#1: "Unceded” is a statement of fact, not an opinion.
#2: Lawfulness of a conflict is a statement of fact, not an opinion.
#3: Funny how there are lots of other "moral" arguments that are ok... but not this one.
#4: Diversity, equity and inclusion isn't a “doctrine”, it's simple kindess, get over yourselves probably-white-entitled-man-children-professors

Es geht hier tatsächlich um einen Anti-AfD-Schriftzug mit Sprühkreide……

Regensburger Ermittlungsrichter ordnet DNA-Test an

„Einen solchen Beschluss hat ein Ermittlungsrichter am Amtsgericht Regensburg letzte Woche gegen eine 22-Jährige erlassen. Ohne vorherige Anhörung verfügte er, dass bei der jungen Frau ein sogenannter Mundhöhlenabstrich „und im Falle der Weigerung die Entnahme einer Blutprobe“ vorgenommen werden darf. Ebenso ordnete der Richter an, dass ihre Wohnung „zum Zweck der Entnahme von Körperzellen“ durchsucht werden darf. Dies stehe „in angemessenem Verhältnis zur Schwere der Tat“, heißt es in dem Beschluss…..“

https://www.regensburg-digital.de/wegen-anti-afd-schriftzug-mit-spruehkreide-regensburger-ermittlungsrichter-ordnet-dna-test-an/19022025/

@data @datadon

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

I mean, logically, it should have been easier to:

1. map the President's brain

2. intend to learn something from the map

3. progress to a more complex mapping of the brain of Drosophila melanogaster.

Pallab Ghosh (Science Correspondent, BBC) offers an eloquent description of the fruit fly:

"They can walk, hover and the males can even sing love songs to woo mates - all this with a brain that’s tinier than a pinhead."

It's alleged that funding for (1) was not forthcoming due to the previously acknowledged lack of connections between sparse neurons; the consequent likelihood of government-funded research finding nothing useful on the map.

@data @datadon

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

@data @datadon

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf

Feature Selection in Python; a script ready to use: https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/