• Statistics
  • 1 Statistics
  • 2 Probability
    • 2.1 Intuition for Probability
    • 2.2 Pandemic Risk Management
  • 3 Big Data Paradox
    • 3.1 Quarantine fatigue thins fat-tailed impacts
    • 3.2 Herd Immunity impossible with new Mutants
    • 3.3 Danish Mask Study
  • 4 Fat Tails
    • 4.1 Extremes
      • 4.1.1 Catastrophe Principle
    • 4.2 Statistical Consequences of Fat Tails
      • 4.2.1 Power Law Distributions
    • 4.3 Lindy Effect
  • 5 R Programming Language
    • 5.1 Reinsurance
    • 5.2 Superspreaders
    • 5.3 Vaccine
  • 6 Inequality
  • 7 Autocorrelation
    • 7.1 Dunning-Kruger is Autocorrelation
  • 8 Causation
    • 8.1 Liang Causality
    • 8.2 Causation in Chaotic Dynamic Systems
    • 8.3 Causal Inference
      • 8.3.1 Causal Inference with Spatio-Temporal Data
    • 8.4 Rethinking Causation in the Social Sciences
  • 9 Hypothesis Testing
    • 9.1 R Testing
    • 9.2 Connecting to Theory
    • 9.3 Placebo Powerless
    • 9.4 GLMM
    • 9.5 Logit
      • 9.5.1 Odd’s Ratio
  • 10 P test
    • 10.1 P-Value Hacking
    • 10.2 Bootstrapping instead of p-values
    • 10.3 Probit
  • 11 Spurious Correlation
    • 11.1 Trending Variables
  • 12 Stationarity
    • 12.1 Record Events
  • 13 Collapse
    • 13.1 AMOC Collapse EWS (Early Warning Signals)
  • 14 Power laws
    • 14.1 Generative Models
    • 14.2 Income Distribution Power Law
    • 14.3 Timescaling Rainfall
    • 14.4 Billionaire Power Law
  • 15 Syntetic Control
  • 16 Econometrics
  • I Appendices
  • Appendices
  • A About
  • B Links
  • C NEWS
  • D Sitelog
  • E Identification
  • On Github

Statistics

B Links

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Not listed:

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