Normal Distributions

Exam Prep · 20 Questions
Statistics — Unit 11.4

Normal Curves,
Z-Scores & Percentiles

Covers: Empirical Rule (68-95-99.7) · z-score calculation · normalcdf · inverse normal · comparing distributions. A z-table is included below.

μ −σ −2σ +2σ −3σ +3σ 68% 95% 99.7%
Quick Reference — Memory Keys
68-95-99.7 RULE

±1σ → 68%
±2σ → 95%
±3σ → 99.7%
Symmetric → each half = ÷2

Z-SCORE

\(z = \dfrac{x - \mu}{\sigma}\)
x=value, μ=mean, σ=std dev
Tells how many σ from mean

normalcdf(LB, UB, μ, σ)

Left tail: LB = −∞
Right tail: UB = +∞
Between: both bounds

RIGHT TAIL = 1 − CDF

P(X > x) = 1 − Φ(z)
Total area always = 1

SYMMETRY Φ(−z)=1−Φ(z)

Negative z → mirror!
Below mean → answer < 0.5

COMPARE = USE Z-SCORE

Higher z = better relative performance
Context always matters!

Z-Score
\(z = \dfrac{x - \mu}{\sigma}\)
Back-Calculate x
\(x = \mu + z \cdot \sigma\)
normalcdf
\(\text{normalcdf}(-\infty, x, \mu, \sigma)\)
Each half of ±1σ
34%  |  ±2σ half = 47.5%

Each value = Φ(z) = P(Z ≤ z), the cumulative area from −∞ to z (left tail).

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