatively rounded down to 1 in 610.
8
Odds in Exhibit T are conservatively rounded down to1 in 18,000.
17
44.3% to 48.4% as shown in Exhibit R. This implies that there was a 95% chance
that Trump’s FL VC would fall within this range due to statistical sampling error.
The blue area over the CI under the bell curve distribution contains 95% of the total
area under the bell curve. As shown in Exhibit U Trump’s reported FL VC of
49.1% is above the upper end of the CI, showing a statistically significant VC
discrepancy with his UEP that would be expected to occur by chance only 0.3872%
of the time, or less than a 1 in 258 chance.
9
Moreover, as was noted above this is
most likely an underestimate of the odds as the FL UEP was probably already
partially adjusted to match the VC due to FL crossing two time zones.
29. In the following the 2016 Senate Races discussed below are analyzed using the
same method as the Presidential race. Exhibit V shows that “red shift” flipped three
Senate races in MO, WI, and PA from Democratic to the Republican candidates. If
the Democratic candidates had won these three highly contested races, Democrats
would have retaken the majority in the Senate.
30. Exhibit V also shows that the 2016 Senate races showed a consistent and
statistically unsupportable “red shift” in 19 out of 21 races for which UEP were
available. The odds of the Democratic candidate UEP being greater than his or her
VC in 19 out of 21 Senate races due to statistical random sampling error are less
than 1 in 9,986 as can be seen in cell 5J in Exhibit V.
31. Exhibit W below shows that VCs were lower than UEP for Democratic Senate
candidates by statistically significant margins in key competitive races including the
races in MO, WI, and PA that flipped in the VC versus UEP outcomes. Note again
9
Odds in Exhibit U are conservatively rounded down to 1 in 220.
18
that all of the statistically significant deviations except one in CA are against the
Democratic Senate candidate. And the one in CA would not have affected the
outcome of that race.
32. Overall, VCs were less than UEP for Democratic Senate candidates in 19 out of 21
races for which UEPs were conducted. The odds of this occurring due to random
sampling error are less than 1 in 9,986 as can be seen in Exhibit W, cell 5M.
33. Exhibit X below shows that VCs were greater than UEPs for Republican Senate
candidates by statistically highly significant margins in key competitive races
including the races in MO and WI. Interestingly, this was not the case in PA where
the statistically significant “red shift” was entirely a result of the Democratic
candidate’s loss of VC relative to his UEP.
34. Overall, VCs were greater than UEPs for Republican Senate candidates in 16 out of
21 races for which UEPs were conducted. The odds of this occurring due to random
sampling error are less than 1 in 103 as can be seen in Exhibit X, cell 5M. There
are no cases of statistically significant shift against the Republican Senate
candidate, though CA comes close.
35. Exhibit Y below illustrates the Kander UEP MO analysis conveyed in Exhibit W,
line 7. The normal distribution bell curve is centered around Kander’s 52.3% MO
UEP share and has a 1.6% SD (or approximate “width”) as calculated in Figure 9.
Based on this SD the 95% Confidence Interval (CI) displayed in the graph ranges
from 49.1% to 55.5% as shown in Figure 9. This implies that there was a 95%
chance that Kander’s MO VC would fall within this range due to statistical
sampling error. The blue area over the CI under the bell curve distribution contains
19
95% of the total area under the bell curve. As shown in Exhibit W, Kander’s
reported MO VC of 46.2% is below the lower end of the CI, showing a statistically
significant VC discrepancy with his UEP that would be expected to occur by chance
only 0.01% of the time, or less than a 1 in 11,082 chance.
10
36. Exhibit Z below illustrates the Feingold UEP WI analysis conveyed in Exhibit W,
line 9. The normal distribution bell curve is centered around Feingold’s 50.7% WI
UEP share and has a 1.2% SD (or approximate “width”) as calculated in Exhibit W.
Based on this SD the 95% Confidence Interval (CI) displayed in the graph ranges
from 48.4% to 53.1% as shown in Exhibit Z. This implies that there was a 95%
chance that Feingold’s WI VC would fall within this range due to statistical
sampling error. The blue area over the CI under the bell curve distribution contains
95% of the total area under the bell curve. As shown in Exhibit Z Feingold’s
reported WI VC of 46.8% is below the lower end of the CI, showing a statistically
significant VC discrepancy with his UEP that would be expected to occur by chance
only 0.05% of the time, or less than a 1 in 1,975 chance.
11
37. Exhibit AA below illustrates the McGinty UEP PA analysis conveyed in Exhibit W,
line 13. The normal distribution bell curve is centered around McGinty’s 50.0% PA
UEP share and has a 1.3% SD (or approximate “width”) as calculated in Exhibit W.
Based on this SD the 95% Confidence Interval (CI) displayed in the graph ranges
from 47.4% to 52.5% as shown in Exhibit 9. This implies that there was a 95%
chance that McGinty’s PA VC would fall within this range due to statistical
sampling error. The blue area over the CI under the bell curve distribution contains
10
Odds are conservatively rounded to 1 in 11,000 in Exhibit Y.
11
Odds are lower at 1 in 1861 (result of calculation before data update) in Exhibit Z.
20
95% of the total area under the bell curve. As shown in Exhibit AA McGinty’s
reported PA VC of 47.2% is below the lower end of the CI, showing a statistically
significant VC discrepancy with her UEP that would be expected to occur by
chance only 1.66% of the time, or less than a 1 in 60 chance.
38. In conclusion, it is nearly impossible to think of a plausible statistical, or innocent
exit poll error, rationale for the one-sided “red shift” UEP discrepancy patterns,
with the most highly significant discrepancies occurring in key battle ground and
deep-red states, in the 2016 U.S. general election. These repeated patterns of exit
poll discrepancies with official vote counts are in practice, statistically impossible,
but plausible from a political or election security standpoint. In other words, the
only plausible explanations are how votes are counted, not counted, or miscounted
by partisan and largely unmonitored and unregulated election officials or other
external violators of election security, such as domestic or foreign hackers.
21
Exhibit A Curriculum Vitae for Ron Baiman
PERSONAL INFORMATION
Name: Ron Paul Baiman Date: August 7, 2017
Home address: 205 S. Humphrey Ave. Phone: (708) 445-9052
City: Oak Park State: Illinois Zip: 60302
E-mail: Baiman@sbcglobal.net
EDUCATION
Ph.D. Economics, 1992
New School for Social Research, New York, NY
Dissertation Title: "Non-Neoclassical Microeconomics: A Nominal Total Bill Approach to Residential
Telephone Usage Demand Estimation"
M.A. Economics with Honors, 1981
New School for Social Research, New York, NY
One Year Graduate Studies, Mathematics
University of California, Berkeley, 1974 and 1978
B. Sc. Mathematics and Physics, magna cum laude, 1973
Hebrew University, Jerusalem, Israel
EXPERIENCE
8/13 - present. Assistant Professor of Economics, Graduate Business Administration, Benedictine
University, Lisle, IL. Teaching economics courses to MBA students, and working on academic research
projects and university service activities.
6/13 - 8/13. Economic Development Planner, Center for Urban Economic Development, University of
Illinois at Chicago, Chicago, IL. Performed data research for State of Working Chicago report and for
Illinois Labor Force projection estimates.
22
8/09 11/12. Director of Budget and Policy Analysis, Center for Tax and Budget Accountability,
Chicago, Illinois. Lead research and analysis of state and local budget and policy issues. With other
staff, author reports, present testimony and public presentations, and engage in media outreach. Consult
union leaders, public officials, journalists, and other key policy actors in Illinois on state and local policy
issues.
8/06 -8/09. Research Economist, Illinois Department of Employment Security, Chicago, Illinois. Senior
economic researcher for special projects and consultant for upper management of Market Analysis and
Information Division.
3/00 6/07. Visiting Assistant Professor, University of Chicago, Chicago, IL. Co- instructor of
senior capstone course on " Neo-Liberalism and Neo-Imperialism", and before that: “Globalization and
Neo-Liberalism,” as part of the "Big Problems" Spring Program.
1/06 6/07. Policy Research Project Development Analyst, Center for Urban Research and Learning,
Loyola University, Chicago, Illinois. Principal investigator for research on the economic impact of a
Chicago west side Wal-Mart.
9/05 -1/06. Visiting Research Assistant Professor, College of Urban Planning and Public Affairs,
University of Illinois at Chicago. In collaboration with other project team members, work on analyses of
economic impacts of federal funded transportation projects targeted toward low-income and otherwise
disadvantaged populations.
12/04 - 1/06. Visiting Senior Economic Research Specialist, Institute of Government and Public Affairs,
University of Illinois at Chicago. Project Director and co-principal investigator for research on the
economic impact of the 2003 Illinois minimum wage increase and other local, state, and national,
economic and public policy issues.
9/04 -12/04. Visiting Senior Economic Research Specialist, Department of Economics, University of
Illinois at Chicago. Senior economic researcher and co-principal investigator for research on the
economic impact of the 2003 Illinois minimum wage increase.
9/01 - 9/04. Visiting Research Assistant Professor, Center for Urban Economic Development, University
of Illinois at Chicago. Senior economic researcher on a wide variety of state and local economic
development issues including Illinois state business tax incidence. Position included supervision of
graduate student researchers and some teaching responsibilities.
8/94 - 8/01. Assistant Professor of Economics, Dept. of Economics, Roosevelt University, Chicago, IL.
Teaching graduate and undergraduate courses in: introductory microeconomics and macroeconomics,
advanced microeconomics, modern political economy, comparative systems, statistics, and intermediate
macroeconomics.
23
1/94 - 6/94. Visiting Professor, New School for Social Research, Graduate School for Management and
Urban Policy, New York, N.Y. Taught Statistical and Research Methods for graduate students in urban
policy and public and non-profit administration programs.
1/93 - 6/94. Associate Director, Telecommunications Exchange, Dept. of Economic Development of
New York State, New York, N.Y. Responsible with the Director for facilitating the formulation of joint
government-business-labor policy recommendations, focusing on telecommunications and economic
development, for the governor. In this capacity responsible for writing, and supervising the writing of,
briefing and policy reports, and of overseeing consultant contracts.
12/91 - 1/93. Modeling Manager, AT&T, Database Marketing Services, Bridgewater, NJ. Responsible
for statistical modeling of customer database information for marketing purposes.
10/87 - 12/91. Manager, AT&T, Consumer Communications Services Forecasting, Basking Ridge, NJ.
Manager of Domestic Consumer Long Distance direct-dial rate evaluation. Responsibilities included:
estimation of rate change effects on telephone usage demand for regulatory and planning purposes, state
level regulatory support, and research into household level estimation of telephone usage demand price
elasticities.
1/87 - 9/87. Instructor, Division of Liberal Arts, Mount Ida College, Newton, MA. Taught
macroeconomic and microeconomic Principles, and conducted an upper division seminar on current
issues in economics.
6/86 - 9/87. Research Associate, Regional Science Research Center, University of Lowell, Lowell, MA.
Responsibilities included literature review and data base preparation for ongoing research.
9/85 - 12/86. Visiting lecturer, School of Graduate and Continuing Education, Framingham State
College, Framingham, MA. Taught quantitative methods for business administration within the graduate
business administration program, a graduate course in quantitative methods for health care with
computer applications and an undergraduate statistics course.
9/84 - 6/86. Lecturer, Economics Dept., University of Massachusetts at Lowell. Taught undergraduate
statistics, and microeconomics and macroeconomics principles courses.
9/83 - 6/84. Research and Teaching Assistant, New School for Social Research, New York, N.Y.
Taught Lab section for graduate level math methods in economics course.
24
PROFESSIONAL AWARDS AND SERVICE
8/95- present. Member of the Policy Council of Illinois Citizens Action.
8/99 - present. Member of the Editorial Board of the Review of Radical Political Economics.
12/05 - Recipient of Woods Foundation Grant, with Joseph Persky, for “An Empirical Evaluation of the
Economic Development Impact of the West Side Chicago Wal-Mart”
11/04 - Recipient of Russell Sage Foundation Grant, with Joseph Persky and Elizabeth Powers, for study
of the “Impacts of the Illinois Minimum Wage: Employment, Hours, and Labor Substitution in the Fast
Food Industry.”
1/01 - Choice award for "Best Academic Title", with Heather Boushey and Dawn Saunders, for Political
Economy and Contemporary Capitalism: Radical Perspectives on Economic Theory and Policy, M. E.
Sharpe, 2000.
10/00 10/01. President of the “Illinois Economics Association” (IEA).
COURSES TAUGHT
Advanced Microeconomics: Self developed graduate course with focus on critiques of Neoclassical
Microeconomics and Post Keynesian alternatives.
Introductory Microeconomics: Undergraduate introduction to microeconomics.
Economic Development: Planning. Graduate course on regional planning strategies and techniques
taught in the College of Urban Planning and Public Affairs.
Statistical and Research Methods: Graduate course on statistical and research methods for students of
urban policy and public and non-profit administration.
Quantitative Methods for Business: Graduate course on quantitative methods for students of business
management.
Quantitative Methods for Health Care: Graduate course on quantitative methods for health care
administration students.
Introduction to Statistics: Introductory undergraduate statistics course for social science and engineering
students.
Mathematical Methods for Economics: Lab section instructor for graduate course on mathematical
methods in economics.
Current Issues in Economics: Upper level undergraduate seminar on contemporary economic policy
issues.
Introductory Macroeconomics: Undergraduate introduction to macroeconomics.
25
Intermediate Macroeconomics: Upper level undergraduate and graduate survey of the major modern
schools of macroeconomics with a particular focus on Post Keynesian macroeconomics.
Globalization and Neo-Liberalism: Self developed, with another instructor, interdisciplinary course on
the history and political economy of globalization and Neo-Liberalism.
From Neo-Liberalism to Neo-Imperialism: Self developed, with another instructor, interdisciplinary
course on the political economy and history of Neo-Liberalism and Neo-Imperialism.
Comparative Economic Systems: Self-developed upper level undergraduate and graduate course on
alternate political economic systems in a wide variety of countries.
Modern Political Economy: Self-developed upper level undergraduate and graduate course on Neo-
Marxist, Post Keynesian, and Radical Economic theory and policy.
History of Economic Thought: Self developed tutorial for Independent Study offering. The Course
emphasized the ideas of Joseph Schumpeter and Robert Owen in accordance with student interest.
Economies in the International Context: International Studies graduate course comparing different
national economies.
SCHOLARLY PUBLICATIONS
Books
The Global Free Trade Error: The Infeasibility of Ricardo’s Comparative Advantage Theory,
Routledge, 2017.
The Morality of Radical Economcs: Ghost Curve Ideology and the Value Neutral Aspect
of Neoclassical Economics, Palgrave Macmillan, 2016.
Political Economy and Contemporary Capitalism: Radical Perspectives on Economic Theory and
Policy, co-edited with Heather Boushey and Dawn Saunders, M. E. Sharpe, June, 2000.
Refereed Papers, Reviews, and Chapters in Books
Vote miscount or poll response bias? What causes discrepancy between polls and election results?”
with Kathy Dopp, Italian Journal of Applied Statistics Vol. 25 (3) 209-237.
“Unequal Exchange and the Rentier Economy,” Review of Radical Political Economics, December
2014, 46(4) 536-557.
The Impact of an Urban Wal-Mart Store on Area Businesses: The Chicago Case,” with David
Merriman, Joseph Persky, and Julie Davis, Economic Development Quarterly, November 2012, 26(4)
321-333.
26
"A Permanent Jobs Program for the U.S.: Economic Restructuring to Meet Human Needs," with
Williaml Barclay, Sidney Hollander, Haydar Kurban, Joseph Persky , Elce Redmond, and Mel
Rothenberg, Review of Black Political Economy, March, 2012, 29(1) 29-41.
"Do State Minimum Wage Laws Reduce Employment? Mixed Messages from Fast Food Outlets in
Illinois and Indiana," with Joseph Persky, Journal of Regional Analysis and Policy, 40(2):132-142,
2010.
The Infeasibility of Free Trade in Classical Theory: Ricardo's Comparative Advantage Parable has no
Solution,” Review of Political Economy, Vol. 22(3), July, 2010.
The Estimated Economic Impact of a Chicago Big Box Living Wage Ordinance,” Review of Radical
Political Economics, Vol. 38(3), Fall 2006.
“Unequal Exchange without the Labor Theory of Prices: On the need for a Global Marshall Plan and a
Solidarity Trading Regime,” Review of Radical Political Economics, Vol. 38(1), Winter 2006.
“Why Equity Cannot be Separated from Efficiency II: When Should Social Pricing be Progressive?,”
Review of Radical Political Economics, Vol. 34(3), Summer 2002.
“Why Equity Cannot be Separated from Efficiency: The Welfare Economics of Progressive Social
Pricing,” Review of Radical Political Economics, Vol. 33(2), Spring 2001.
“Why the Emperor has no Clothes: The Neoclassical Case for Price Regulation,” included in Political
Economy and Contemporary Capitalism, Baiman, Boushey, and Saunders, Eds. M. E. Sharpe, June,
2000.
“Neoclassical Economics and the End of Equitable, Open, and Universal Telecommunications Services
in the United States,” Review of Radical Political Economics, Vol. 27(3), September 1995.
“Structural Subemployment in the U.S. and the Full Employment Debate”, The Imperiled Economy, Vol
II, New York: URPE 1988.
Review of Deepening Democracy: Institutional Innovations in Empowered Participatory Governance,
by Archon Fung and Erik Olin Wright. Science and Society, Vol. 70(4) 2006.
Review of Market Socialism: The Debate among Socialists, edited by David Schweickart, James Lawler,
Hillel Ticktin, and Bertell Ollman, Science and Society, Vol. 63(4), winter 1999-2000.
Review of Socialism after Communism: The New Market Socialism, By Christopher Pierson, Science
and Society, Vol. 62(2), summer 1998.
Review of Lean and Mean: The Changing Landscape of Corporate Power in the Age of Flexibility, by
Bennett Harrison, Review of Radical Political Economics, Vol. 29(4), Dec. 1997.
Review of Against Capitalism, by David Schweickart, Review of Radical Political Economics, Vol.
27(1), 1995.
Review of The Overworked American: The Unexpected Decline of Leisure, by Juliet B. Schor, Review of
27
Radical Political Economics, Vol. 25(2), 1993.
SAMPLE RESEARCH AND OTHER REPORTS AND PUBLICATIONS
Why Did So Many Cook County Municipalities Vote for Increased Poverty and Super-Exploitation? July
10, 2017. Chicago Political Economy Group.
What a LaSalle Street Tax Would Do for Chicago, with Bill Barclay, in Chicago is Not Broke: Funding
the City We Deserve, Edited by Tom Tresser, 2016. CivicLab and the TIF Illumination Project.
We Don’t Need Another Casino: We Need to Tax the One We Have! with Bill Barclay, June 15, 2015.
Chicago Political Economy Group.
Restoring Chicago’s Fiscal and Economic Health, with Bill Barclay, Luis Diaz-
Perez, Caitlyn Prosapio, and June Zaccone, March 21, 2015. Chicago Political Economy Group.
CTBA Analysis of the Illinois FY 2013 General Fund Enacted Budget, with Ralph Martire, Yerik
Kaslow, Amanda Kass, and Jennifer Lozano, August, 2103, Center for Tax and Budget Accountability.
CTBA Analysis of Proposed Illinois FY 2013 General Fund Budgets, with Ralph Martire, Yerik Kaslow,
Amanda Kass, and Jennifer Lozano, April, 2103, Center for Tax and Budget Accountability.
Illinois Should Enact a Wage Sharing Program, June 2012, with Ralph Martire, Kathy Miller, and Zach
Lipchutz, Center for Tax and Budget Accountability.
Raise the Illinois Minimum Wage Now, May 2012, Center for Tax and Budget Accountability.
The Case for Creating a Graduated Income Tax in Illinois, with Ralph Martire, Yerik Kaslow, Amanda
Kass, and Jennifer Lozano, February, 2012, Center for Tax and Budget Accountability.
Cost Benefit Analysis of Chicago's Proposed Stable Jobs, Stable Airports Ordinance, with Virginia
Parks, Jack Metzgar, and William Sites, November, 2011. Chicago Political Economy Group,
University of Chicago, and Roosevelt University.
CTBA Analysis of the Enacted FY 2012 Illinois Budget, with Ralph Martire and Yerik Kaslow, October,
2011, Center for Tax and Budget Accountability.
Wrong Time to Implement New Tax Breaks, with Ralph Martire, Nov. 2011, Center for Tax and Budget
Accountability.
FY 2011 Illinois Proposed Budget Analysis, March 2011, with Ralph Martire, March 2011, Center for
Tax and Budget Accountability,.
Issue Brief: The Taxpayer Accountability and Budget Stabilization Act (P.A. 96-1496), with Ralph
Martire, February, 2011, Center for Tax and Budget Accountability.
Funding our Future: Reforming Illinois Tax Policy, with Ralph Martire, Yerik Kaslow, and Mason
Laird, October, 2010, Center for Tax and Budget Accountability, ,.
28
Issue Brief: A Comparison of Major Illinois Tax Proposals, with Ralph Martire, Center for Tax and
Budget Accountability, September 2010.
Illinois Funding for Human Services in Context, Center for Tax and Budget Accountability, with Ralph
Martire, Center for Tax and Budget Accountability, February, 2010.
A Permanent Jobs Program for the U.S.: Economic Restructuring to Meet Human Needs, with Bill
Barclay, Sidney Hollander, Joe Persky, Elce Redmond, and Mel Rothenberg, The Chicago Political
Economy Group, February, 2009.
Replacing the Baby Boomers: An Industry Perspective, with George Putnam and Allan Ross, State of
Working Illinois Policy Brief, Northern Illinois University Regional Development Institute, November
2006.
Was the 2004 Election Stolen? The History, The Crime, The Cover-Up, and Conclusions, American
Association of Public Opinion Research Conference (AAPOR), Montreal, May 2006.
The Gun is Smoking: 2004 Ohio Precinct-Level Exit Poll Data Show Virtually Irrefutable
Evidence of Vote Miscount, with Kathy Dopp, US Count Votes / National Election Data
Archive, January 2006.
Official States Electronic Voting System Added Votes Never Cast In 2004 Presidential Election: Audit
Log Missing, with Peter Peckarsky and Robert Fitrakis. The Free Press , November 2006
A Longitudinal Analysis of Effects of Lack of Adequate Transportation Access, with Piyushimita
Thakuriah and Yihua Liao, Urban Transportation Center, University of Illinois at Chicago,
North American Regional Science Council, November 2005.
Analysis of the 2004 Presidential Election Exit Poll Discrepancies, with Dopp, Freeman, Joiner,
Lovegren, Mittledorf, Read, Sheehan, Simon, Singer, Velleman, and O’Dell. US Count Votes’ National
Election Data Archive Project, Park City, Utah. March 31, 2005, updated April 12, 2005. Major
contribution: Appendix B.
The Economic Impact of Wal-Mart: An Assessment of the Wal-Mart Store Proposed for Chicago’s West
Side, with Chirag Mehta and Joseph Persky. Center for Urban Economic Development, University of
Illinois at Chicago, March 2004.
“Stop the Free Trade Shipwreck: An Open Letter to John Kerry and John Edwards,” Democratic Left,
Summer 2004.
A County-Level Regional Cost-of-Living Index for Illinois, with Sarah Beth Coffey. Center for Urban
Economic Development, University of Illinois at Chicago, May 2004.
Illinois Business Tax Incidence, with Joseph Persky and Marc Doussard. Center for Urban Economic
Development, University of Illinois at Chicago, April 2004.
29
"Free Trade" and the Coming National and Global Economic Train Wreck: an Open Letter to John
Kerry,” New Ground, July-August 2004.
“Giving Bigger Tax Breaks to the Poor Could Really Stimulate the Economy,” with Joseph Persky,
Chicago Sun Times, June 7, 2003.
Raising and Maintaining the Value of the Illinois Minimum Wage: An Economic Impact Study, with
Marc Doussard, Sharon Mastracci, Joe Persky, and Nik Theodore. Center for Urban Economic
Development, University of Illinois at Chicago, March 2003.
“A Dinner in the Trenches of the Low Wage Economy,” New Ground, Fall, 2003
The High Cost of Living and Working in DuPage County: A Case for a Living Wage for the Suburban
Workforce, with Chris Schwartz, Joseph Persky, Patricia Nolan, and Nick Brunick. Center for Urban
Economic Development, University of Illinois at Chicago, December 2002.
A Self-Sufficiency Living Wage for Chicago, with Joseph Persky and Patrica Nolan. Center for Urban
Economic Development, University of Illinois at Chicago, October 2002.
Fulfilling the Promise of the Living Wage: A Review of Potential Improvements to the Chicago Living
Wage Ordinance, with Nicholas Brunick, Saura Sahu, Julie H. Hurwitz and Chirstina K. Salib. Center
for Urban Economic Development, University of Illinois at Chicago, August, 2002.
A Step in the Right Direction: An Analysis of Forcasted Costs and Benefits of the Chicago Living Wage
Ordinance, with Joseph Persky and Nicholas Brunick, Center for Urban Economic Development,
University of Illinois at Chicago, July 2002
The Economic and Fiscal Benefits of O’Hare Airport Expansion to Bensenville and Elk Grove Village,
Illinois, with Bill Lester, Joseph Persky, and Nik Theodore. Center for Urban Economic Development,
University of Illinois at Chicago, Chicago, IL, March, 2002.
Economic Effects of the Proposed Closing of the Lincoln Developmental Center, Center for Urban
Economic Development, University of Illinois at Chicago, Chicago, IL, October, 2001
Connecting to the Future: Greater Access, Services, and Competition in Telecommunications, co-author
with other core staff, report of the New York Telecommunications Exchange, Published by the Office of
Economic Development and Department of Public Service of New York State, Dec. 1993.
NON-PROFESSIONAL INTERESTS
Old-time and Bluegrass Fiddle
4th Place, Senior Division, 2005 DeKalb County, IL (Sandwich) Fair, Fiddle Contest
3rd Place, Senior Division, 2005 Butterprint Farm, Monee IL, Fiddle Contest
2nd Place, Senior Division, 2011 Butterprint Farm, Monee IL, Fiddle Contest
30
Exhibit G
1 B C D E F G H I J K L M N O P Q
2Exhibit G: Presidential Race Unadjusted Exit Poll (UEP) Calculations, Adjusted Exit Poll (AEP) Sample Size data, UEP/AEP Sample Size Percentage by State
3
4Clinton Trump C linton Trump Clinton Trump Cli nton Trump
5
State
Time Stamp
ET
Male Female Male Male Female Female
Vote Count Vote Count
UEP Sample
Size/ AEP
Sample Size
6AZ 8:25 PM 49% 51% 38.0% 51.0% 49.0% 43.0% 43.6% 46.9% 45.4% 49.5% 1729 1729 100%
7CA 10:39 PM 50% 50% 55.0% 34.0% 65.0% 29.0% 60.0% 31.5% 61.6% 32.8% 2282 2469 92%
8CO 8:56 PM 50% 50% 39.0% 45.0% 54.0% 38.0% 46.5% 41.5% 47.3% 44.4% 1335 1335 100%
9FL 7:42 PM 47% 53% 44.0% 49.0% 51.0% 44.0% 47.7% 46.4% 47.8% 49.1% 3941 3997 99%
10 GA 6:51 PM 45% 53% 38.0% 57.0% 54.0% 41.0% 45.7% 47.4% 45.6% 51.3% 2611 2767 94%
11 IA 9:47 PM 47% 53% 34.0% 57.0% 53.0% 40.0% 44.1% 48.0% 42.2% 51.8% 2941 2972 99%
12 IL 7:42 PM 49% 51% 49.0% 44.0% 58.0% 33.0% 53.6% 38.4% 55.4% 39.4% 594 916 65% x
13 IN 6:54 PM 49% 51% 35.0% 58.0% 44.0% 50.0% 39.6% 53.9% 37.9% 57.2% 1753 1817 96%
14 KY 6:54 PM 50% 50% 28.0% 69.0% 42.0% 54.0% 35.0% 61.5% 32.7% 62.5% 1070 1099 97%
15 ME 7:42 PM 48% 52% 45.0% 47.0% 57.0% 34.0% 51.2% 40.2% 47.9% 45.2% 1371 2128 64% x
16 MI 8:41 PM 48% 52% 40.0% 52.0% 53.0% 42.0% 46.76% 46.80% 47.3% 47.6% 2774 2812 99%
17 MN 8:56 PM 47% 53% 42.0% 49.0% 49.0% 43.0% 45.7% 45.8% 46.9% 45.4% 1515 1636 93%
18 MO 7:53 PM 47% 53% 37.0% 56.0% 48.0% 47.0% 42.8% 51.2% 38.0% 57.1% 1648 1941 85%
19 NC 7:19 PM 46% 54% 41.0% 53.0% 55.0% 41.0% 48.6% 46.5% 46.7% 50.5% 3967 4297 92%
20 NH 7:19 PM 47% 53% 42.0% 50.0% 56.0% 39.0% 49.4% 44.2% 47.6% 47.2% 1719 2800 61% x
21 NJ 7:42 PM 47% 53% 55.0% 39.0% 64.0% 33.0% 59.8% 35.8% 55.0% 41.8% 1037 1633 64% x
22 NM 8:56 PM 47% 53% 41.0% 41.0% 54.0% 35.0% 47.9% 37.8% 48.3% 40.0% 1515 2014 75% x
23 NV 9:47 PM 48% 52% 43.0% 47.0% 54.0% 39.0% 48.7% 42.8% 47.9% 45.5% 2418 2778 87%
24 NY 8:56 PM 44% 56% 48.0% 46.0% 62.0% 35.0% 55.8% 39.8% 58.8% 37.5% 1362 1466 93%
25 OH
7:18 PM 47% 53% 39.0% 54.0% 54.0% 41.0% 47.0% 47.1% 43.5% 52.1% 3190 3397 94%
26 OR 10:39 PM 48% 52% 45.0% 45.0% 56.0% 33.0% 50.7% 38.8% 51.7% 41.1% 1128 1128 100%
27 PA 7:52 PM 47% 53% 42.0% 54.0% 58.0% 39.0% 50.5% 46.1% 47.6% 48.8% 2613 2935 89%
28 SC 6:54 PM 47% 53% 38.0% 54.0% 47.0% 47.0% 42.8% 50.3% 40.8% 54.9% 867 895 97%
29 TX 8:56 PM 47% 53% 37.0% 56.0% 47.0% 48.0% 42.3% 51.8% 43.4% 52.6% 2610 2827 92%
30 UT 8:25 PM 47% 53% 26.0% 46.0% 38.0% 38.0% 32.4% 41.8% 27.8% 45.9% 870 1203 72% x
31 VA 6:54 PM 47% 53% 44.0% 49.0% 57.0% 38.0% 50.9% 43.2% 49.9% 45.0% 2866 2942 97%
32 WA 10:39 PM 48% 52% 44.0% 42.0% 58.0% 30.0% 51.3% 35.8% 54.4% 38.2% 1024 1024 100%
33 WI 8:56 PM 48% 52% 42.0% 49.0% 54.0% 40.0% 48.2% 44.3% 47.0% 47.8% 2981 3047 98%
Clinton Trump Clinton Trump Cli nton Trump Clinto n Trump Sample Size
Sources :
1. CNN UEP screen pri ntouts from Theodor e de Mateo Soare s and Jona than Simon.
2. CNN official vote coun t and sampl e size data downloade d Dec. 12 -14, 201 6.
31
Exhibit H
2 Exhibit H: Senatel Races Unadjusted Exit Poll (UEP) Calculations, Adjusted Exit Poll (AEP) Sample Size data, UEP/AEP Sample Size Percentage by State
3 Dem Rep Dem Rep Dem Rep Dem Rep
4
Time Stamp
ET
Male Female Male Male Female Fe male
Calculated
UEP
Calculated
UEP
Vote Count Vote Count
UEP Sample
Size
AEP Sample
Size
UEP Sampe
Size/AEP
Sample Size
%UEP
SS/AEP SS <
84%
5AZ 8:56 PM 49% 51% 39% 58% 46% 52% 42.6% 54.9% 54.9% 53.4% 1726 1726 100%
6CA 10:39 PM 48% 52% 56% 41% 59% 39% 57.6% 40.0% 41.1% 37.6% 1937 2097 92%
7CO 8:56 PM 49% 51% 51% 47% 57% 42% 54.1% 44.5% 41.1% 45.3% 1335 1335 100%
8FL 7:59 PM 46% 54% 44% 53% 49% 49% 46.7% 50.8% 41.1% 52.0% 3828 3835 100%
9GA 6:54 PM 46% 55% 33% 62% 48% 46% 41.6% 53.8% 41.1% 55.0% 2541 2696 94%
10 IA 9:47 PM 47% 53% 35% 64% 45% 54% 40.3% 58.7% 41.1% 60.2% 2844 2875 99%
11 IL 7:59 PM 49% 51% 54% 42% 62% 36% 58.1% 38.9% 41.1% 40.2% 507 820 62% X
12 IN 6:54 PM 49% 51% 40% 53% 48% 47% 44.1% 49.9% 41.1% 52.1% 1676 1738 96%
13 KY 6:54 PM 50% 50% 40% 60% 51% 49% 45.5% 54.5% 41.1% 57.3% 1037 1064 97%
14 MO
Simon
Downloa d
8:11 PM
47% 53% 47% 49% 57% 41% 52.3% 44.8% 41.1% 49.4% 1589 1881 84%
15 NC 7:19 PM 46% 54% 41% 55% 53% 42% 47.5% 48.0% 41.1% 51.1% 3904 4230 92%
16 NH 7:59 PM 48% 52% 43% 53% 57% 41% 50.3% 46.8% 41.1% 47.9% 2643 2741 96%
17 NV 9:47 PM 49% 51% 43% 50% 52% 41% 47.6% 45.4% 41.1% 44.7% 2390 2739 87%
18 NY 8:56 PM 45% 55% 61% 36% 76% 23% 69.3% 28.9% 41.1% 27.4% 1220 1317 93%
19 OH 7:19 PM 47% 53% 37% 61% 48% 51% 42.8% 55.7% 41.1% 58.3% 3107 3313 94%
20 OR 10:39 PM 48% 52% 60% 38% 67% 32% 63.6% 34.9% 41.1% 33.6% 1117 1117 100%
21 PA 7:59 PM 47% 53% 42% 54% 57% 41% 50.0% 47.1% 41.1% 48.9% 2535 2853 89%
22 SC 6:54 PM 47% 53% 38% 60% 44% 54% 41.2% 56.8% 41.1% 60.5% 820 840 98%
23 UT 9:47 PM 47% 53% 32% 62% 36% 59% 34.1% 60.4% 41.1% 68.0% 852 1168 73% X
24 WA 10:39 PM 48% 52% 57% 41% 67% 31% 62.2% 35.8% 41.1% 40.9% 1011 1011 100%
25 WI 8:56 PM 48% 52% 45% 52% 56% 42% 50.7% 46.8% 41.1% 50.2% 2970 3035 98%
Notes and Source s:
1) No exit poll da ta was a vailab le for states not include d in table
2) Vote count numbe rs from CNN downloa ded 12/12/20 16 9 PM CT
3) Exit poll sha res from CNN scree n shots provide d by Theodore de Mace do Soares exc ept for MO provided by Jona than Simon.
32
Exhibit I
1 A B C D E F G H I J
2 Exhibit I: 2016 Presidential Election "Red Shift" or Exit Polls versus Vote Count Margins
3
4 State Sample Size ClintonEP TrumpEP
Exit Poll
Margin
(+ Clinton,
- Trump)
ClintonVC TrumpVC
Vote Count
Margin
(+ Clinton,
- Trump)
VC Margin
minus Exit
Poll Margin
(+Clinton, -
Trump "Red
Shift")
Odds of 22 out
of 28 negative
"red shifts" if
probablity of
one negative
red shift is 0.5
5NJ 1037 59.8% 35.8% 24.0% 55.0% 41.8% 13.2% -10.8% 713
6MO 1648 42.8% 51.2% -8.4% 38.0% 57.1% -19.1% -10.7% 376,740
7UT 870 32.4% 41.8% -9.4% 27.8% 45.9% -18.1% -8.7% 268,435,456
8OH 3190 47.0% 47.1% -0.2% 43.5% 52.1% -8.6% -8.4% 0.14%
9ME 1371 51.2% 40.2% 11.0% 47.9% 45.2% 2.7% -8.3%
10 SC 867 42.8% 50.3% -7.5% 40.8% 54.9% -14.1% -6.6%
11 NC 3967 48.6% 46.5% 2.0% 46.7% 50.5% -3.8% -5.8%
12 IA 2941 44.1% 48.0% -3.9% 42.2% 51.8% -9.6% -5.7%
13 PA 2613 50.5% 46.1% 4.4% 47.6% 48.8% -1.2% -5.6%
14 IN 1753 39.6% 53.9% -14.3% 37.9% 57.2% -19.3% -5.0%
15 NH 1719 49.4% 44.2% 5.3% 47.6% 47.2% 0.4% -4.9%
16 WI 2981 48.2% 44.3% 3.9% 47.0% 47.8% -0.8% -4.7%
17 GA 2611 45.7% 47.4% -1.7% 45.6% 51.3% -5.7% -4.0%
18 NV 2418 48.7% 42.8% 5.9% 47.9% 45.5% 2.4% -3.5%
19 KY 1070 35.0% 61.5% -26.5% 32.7% 62.5% -29.8% -3.3%
20 VA 2866 50.9% 43.2% 7.7% 49.9% 45.0% 4.9% -2.8%
21 FL 3941 47.7% 46.4% 1.4% 47.8% 49.1% -1.3% -2.7%
22 CO 1335 46.5% 41.5% 5.0% 47.3% 44.4% 2.9% -2.1%
23 NM 1515 47.9% 37.8% 10.1% 48.3% 40.0% 8.3% -1.8%
24 OR 1128 50.7% 38.8% 12.0% 51.7% 41.1% 10.6% -1.4%
25 AZ 1729 43.6% 46.9% -3.3% 45.4% 49.5% -4.1% -0.8%
26 MI 2774 46.8% 46.8% 0.0% 47.3% 47.6% -0.3% -0.3%
27 TX 2610 42.3% 51.8% -9.5% 43.4% 52.6% -9.2% 0.3%
28 CA 2282 60.0% 31.5% 28.5% 61.6% 32.8% 28.8% 0.3%
29 WA 1024 51.3% 35.8% 15.5% 54.4% 38.2% 16.2% 0.7%
30 IL 594 53.6% 38.4% 15.2% 55.4% 39.4% 16.0% 0.8%
31 MN 1515 45.7% 45.8% -0.1% 46.9% 45.4% 1.5% 1.6%
32 NY 1362 55.8% 39.8% 16.0% 58.8% 37.5% 21.3% 5.3%
Notes and Sources:
1) No exit poll data was avai lable for states not included in ta ble
2) Vote count numbers from CNN downloade d 12/12/2016 9 PM CT
3) Exit poll s hares from CNN scre en shots provided by Theodore de Macedo Soa res .
33
Exhibit P
1 A B C D E F G H I J K L M
2 Exhibit P: 2016 Presidenti al Election Cli nton Exit Polls versus Vote Count
3
4 ClintonEP Cli ntonVC
Clinton VC
reducti on
relative to
exit poll (+
indicates VC
share < EP
share for
Clinton)
Sample Size
Random
Sample SD
assuming
Clinton exit
poll
population
proportion
Random
Sample with
30%
"Cluster
Factor"
added to
Clinton SD
Estimate
UEP - VC
Discrepancy
Measured in
Z-Score, or
SD's from
Clinton UEP
Share
One tail P
value:
Probabilily
of Clinton
VC share if
EP is True
share
Odds based
on Clinton
one tail
Probablility:
one in x
chance
95%
Confidence
Interval (CI)
Low value
for Clinton
VC deviation
from EP
95%
Confidence
Interval (CI)
High value
for Clinton
VC deviation
from EP
Odds of Clinton
VC share being
smaller than EP
share 16 out of
28 times
5MO (1648) 42.8% 38.0% 4.8% 1648 1.22% 1.6% 3.05 0.1152% 868.4 39.7% 45 .9% 9
6OH (3190) 47.0% 43.5% 3.5% 3190 0.88% 1.1% 3.00 0.1335% 749.1 44.7% 49.2% 30,421,755
7NJ (1590) 59.8% 55.0% 4.8% 1037 1.52% 2.0% 2.41 0.7985% 125.2 55.9% 63.6% 268,435,456
8PA (2613) 50. 5% 47.6 % 2.9 % 2613 0.98% 1.3% 2.27 1.1756% 85.1 48.0% 53.0% 11.3%
9UT (1171) 32.4% 27.8% 4.6% 870 1.59% 2.1% 2.21 1.3503% 74.1 28.3% 36.4%
10 ME (1371) 51.2% 47.9% 3.3% 1371 1.35% 1.8% 1.90 2.8507% 35.1 47.8% 54.7%
11 NC (3967) 48.6% 46.7% 1.9% 3967 0.79% 1.0% 1.80 3.5689% 28.0 46.5% 50.6%
12 IA (2941) 44.1% 42.2% 1.9% 2941 0.92% 1.2% 1.57 5.8060% 17.2 41.7% 46.4%
13 KY (1070) 35.0% 32.7% 2.3% 1070 1.46% 1.9% 1.21 11.2498% 8.9 31.3% 38.7%
14 NH (2702) 49.4% 47.6% 1.8% 1719 1.21% 1.6% 1.16 87.7175% 1.1 46.3% 52.5%
15 IN (1753) 39.6% 37.9% 1.7% 1753 1.17% 1.5% 1.11 13.2859% 7.5 36.6% 42.6%
16 WI (2981) 48.2% 47.0% 1.2% 2981 0.92% 1.2% 1.04 14.8655% 6.7 45.9% 50.6%
17 SC (876) 4 2.8% 40 .8% 2 .0% 867 1.68% 2.2% 0.90 18.3559% 5.4 38.5% 47.1%
18 VA (2866) 50.9% 49.9% 1.0% 2866 0.93% 1.2% 0.82 20.7391% 4.8 48.5% 53.3%
19 NV (2418) 48.7% 47.9% 0.8% 2418 1.02% 1.3% 0.62 26.7451% 3.7 46.1% 51.3%
20 GA (2611) 45.7% 45.6% 0.1% 2611 0.97% 1.3% 0.09 46.2284% 2.2 43.2% 48.2%
21 FL (3941) 47 .7% 47. 8% -0.1 % 3941 0.80% 1.0% -0.09 46.5330% 2.1 45.7% 49.7%
22 NM (1948) 47.9% 48.3 % -0.4% 1515 1.28% 1.7% -0.25 40.2944% 2.5 44.6% 51.2%
23 MI (2774) 46.8% 47.3 % -0.5% 2774 0.95% 1.2% -0.44 33.0520% 3.0 44.3% 49.2%
24 CO (1335) 46. 5% 47.3 % -0.8% 1335 1.37% 1.8% -0.45 32.6067% 3.1 43.0% 50.0%
25 OR (1128) 50. 7% 51.7% -1.0% 1128 1.49% 1.9% -0.51 30.6280% 3.3 46.9% 54.5%
26 IL (802) 53.6% 55.4% -1.8% 594 2.05% 2.7% -0.68 75.1883% 1.3 48.4% 58.8%
27 MN (1583) 45.7% 46.9 % -1.2% 1515 1.28% 1.7% -0.72 23.7234% 4.2 42.4% 49.0%
28 TX (2610) 4 2.3% 43 .4% -1. 1% 2610 0.97% 1.3% -0.88 19.0785% 5.2 39.8% 44.8%
29 AZ (1729) 43 .6% 45.4 % -1.8 % 1729 1.19% 1.6% -1.15 12.4137% 8.1 40.6% 46.6%
30 CA (2282) 60.0% 61.6 % -1.6% 2282 1.03% 1.3% -1.20 11.5044% 8.7 57.4% 62.6%
31 WA (1024) 51.3 % 54.4% -3.1% 1024 1.56% 2.0% -1.54 6.2207% 16.1 47.3% 55.3%
32 NY (1362) 5 5.8% 58 .8% -3 .0% 1362 1.35% 1.7% -1.69 4.5305% 22.1 52.4% 59.3%
Notes and Sourc es:
1) No exit poll data was ava ilable for sta tes not incl uded in tabl e
2) Vote count numb ers from CNN downloade d 12/12/2016 9 PM CT
3) Exit poll sh ares from CNN scre en shots provi ded by Theodore de Mac edo Soares
34
Exhibit Q
Figure Credit: Greg Kilcup and Peter Peckarsky
35
Exhibit R
1 A B C D E F G H I J K L M
2Exhibit R: 2016 Preside ntial Electi on Trump Exit Polls versus Vote Count
3Calculations off of Trump EP and VC Shares
4TrumpEP TrumpVC
Trump VC
reduct ion
relative to
exit poll (-
indicate s VC
share > EP
share for
Trump)
Sample Size
Random
Sample SD
assuming
Trump exit
poll
population
proportion
Random
Sample with
30%
"Cluster
Factor"
added to
Trump
Estimate
UEP - VC
Discrepancy
Measured in
Z-Score, or
SD's from
Trump UEP
Share
One tail P
value:
Probabilily
of Trump VC
share if EP is
True share
Odds based
on Trump
one tail
Probablilit y:
one in x
chance
95%
Confidence
Interval (CI)
Low value
for Trump
VC deviation
from EP
95%
Confidence
Interval (CI)
High value
for Trump
VC deviation
from EP
Odds of Trump VC
share being
larger than EP
share 26 out of
28 time s
5OH 47.1% 52.1% -5.0% 3190 0.88% 1.1% -4.34 0.0007% 142,424 44.9% 49.4% 710,147
6NC 46.5% 50.5% -4.0% 3967 0.79% 1.0% -3.87 0.0055% 18,073 44.5% 48.5% 378
7 MO 51.2 % 57 .1% -5.9% 1648 1.23% 1.6% -3.67 0.0123% 8,156 48.1% 54.4% 268,435,456
8IA 48.0% 51.8% -3.8% 2941 0.92% 1.2% -3.18 0.0733% 1,364 45.6% 50.3% 0.0001%
9 NJ 35.8% 41.8% -6.0% 1037 1.49% 1.9% -3.09 0.1003% 997 32.0% 39.6%
10 GA 47.4% 51.3% -3.9% 2611 0.98% 1.3% -3.09 0.1015% 985 44.9% 49.9%
11 WI 44.3% 47.8% -3.5% 2981 0.91% 1.2% -2.94 0.1630% 614 42.0% 46.6%
12 ME 40.2% 45.2% -5.0% 1371 1.32% 1.7% -2.88 0.1983% 504 36.9% 43.6%
13 FL 46.4% 49.1% -2.8 % 3941 0.79% 1.0% -2.66 0.3872% 258 44.3% 48.4%
14 PA 46.1 % 48 .8% -2.8% 2613 0.98% 1.3% -2.17 1.5024% 67 43.6% 48.5%
15 IN 53.9% 57.2% -3.3% 1753 1.19% 1.5% -2.12 1.7033% 59 50.9% 57.0%
16 SC 50.3% 54.9% -4.6% 867 1.70% 2.2% -2.09 1.8383% 54 46.0% 54.6%
17 NV 42.8% 45.5% -2.7% 2418 1.01% 1.3% -2.03 2.1012% 48 40.3% 45.4%
18 NH 44.2% 47.2% -3.0% 1719 1.20% 1.6% -1.95 2.5828% 39 41.1% 47.2%
19 UT 41.8% 45.9 % -4 .1% 870 1.67% 2.2% -1.90 2.8410% 35 37.5% 46.0%
20 CO 41.5% 44.4% -2.9% 1335 1.35% 1.8% -1.65 4.9041% 20 38.1% 44.9%
21 AZ 46 .9% 49.5 % -2. 6% 1729 1.20% 1.6% -1.65 4.9105% 20 43.9% 50.0%
22 VA 43.2% 45.0% -1.8% 2866 0.93% 1.2% -1.52 6.4070% 16 40.8% 45.5%
23 NM 3 7.8% 40. 0% -2.2% 1515 1.25% 1.6% -1.35 8.9157% 11 34.6% 41.0%
24 WA 35.8% 38.2% -2.4% 1024 1.50% 1.9% -1.25 10.5080% 10 31.9% 39.6%
25 OR 38.8% 41.1% -2.3% 1128 1.45% 1.9% -1.24 10.7331% 9 35.1% 42.5%
26 CA 31.5% 32.8% -1.3% 2282 0.97% 1.3% -1.03 15.1884% 7 29.0% 34.0%
27 TX 51.8% 52.6% -0.8% 2610 0.98% 1.3% -0.66 25.4426% 4 49.3% 54.3%
28 MI 46.8% 47.6% -0.8% 2774 0.95% 1.2% -0.65 25.7987% 4 44.4% 49.2%
29 KY 61.5% 62.5% -1.0% 1070 1.49% 1.9% -0.52 30.2541% 3 57.7% 65.3%
30 IL 38.4% 39.4% -1.0% 594 2.00% 2.6% -0.39 34.8510% 3 33.3% 43.5%
31 MN 45.8% 45.4% 0.4% 1515 1.28% 1.7% 0.25 40.0371% 2 42.6% 49.1%
32 NY 39.8% 37.5 % 2.3% 1362 1.33% 1.7% 1.36 8.7407% 11 36.5% 43.2%
Notes and Sourc es:
1) No exit poll data was ava ilable for stat es not incl uded in tabl e
2) Vote count numb ers from CNN download ed 12/12/201 6 9 PM CT
3) Exit poll s hares from CNN scre en shots pro vided by Theodore de Macedo Soa res
36
Exhibit S
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
37
Exhibit T
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
38
Exhibit U
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
39
Exhibit V
A B C D E F G H I J
2 Exhibit V: 2016 Senat e Rac es "Red Shift" or Exit Polls versus Vote Count Margi ns
3 1
4 Sampl e Size DemEP RepEP
Exit Poll
Margin (+
Dem, - Rep)
DemVC RepVC
Vote Count
Margin (+
Dem,- Rep)
Dem VC
reduction
relati ve to
exit poll
"Red Shift" (+
indi cates VC
sha re < EP
sha re for
Dem)
Odds of 19
out of 21
positive red
shi fts if
proba bility
of one red
shi ft is 0.5
5OR 1117 63.6% 34.9% 28.8% 56.7% 33.6% 23.1% 6.9% 9,986
6UT 852 34.1% 60.4% -26.3% 27.4% 68.0% -40.6% 6.7%
7MO 1589 52.3% 44.8% 7.5% 46.2% 49.4% -3.2% 6.1% 210
8OH 3107 42.8% 55.7% -12.9% 36.9% 58.3% -21.4% 5.9% 2097152
9CO 1335 54.1% 44.5% 9.6% 49.2% 45.3% 3.9% 4.9% 0.01%
10 IA 2844 40.3% 58.7% -18.4% 35.7% 60.2% -24.5% 4.6%
11 SC 820 41.2% 56.8% -15.6% 37.0% 60.5% -23.5% 4.2%
12 WI 2970 50.7% 46.8% 3.9% 46.8% 50.2% -3.4% 3.9%
13 IL 507 58.1% 38.9% 19.1% 54.4% 40.2% 14.2% 3.7%
14 WA 1011 62.2% 35.8% 26.4% 59.1% 40.9% 18.2% 3.1%
15 KY 1037 45.5% 54.5% -9.0% 42.7% 57.3% -14.6% 2.8%
16 PA 2535 50.0% 47.1% 2.8% 47.2% 48.9% -1.7% 2.8%
17 FL 3828 46.7% 50.8% -4.1% 44.3% 52.0% -7.7% 2.4%
18 NH 2643 50.3% 46.8% 3.5% 48.0% 47.9% 0.1% 2.3%
19 NC 3904 47.5% 48.0% -0.5% 45.3% 51.1% -5.8% 2.2%
20 IN 1676 44.1% 49.9% -5.9% 42.2% 52.1% -9.9% 1.9%
21 AZ 1726 42.6% 54.9% -12.4% 41.1% 53.4% -12.3% 1.5%
22 GA 2541 41.6% 53.8% -12.2% 40.8% 55.0% -14.2% 0.8%
23 NV 2390 47.6% 45.4% 2.2% 47.1% 44.7% 2.4% 0.5%
24 NY 1220 69.3% 28.9% 40.4% 70.4% 27.4% 43.0% -1.1%
25 CA 1937 57.6% 40.0% 17.6% 62.4% 37.6% 24.8% -4.8%
Notes and Sources:
1) No exit poll data was availa ble for stat es not included in tabl e
2) Vote count numbers from CNN downloade d 12/12/2016 9 PM CT
3) Exit poll shares from CNN scre en shots provided by Theodore de Macedo Soa res except for MO supplied by Jonathan Simon.
40
Exhibit W
1A B C D E F G H I J K L M
2Exhibit W: 2016 Senate Races Democratic Candidate Exit Polls versus Vote Count
3
4State Sample Size DemEP DemVC
Dem VC
reduct ion
relative to
exit poll (+
indicate s VC
share < EP
share for
Dem)
Random
Sample SD
assuming
Senate Dem
exit poll
population
proportion
Random
Sample with
30%
"Cluster
Factor"
added to
Dem SD
Estimate
UEP - VC
Discrepancy
Measured in
Z-Score, or
SD's from
Dem UEP
Share
One tail P
value:
Probabilily
of Dem VC
share if EP is
True share
Odds based
on Dem one
tail
Probablilit y:
one in x
chance
95%
Confidence
Interval (CI)
Low value
for Dem VC
deviation
from EP
95%
Confidence
Interval (CI)
High value
for Dem VC
deviation
from EP
Odds of Dem
VC share
being
smaller than
EP share 19
out of 21
times
5OH 3107 42.8% 36.9% 5.9% 0.9% 1.2% 5.14 0.00% 7,216,083 40.6% 45.1% 9,986
6IA 2844 40.3% 35.7% 4.6% 0.9% 1.2% 3.85 0.01% 16,737 38.0% 42.6%
7MO 1589 52.3% 46.2% 6.1% 1.3% 1.6% 3.74 0.01% 11,082 49.1% 55.5% 210
8OR 1117 63.6% 56.7% 6.9% 1.4% 1.9% 3.71 0.01% 9,615 60.0% 67.3% 2,097,152
9WI 2970 50.7% 46.8% 3.9% 0.9% 1.2% 3.29 0.05% 1,975 48.4% 53.1% 0.01%
10 UT 852 34.1% 27.4% 6.7% 1.6% 2.1% 3.18 0.07% 1,370 30.0% 38.3%
11 CO 1335 54.1% 49.2% 4.9% 1.4% 1.8% 2.74 0.31% 326 50.6% 57.5%
12 FL 3828 46.7% 44.3% 2.4% 0.8% 1.0% 2.29 1.10% 91 44.6% 48.8%
13 PA 2535 50.0% 47.2% 2.8% 1.0% 1.3% 2.13 1.66% 60 47.4% 52.5%
14 NC 3904 47.5% 45.3% 2.2% 0.8% 1.0% 2.1 1.79% 56 45.4% 49.5%
15 SC 820 41.2% 37.0% 4.2% 1.7% 2.2% 1.87 3.07% 33 36.8% 45.6%
16 NH 2643 50.3% 48.0% 2.3% 1.0% 1.3% 1.8 3.57% 28 47.8% 52.8%
17 WA 1011 62.2% 59.1% 3.1% 1.5% 2.0% 1.56 5.89% 17 58.3% 66.1%
18 KY 1037 45.5% 42.7% 2.8% 1.6% 2.0% 1.39 8.18% 12 41.6% 49.4%
19 IL 507 58.1% 54.4% 3.7% 2.2% 2.8% 1.29 9.82% 10 52.5% 63.7%
20 IN 1676 44.1% 42.2% 1.9% 1.2% 1.6% 1.19 11.65% 9 41.0% 47.2%
21 AZ 1726 42.6% 41.1% 1.5% 1.2% 1.5% 0.95 17.10% 6 39.5% 45.6%
22 GA 2541 41.6% 40.8% 0.8% 1.0% 1.3% 0.61 26.97% 4 -343.8% 714.5%
23 NV 2390 47.6% 47.1% 0.5% 1.0% 1.3% 0.37 35.61% 3 45.0% 50.2%
24 NY 1220 69.3% 70.4% -1.1% 1.3% 1.7% -0.67 25.16% 4 65.9% 72.6%
25 CA 1937 57.6% 62.4% -4.8% 1.1% 1.5% -3.32 0.05% 2,184 54.7% 60.4%
Notes and Sourc es:
1) No exit poll data was avai lable for states not includ ed in table
2) Vote count num bers from CNN downloade d 12/12 /2016 9 PM CT
3) Exit poll s hare s from CNN scre en shots provi ded by Theodo re de Mace do Soare s except for MO suppli ed by Jonatha n Simon.
41
Exhibit X
1 A B C D E F G H I J K L M
2 Exhibit X: 201 6 Senate Races Republican Candidate Exit Polls versus Vote Count
3 State Sample Size RepEP RepV C
Rep VC
reduct ion
relative to
exit poll (+
indicate s VC
share < EP
share for
Rep)
Random
Sample SD
assuming
Senate Rep
exit poll
population
proportion
Random
Sample with
30%
"Cluster
Factor"
added to
Rep SD
Estimate
UEP - VC
Discrepancy
Measured in
Z-Score, or
SD's from
Rep UEP
Share
95%
Confidence
Interval (CI)
Low value
for Rep VC
deviation
from EP
95%
Confidence
Interval (CI)
High value
for Rep VC
deviation
from EP
One tail P
value:
Probabilily
of Rep VC
share if EP is
True share
Odds based
on Rep one
tail
Probablilit y:
one in x
chance
Odds of Rep
VC share
being larger
than EP
share 16 out
of 21 times
4UT 852 60.4% 68.0% -7.6% 1.7% 2.2% -3.48 56.1% 64.7% 0.02% 4,060
NC 3904 48.0% 51.1% -3.1% 0.8% 1.0% -3 45.9% 50.0% 0.13% 745 103
6MO 1589 44.8% 49.4% -4.6% 1.3% 1.6% -2.86 41.6% 47.9% 0.21% 474 20,349
7WI 2970 46.8% 50.2% -3.4% 0.9% 1.2% -2.86 44.5% 49.1% 0.21% 467 2,097,152
8WA 1011 35.8% 40.9% -5.1% 1.5% 2.0% -2.6 32.0% 39.6% 0.46% 216 0.97%
9OH 3107 55.7% 58.3% -2.6% 0.9% 1.2% -2.24 53.4% 58.0% 1.24% 81
10 SC 820 56.8% 60.5% -3.7% 1.7% 2.2% -1.64 52.4% 61.2% 5.09% 20
11 KY 1037 54.5% 57.3% -2.8% 1.6% 2.0% -1.39 50.6% 58.4% 8.18% 12
12 PA 2535 47.1% 48.9% -1.8% 1.0% 1.3% -1.39 44.6% 49.6% 8.24% 12
13 IN 1676 49.9% 52.1% -2.2% 1.2% 1.6% -1.36 46.8% 53.1% 91.32% 1
14 IA 2844 58.7% 60.2% -1.5% 0.9% 1.2% -1.25 56.3% 61.1% 10.57% 9
15 FL 3828 50.8% 52.0% -1.2% 0.8% 1.1% -1.1 48.8% 52.9% 13.47% 7
16 GA 2541 53.8% 55.0% -1.2% 1.0% 1.3% -0.92 51.3% 56.3% 17.94% 6
17 NH 2643 46.8% 47.9% -1.1% 1.0% 1.3% -0.9 44.3% 49.2% 18.31% 5
18 CO 1335 44.5% 45.3% -0.9% 1.4% 1.8% -0.48 41.0% 47.9% 31.53% 3
19 IL 507 38.9% 40.2% -1.3% 2.2% 2.8% -0.45 33.4% 44.5% 32.72% 3
20 NV 2390 45.4% 44.7% 0.7% 1.0% 1.3% 0.54 42.8% 48.0% 29.59% 3
21 OR 1117 34.9% 33.6% 1.3% 1.4% 1.9% 0.69 31.2% 38.5% 24.49% 4
22 NY 1220 28.9% 27.4% 1.5% 1.3% 1.7% 0.86 25.5% 32.2% 19.49% 5
23 AZ 1726 54.9% 53.4% 1.5% 1.2% 1.6% 0.99 51.9% 58.0% 16.13% 6
24 CA 1937 40.0% 37.6% 2.4% 1.1% 1.4% 1.63 37.1% 42.8% 5.14% 19
Notes and Sourc es:
1) No exit poll data was ava ilable for stat es not incl uded in tabl e
2) Vote count num bers from CNN download ed 12/12/201 6 at 9 PM CT.
3) Exit poll s hares from from CNN scree n shots provi ded by Theodore de Mate o Soares ex cept for MO suppli ed by Jonatha n Simon.
42
Exhibit Y
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
43
Exhibit Z
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
44
Exhibit AA
Figure Credit: Greg Kilcup, Peter Peckarsky, and Ron Baiman
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ResearchGate has not been able to resolve any references for this publication.