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📚 Statistical Inference for Everyone
Chapters ▾
1 Introduction to Probability2 Applications of Probability3 Random Sequences and Visualization4 Introduction to Model Comparison5 Applications of Model Comparison6 Introduction to Parameter Estimation7 Priors, Likelihoods, and Posteriors8 Common Statistical Significance Tests9 Applications of Parameter Estimation and Inference10 Multi-parameter Models11 Introduction to MCMC12 Concluding Thoughts13 Appendix A: Computational Analysis14 Appendix B: Notation and Standards15 Appendix C: Common Distributions and Their Properties16 Appendix D: Tables

Chapter 14: Appendix B: Notation and Standards

α Alphaslope of a line
β Betaslope of a line, intercept
γ Gamma
Γ Gamma
δ DeltaA small change in a variable
Δ DeltaA change in a variable
ϵ Epsilon
ζ Zeta
η Eta
θ ThetaThe parameters in a binomial/beta distribution
Θ Theta
κ Kappa
λ Lambdathe mean in a poisson distribution
Λ Lambda
μ Muthe mean in a normal distribution (pronounced “mew”)
ν Nu(pronounced “new”)
ξ Xi
Ξ Xi

Adapted from Statistical Inference for Everyone, by Brian Blais (Bryant University), licensed under CC BY-SA 4.0 (dual-licensed under the GNU FDL 1.2 or later; this adaptation uses the CC BY-SA grant). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.