Previous post on Theresa Long.

Mathew Crawford has done a deep investigation of the DMED "glitch" and has criticized Long and attorney Thomas Renz for exaggerations. He's pleading for an investigation of contractor Unissant.

Mathew Crawford has done a deep investigation of the DMED "glitch" and has criticized attorney Thomas Renz for exaggerations.

Thanks. I'm using the t-test from stats 101, but doing 15650 of them and adjusting for the big number of tests so we control the false discovery rate. The third file has the results in a .csv file sorted by significance and the steps above are how I got there from the raw data in the first two files.

Previous discussion on the German KBV data here and here, including comments by the original Substack author AMidwesternDoctor.

For anons wanting to look at the original data, here are three files (this link expires in 12 days; plz lemme know if it doesn't work). The .xlsx file has the raw data, the .txt file is the German ICD codes, and the .csv file contains the results, sorted in descending order by -log10(p-value).

Analysis steps:

1. Stack the data and add together the code and nocode numbers for each quarter and ICD-10 code, and then take log2 of this sum to standardize it.

2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).

3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions

4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.

5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).

6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

Things I wish Olsen and Blehm would have pursued and argued more aggressively:

1. The fact that a large percentage of voters waited until election day is direct evidence of distrust in the current system. The public suspects the MC system is fraudulent and a re-do under a special master with strict rules would help settle this and rebuild trust (find legal precedents). Defense should have no problem with a re-do since Hobbs presumably won by a comfortable margin. Carefully but firmly show the vital importance of this case.

2. 1/3 - 2/3 of centers misprinting 19" images on 20" paper is probabilistically way beyond anything that could have happened accidentally, meaning somebody intentionally made the changes. Were the printers or the ballot image laptops connected to the internet the night before election day? How easily could they have been manipulated? + follow up questions to try and discover the culprit(s).

3. Right at the end of his testimony yesterday, Richer categorically denied being involved in any PAC against Lake. Cannot this be shown fairly easily and then bring him up for perjury and totally discredit him and his team?

4. Jarrett says they are still conducting a root cause analysis and admitted shrink-to-fit at 3 centers. Ask how many man-hours are being devoted to this each day and why is it taking so long? Build a case for a slow walk cover up.

5. Diminish perceived strength of bipartisan reviewers in the chain of custody by mentioning the Republican party has clear splits over candidates like Lake and Trump, especially in Arizona. How are the bipartisan reviewers selected?

6. Obtain a spreadsheet of numbers from each voting center, including precise totals of the number of voters who showed up each day (especially election day), number of ballots printed, exact counts from each tabulator, drawer 3, spoiled ballots, provisional ballots, dropped off early ballots, etc. When a tabulator fails to read a ballot, is that counted somehow? Is the lifetime total counter incremented? Obtain all data from each tabulator memory card. Has chain of custody on these memory cards been maintained? Contact all voting location supervisors to verify the numbers. Examine all numbers carefully and drill down on discrepancies. How many and what percentages were ultimately adjudicated from each category of ballot?

7. Show a well-constructed and easily visible geographical heatmap map of all vote centers overlayed with proportion of registered Republican voters. Somebody (Baris?) reported seeing such a map in MC headquarters--obtain it as evidence and ask pointed questions.

8. Create a visual aid of each of the chains of custody and highlight every link that is questionable, especially chains that involves Runbeck. Are the packets or ballots in the "vault" under 24/7 bipartisan surveillance? In which places are "seals" easily broken and replaced?

9. Richard Baris' rambling and talkative style appeared to flummox the defense. But be ready in closing to very clearly state and support his estimated range of disenfranchised voters.

10. In the hearing on Monday, make absolutely sure laches is not applied to signature verification (Count 3) by claiming the defendants did not follow the existing procedure as per testimony of the level 1 witnesses.

11. Also on Monday, tie the First Amendment and Twitter evidence to Hobbs' duty to run a "free and fair" election in her role as Secretary of State, and really hammer home the apparent impropriety of her running her own election.

Previous discussion on the German KBV data

Here are three files (this link expires in 25 days; plz lemme know if it doesn't work). The .xlsx file has the raw data, the .txt file is the German ICD codes, and the .csv file contains the results, sorted in descending order by -log10(p-value).

If anyone knows what "code" and "nocode" means in the data, please reply.

Analysis steps:

1. Stack the data and add together the code and nocode numbers for each quarter and ICD-10 code, and then take log2 of this sum to standardize it.

2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).

3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions

4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.

5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).

6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

I don't understand this one. Laches say that an election procedure must be contested before the election. But wasn't Lake's claim that the existing signature verification procedure was not properly followed in this election?

Some sauce to the contrary, see cases from 2021: https://election-integrity.info/2020_Election_Cases.htm But granted, there really should be a lot more in every battleground state.

Thanks for the request. Here are three files (this link expires in 30 days; plz lemme know if it doesn't work). The .xlsx file has the raw data, the .txt file is the German ICD codes, and the .csv file contains the results, sorted in descending order by -log10(p-value). cc u/ashlanddog

I watched the hearing and don't think we can really blame this one on the judge; attorney McCauley did a poor job and the case was not nearly as well organized as Lake's and Hamadeh's Notes on Finchem Hearing

Thank you for the sentiments and you raise many valid criticisms, especially on how Christianity is practiced. There remain difficult issues in our hierarchy of American governments regarding where to draw legal lines on difficult issues like abortion and what is taught in public schools. All such decisions and laws ultimately rely on some form of moral code. But from where does that code arise?

The Myth of Religious Neutrality by Roy Clouser makes the convincing case that everyone is religious in some sense, even atheists. That is, everyone considers something to be divine, e.g. for many who reject Christianity it becomes the cosmos or some kind of mysticism. We have evidence that many of the elite cabal actually worship Satan.

While a fundamental principle of American jurisprudence is freedom of religion, we still must enact and enforce laws locally and federally that ultimately decide what kind of nation we are and will become. Remember Biden and Harris' campaign slogan: "Battle for the Soul of America". My vote is that we, with the love and grace you recommend, continue to rely on traditional Christian moral values and curtail progressives who wish to banish them from the public square.

Both Hobbs and Finchem livestreams appear to be available now.

Finchem also at the same time! I'm a lot less optimistic about his case due to his judge.

More analysis on the German KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far.

I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter and ICD-10 code, and then take log2 of this sum to standardize it.
2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

summary posted right afterwards

According to the GP article it will be Peter Thompson, with degrees from BYU

looks like 10/31/2020

Bar chart down-ballot comparison: https://www.analytics805.com/az-analysis Fishy

Although Wikipedia is left-leaning, it provides some useful background and links, and has already has a summary of today: https://en.wikipedia.org/wiki/Moore_v._Harper

Moore's case is based on the Independent State Legislature (ISL) theory derived from the Article I Election Clause and is argued nicely by David Thompson. ISL solidifies authority of the state legislatures to conduct elections but appears vulnerable to arguments circa gerrymandering and substance vs procedure.

A full written transcript of today's arguments is available at https://www.supremecourt.gov/oral_arguments/audio/2022/21-1271 . The questions from each justice are interesting and revealing.

Hope for the best. Plan for the worst.

I presume Putin and Armageddon are in on the plan, at least as facilitators. To what degree will Russian tech play a role in the show?

Each registrant is checked for symptoms at multiple times; the average is around 14 times, but it varies from 0 to 100+.

Downloaded the V-Safe data from https://www.icandecide.org/v-safe-data/ Out the 9.55M registrants with at least one follow up record in the largest table, I’m seeing:

• 15.2% experienced at least one Severe Adverse Event (out of the dozen variables coded as Mild, Moderate, or Severe)
• 36.6% experienced at least one health impact event causing them to miss work, school, or seek medical care (the HEALTH IMPACT variable has somewhat combined and confusing coding, so this number reflects any nonmissing event)

Thanks for the summary! Here are the doctors from the first hour: Peter McCullough, Ryan Cole, Harvey Risch, Pierre Kory, Richard Urso, Christina Parks, Mary Bowden, Harpal Mangat, Paul Marik, Aaron Kheriaty, Robert Malone, David Wiseman, Paul Alexander

This interesting work by Craig Paardekooper is sufficient grounds for a formal investigation. We need much more transparency by the manufacturers regarding lot sizes and distribution patterns. Furthermore, I just downloaded the expiration data and found that 19.5% of the 354550 rows in the US VAERS data that have expiration data have RECVDATE > Expiration Date. This appears to be grounds for lawsuits for administration of expired product: approximately 1 in 5 vials being injected and reported in VAERS are expired.