Richard Charnin
Dec.14, 2017
My Books
Trump Won the True Vote
77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll
Reclaiming Science: The JFK Conspiracy
LINKS TO POSTS
2017 Alabama Senate True Vote Model
Did 75% of Clinton and 45% of Trump voters return in 2017?
That’s what was required to match the recorded vote.
In 2016, Trump won the state by 589,000 votes: 62.08-34.36%.
There were 2,123,372 total recorded votes.
In the Senate election, Jones won by 20,715 votes: 49.9-48.4% .
There were 1,344,438 total recorded votes.
Returning 2016 Voter Turnout
Clinton Trump margin Winner
75%.. 45%.. -20,715 Recorded Jones
70%.. 50%.. 70,577 Moore (Base Case scenario)
65%.. 55%.. 161,011 Moore
60%.. 60%.. 251,446 Moore
True Vote Model: Use identical exit poll recorded vote shares of returning and new voters, but adjust 2016 voter turnout to 70% Clinton and 50% Trump.
Sensitivity Analysis
Base case scenario: Moore has 4% of Clinton and 92% of Trump voters.
Moore wins by 51.8-46.5% (71,000 votes)
Worst case scenario: Moore has 2% of Clinton and 90% of Trump voters.
Moore wins by 50.1-48.3% (24,000 votes)
Best case scenario: Moore has 6% of Clinton and 94% of Trump voters.
Moore wins by 53.5-44.8% (117,000 votes)
https://www.scribd.com/document/367999441/Moore-Voter-Fraud-Complaint
https://www.washingtonpost.com/graphics/2017/politics/alabama-exit-polls/?utm_term=.8906eed4d60f
Recorded vote match: Returning voter turnout: Clinton 75%; Trump 45%
| 2016 | 2017 | Returning | Vote | |||
| Actual | Turnout | Voters | Pct | Jones | Moore | Other |
| Clinton | 75% | 541,761 | 40.3% | 96% | 4% | 0% |
| Trump | 45% | 587,210 | 43.7% | 7% | 92% | 1% |
| Other | 60% | 44,927 | 3.3% | 47% | 24% | 29% |
| DNV (new) | – | 170,539 | 12.7% | 52% | 46% | 2% |
| Recorded | 1,344,438 | 100% | 49.9% | 48.4% | 1.7% | |
| Votes | 671,151 | 650,436 | 22,811 | |||
2016 Returning voter turnout: Clinton 70%; Trump 50%
| True Vote (est.) | 2017 | Returning | ||||
| 2016 | Turnout | Voters | Pct | Jones | Moore | Other |
| Clinton | 70% | 505,644 | 37.6% | 96% | 4% | 0% |
| Trump | 50% | 652,456 | 48.5% | 7% | 92% | 1% |
| Other | 60% | 44,927 | 3.3% | 47% | 24% | 29% |
| DNV(new) | – | 141,411 | 10.5% | 52% | 46% | 2% |
| Total | 63.32% | 1,344,438 | True Vote | 46.5% | 51.8% | 1.7% |
| Votes | 1,344,438 | 625,740 | 696,317 | 22,382 | ||
|
True Vote
|
Sensitivity Analysis | ||||
|
Moore% Clinton
|
|||||
| Moore | 2% | 3% | 4% | 5% | 6% |
| % Trump | Moore | ||||
| 94% | 52.0% | 52.4% | 52.8% | 53.1% | 53.5% |
| 93% | 51.5% | 51.9% | 52.3% | 52.7% | 53.0% |
| 92% | 51.0% | 51.4% | 51.8% | 52.2% | 52.5% |
| 91% | 50.6% | 50.9% | 51.3% | 51.7% | 52.1% |
| 90% | 50.1% | 50.4% | 50.8% | 51.2% | 51.6% |
| Jones | |||||
| 94% | 46.3% | 45.9% | 45.6% | 45.2% | 44.8% |
| 93% | 46.8% | 46.4% | 46.1% | 45.7% | 45.3% |
| 92% | 47.3% | 46.9% | 46.5% | 46.2% | 45.8% |
| 91% | 47.8% | 47.4% | 47.0% | 46.7% | 46.3% |
| 90% | 48.3% | 47.9% | 47.5% | 47.1% | 46.8% |
| Moore margin | |||||
| 94% | 5.7% | 6.4% | 7.2% | 7.9% | 8.7% |
| 93% | 4.7% | 5.5% | 6.2% | 7.0% | 7.7% |
| 92% | 3.7% | 4.5% | 5.2% | 6.0% | 6.8% |
| 91% | 2.8% | 3.5% | 4.3% | 5.0% | 5.8% |
| 90% | 1.8% | 2.6% | 3.3% | 4.1% | 4.8% |
|
Moore margin (000)
|
|||||
| 94% | 76 | 87 | 97 | 107 | 117 |
| 93% | 63 | 74 | 84 | 94 | 104 |
| 92% | 50 | 60 | 71 | 81 | 91 |
| 91% | 37 | 47 | 58 | 68 | 78 |
| 90% | 24 | 34 | 44 | 55 | 65 |
Richard Charnin's Blog 
Richard Charnin
December 28, 2017 at 10:10 am
You call sensitivity analysis prejudicial? Looking at various scenarios is prejudicial? Let’s see an example of your modeling background.
mitch
December 28, 2017 at 2:13 pm
How do you account for non-voters in 2016? Political economy theory implies that voting probability increases as a race becomes closer (and I believe evidence shows that the effect is stronger on the losing side). Isn’t it true that many people would stay home in Nov. 2016 knowing their vote wouldn’t matter but come out in 2017 as the race got closer?
Richard Charnin
December 30, 2017 at 7:20 pm
I use a sensitivity analysis based on various 2016 voter turnout rates. The vote shares are identical the the exit poll which was forced to match the bogus recorded vote.