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Summary Statistics II Abbreviations Cheat Sheet | VUB | 2025/26

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This cheat sheet provides a comprehensive reference guide for all abbreviations, symbols, and notations used throughout Statistics II at VUB. It covers Greek letters for population parameters, Roman letters for sample statistics, critical values, core abbreviations (SD, SE, CI, ANOVA, OLS, etc.), hypothesis test names with chapter references, and key formulas across inferential statistics. Essential for exam preparation and quick lookup during problem-solving—organizes all technical notation in one place so you spend less time decoding symbols and more time solving problems.

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2026




STATISTICS II
ABBREVIATIONS CHEAT SHEET



KOEN HANEGREEFS
VUB

, Statistics II — Abbreviations & Symbols

1. Greek letters — population parameters (usually unknown)
Symbol Meaning
μ (mu) Population mean
σ (sigma) Population standard deviation
σ² Population variance
p Population proportion (probability of success)
q=1−p Population proportion of failures
π (pi) Sometimes used for population proportion; in this course textbook always p
α (alpha) Significance level — probability of a Type I error (e.g. 0.05)
β (beta) Probability of a Type II error; 1 − β = power of the test
β₀, β₁ True (population) regression intercept and slope
ρ (rho) Population correlation coefficient
χ² (chi-squared) Chi-square test statistic / distribution
ε (epsilon) Random error term in the regression model y = β₀ + β₁x + ε
Δ (delta) Difference, e.g. Δ = μ₁ − μ₂
Σ (capital sigma) Summation operator: Σxᵢ means add all the xᵢ
ν (nu) Sometimes used for degrees of freedom in tables


2. Roman letters — sample statistics (computed from data)
Symbol Meaning
n Sample size of a single group
n₁, n₂ Sample sizes for group 1 and group 2
N Population size
ȳ (y-bar) or x̄ Sample mean
s Sample standard deviation
s² Sample variance
p̂ (p-hat) Sample proportion of successes
q̂ = 1 − p̂ Sample proportion of failures
b₀, b₁ Estimated regression intercept and slope (from sample data)
ŷ (y-hat) Predicted / fitted value from a regression model
e=y−ŷ Residual = observed value minus predicted value
r Sample correlation coefficient, −1 ≤ r ≤ 1
Coefficient of determination — proportion of variability in y explained by the

model
sₑ (or s) Standard deviation of the residuals — in R: 'Residual standard error'
z Standardised score: z = (x − μ) / σ; also Normal-based test statistic
t t-statistic, used when σ is unknown
F F-statistic: ratio of two variances (ANOVA, regression overall test)
W Wilcoxon signed-rank test statistic (non-parametric)
U Mann–Whitney U statistic (non-parametric, two independent samples)
H Kruskal–Wallis test statistic (non-parametric ANOVA)



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