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== Communist genocide ==
== Antifs ==
]? ] ] 17:39, 7 September 2024 (UTC)
Hi, and thanks for your advice regarding my 1RR sanction. I will think this over and then decide whether to appeal and what to write.
:Perhaps hold off for a while and see if we make any progess. ] (]) 00:13, 8 September 2024 (UTC)
By the way, I made an interesting observation regarding the AfD of ].
== Invitation to participate in a research ==
*the EEMLers had canvassed the vote twice, see 20090804-0758 by Digwuren and two days later by Martintg 20090806-0526
*the EEMLers voted for only the same position
*unusually many of the the EEMLers voted
*the EEMLers sabotaged a 2/3 majority
Hello,
*support: triplestop, shawn, drmies, mintrick, offliner, russavia, abductive, lokiit, rankiri, ezhiki, ironholds, muzemike, passwordusername, the four deuces, good ol'factory, jeff g., irbisgreif, igny, nick-de, calton, rd232, multixfer, soap, dimawik, the red hat of pat ferrick, voluntary slave, soman, lk, csloat
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*support: 29 (of which: 0 EEMLer)
*oppose: 22 (of which: 8 EEMLer)
*percentage of support: 56.86 %
*support (excluding EEMLer): 29
*oppose (excluding EEMLer): 14
*percentage of support: 67.44 %
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*2/3 majority required for pass = 66.6 %
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I think this should be pointed out to the arbs and the admins considering the AE threads. ] (]) 17:29, 4 January 2010 (UTC)
Kind Regards,
== RS/N & Transaction Publishers ==
]
If you can find it stated that Transaction Publishers are as bad as I feel them to be, in appropriate reliable sources, then it would be great if you could note the sources criticising Transaction for being a conservative / conspiracy theorist / white reactionist press whose standards of fact checking and faithfulness are those of your average CIA/Ford Foundation establishment that would be great. If it doesn't exist, then their texts will have to be case-by-case with the usual appropriate presumptions. ] (]) 01:44, 12 January 2010 (UTC)
<bdi lang="en" dir="ltr">] (]) 19:27, 23 October 2024 (UTC) </bdi>
== Socks ==
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== ArbCom 2024 Elections voter message ==
I supported the request - and also added the notice to his page --] <small><sup>]</sup></small> 06:38, 6 February 2010 (UTC)
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Look, your edit warring on ] is not acceptable. I did block you, but undid it when I saw that I had neglected to account for you starting to discuss on the talk page of the article. An edit like , after SandyGeorgia had explicitly listed the reason for adding each one in her edit summary, is disruptive. Please stop. <font color="navy">''']</font>''' ''(<font color="green">]</font>)'' 20:44, 6 February 2010 (UTC)
== ] ==
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Will you please refactor the section of your edit that consists of a personal attack? Also, please note, e.g., that I support the article name change and also believe that anthropogenic global warming is occuring.--] (]) 04:21, 9 February 2010 (UTC)
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== Statistics on Education and Jobs, with home-made graphs ==
]
]
Instead of us getting into the Misplaced Pages-equivalent of ], I'm going to make more statistical plots and graphs. My goal is to understand the American workplace as it relates to education.
I'm working on taking statistics from the BLS (]) and making them into plots. Fundamentally, my goal is to analyze educational attainment as it relates to labor, with graphs I make myself. '''This is a work in progress.'''
These graphs do explain why Kamala Harris won high-income voters, and Trump won low-income voters this year. Median weekly earnings systematically increase as educational attainment increases. It may seem obvious, but the graph here really shows how thorough it is.
'''Note''': I graduated this year with a Bachelor's in mathematics & statistics, and one of my skills is making plots in ]. ] (]) 02:48, 27 December 2024 (UTC)
:Your perceived correlation between higher income and voting Democratic is a ]; "a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor." Education is the ] variable. It explains why your hypothesis breaks down for both the highest 1% and lowest 20% of voters. Furthermore, there are other variables that influence voting which may also help explain the false finding.
:In order to test your hypothesis, you need to test it.
:Could you please provide linear regression analysis with three variables: party, income and education level. Then tell me what the correlation income and party is. Since you have already entered all the data to determine correlation between party and income and party and education, this should be simple.
:Once you factor in religion, race and urban/suburban/exurban/rural, you should see strong relationship between income and party preference, with Republican voting correlating to higher levels of income.
:Let me know your results! ] (]) 05:19, 27 December 2024 (UTC)
::'''Challenge accepted, to show that I really do know linear regression.''' These were my three variables: Trump & Harris vote share, median income by state, and percentage Bachelor's by state, for all 50 States and D.C. I first did income. The massive outlier for both is the District of Columbia.
::]
::] ] (]) 16:10, 27 December 2024 (UTC)
:::Second, I did education. This time the correlations were much stronger, as you can see visually. Again, the District of Columbia is a massive outlier.
:::]
:::] ] (]) 16:37, 27 December 2024 (UTC)
::::Third, I did 3-D plots for both, comparing vote share to both education and income. I'll give you the correlations next.
::::]
::::] ] (]) 16:51, 27 December 2024 (UTC)
:::::Here are the sets of correlations for income and education each. For full transparency, I'm just copy-and-pasting the R code:
:::::'''# Calculate correlations for income and vote share'''
:::::'''For the 3-D model there are two ways to do it. I bolded the relevant correlations for you.''' The first is to do it by calculating the correlation between predicted and actual vote share. The second is to do it for the multiple regression model using the summary() function. I did both, but I'm not showing all the summary() data to save space.
:::::> # Add predicted values to the original data
::::::You omitted the top 1% and bottom quintile income earners. Please add information for all percentiles.
::::::Please provide the algebraic formula, viz., x=a*A + b*B, where:
::::::x=likelihood of voting for Trump,
::::::a=first variable
::::::A=income expressed as a percentile of the population
::::::b=second variable
::::::B=education level expressed as a percentile of the population ] (]) 18:43, 27 December 2024 (UTC)
:::::::For full transparency, these are the algebraic formulas in bold for the multiple linear regression models. Vote share is treated like a probability, ranging from 0 to 1. Bachelor's percentage ranges from 0 to 100, and income is in the thousands. <u>I don't have data on individual voters by income, but if you have it, I might be able to do linear regression on individual income as well.</u>
:::::::'''Note:''' Median Income by state has almost zero effect on the model, with p-values hovering around the standard 0.05: 0.0455 for Trump and 0.0715 for Harris. The coefficients are -2.528e-06 and 2.288e-06, which are rounded to zero.
:::::::* I can't run regression on individual voters by income, because I don't have that data. All we have are exit polls for individual voting behavior by income in specific dollar ranges, which don't nicely correspond with income percentiles. If you can provide me that data, I might be able to do it. The electoral college is by state or at best state counties, and individuals within those jurisdictions have widely varying incomes.
:::::::* Education is a ], and it doesn't really make sense do it by percentile because educational attainment isn't quantitative. It would instead requiring in effect clusters of people with specific amounts of education as essentially different sub-populations, as percentages of the total population.
:::::::Note: <u>You are helping me improve my statistical skills,</u> and we're acting like a human version of a ], with you as the discriminative network to judge my work and me as the generative network to create statistical models. ] (]) 23:01, 27 December 2024 (UTC)
The Wikimedia Foundation is conducting a survey of Wikipedians to better understand what draws administrators to contribute to Misplaced Pages, and what affects administrator retention. We will use this research to improve experiences for Wikipedians, and address common problems and needs. We have identified you as a good candidate for this research, and would greatly appreciate your participation in this anonymous survey.
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The survey should take around 10-15 minutes to complete. You may read more about the study on its Meta page and view its privacy statement .
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Hello The Four Deuces, may you be surrounded by peace, success and happiness on this seasonal occasion. Spread the WikiLove by wishing another user a Merry Christmas and a Happy New Year, whether it be someone you have had disagreements with in the past, a good friend, or just some random person. Sending you heartfelt and warm greetings for Christmas and New Year 2025. Happy editing, Abishe (talk) 23:01, 24 December 2024 (UTC)
Statistics on Education and Jobs, with home-made graphs
Percentage of jobs by minimum educational attainmentEducational attainment and income
Instead of us getting into the Misplaced Pages-equivalent of flame wars, I'm going to make more statistical plots and graphs. My goal is to understand the American workplace as it relates to education.
I'm working on taking statistics from the BLS (Bureau of Labor Statistics) and making them into plots. Fundamentally, my goal is to analyze educational attainment as it relates to labor, with graphs I make myself. This is a work in progress.
These graphs do explain why Kamala Harris won high-income voters, and Trump won low-income voters this year. Median weekly earnings systematically increase as educational attainment increases. It may seem obvious, but the graph here really shows how thorough it is.
Your perceived correlation between higher income and voting Democratic is a spurious relationship; "a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor." Education is the confounding variable. It explains why your hypothesis breaks down for both the highest 1% and lowest 20% of voters. Furthermore, there are other variables that influence voting which may also help explain the false finding.
In order to test your hypothesis, you need to test it.
Could you please provide linear regression analysis with three variables: party, income and education level. Then tell me what the correlation income and party is. Since you have already entered all the data to determine correlation between party and income and party and education, this should be simple.
Once you factor in religion, race and urban/suburban/exurban/rural, you should see strong relationship between income and party preference, with Republican voting correlating to higher levels of income.
Challenge accepted, to show that I really do know linear regression. These were my three variables: Trump & Harris vote share, median income by state, and percentage Bachelor's by state, for all 50 States and D.C. I first did income. The massive outlier for both is the District of Columbia.
Trump Vote Share vs. Median Income in all 50 States and DC
For the 3-D model there are two ways to do it. I bolded the relevant correlations for you. The first is to do it by calculating the correlation between predicted and actual vote share. The second is to do it for the multiple regression model using the summary() function. I did both, but I'm not showing all the summary() data to save space.
For full transparency, these are the algebraic formulas in bold for the multiple linear regression models. Vote share is treated like a probability, ranging from 0 to 1. Bachelor's percentage ranges from 0 to 100, and income is in the thousands. I don't have data on individual voters by income, but if you have it, I might be able to do linear regression on individual income as well.
Note: Median Income by state has almost zero effect on the model, with p-values hovering around the standard 0.05: 0.0455 for Trump and 0.0715 for Harris. The coefficients are -2.528e-06 and 2.288e-06, which are rounded to zero.
I can't run regression on individual voters by income, because I don't have that data. All we have are exit polls for individual voting behavior by income in specific dollar ranges, which don't nicely correspond with income percentiles. If you can provide me that data, I might be able to do it. The electoral college is by state or at best state counties, and individuals within those jurisdictions have widely varying incomes.
Education is a categorical variable, and it doesn't really make sense do it by percentile because educational attainment isn't quantitative. It would instead requiring in effect clusters of people with specific amounts of education as essentially different sub-populations, as percentages of the total population.
