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Lognormal Distribution Calculator

Free lognormal distribution calculator. PDF, CDF, mean, median, mode, variance, percentiles. Income,

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Why This Statistical Analysis Matters

Why: Statistical calculator for analysis.

How: Enter inputs and compute results.

ln
STATISTICSDistributions

Lognormal — PDF, CDF for ln(X) ~ N(μ,σ²)

Models income, stock prices, particle sizes. Right-skewed, positive-only. Mean, median, mode, percentiles.

Real-World Scenarios — Click to Load

Inputs

PDF Curve

lognormal_results.sh
CALCULATED
$ lognormal --mu=0 --sigma=1
P(X ≤ 2.0000)
75.5891%
Mean
1.6487
Median
1.0000
Mode
0.3679
Variance
4.6708
Share:
Lognormal Distribution
μ=0, σ=1
P(X ≤ 2.0000) = 75.59%
Mean = 1.649Median = 1.000Mode = 0.368
numbervibe.com/calculators/statistics/lognormal-distribution-calculator

CDF Curve

Calculation Breakdown

COMPUTATION
Mean
1.6487
exp(μ + σ²/2) = exp(0 + 1²/2)
Median
1.0000
exp(μ) = exp(0)
Mode
0.3679
exp(μ − σ²) = exp(0 − 1²)
MOMENTS
Variance
4.6708
(\text{exp}(\text{sigma} ^{2})-1) imes \text{exp}(2\text{mu} +\text{sigma} ^{2})
P(X ≤ 2.0000)
75.5891%
ext{CDF} ext{or} ext{percentile} ext{formula}

For educational and informational purposes only. Verify with a qualified professional.

Key Takeaways

  • • If ln(X) ~ N(μ, σ²), then X follows a lognormal distribution — the logarithm of X is normal
  • • Lognormal is right-skewed and positive-only — ideal for income, prices, sizes, durations
  • • Mean = exp(μ + σ²/2), Median = exp(μ), Mode = exp(μ − σ²) — mean > median > mode for σ > 0
  • • P(a ≤ X ≤ b) = Φ((ln(b)−μ)/σ) − Φ((ln(a)−μ)/σ) — use standard normal CDF
  • • Percentile q: x_q = exp(μ + σ × Φ⁻¹(q)) — inverse normal gives the value

Did You Know?

💰Personal income and wealth are often lognormally distributed — a few earn vastly more than the medianSource: Economics
🏠House prices, stock prices, and asset returns are commonly modeled with the lognormal distributionSource: Finance
⏱️Repair times, service durations, and many "time until event" variables follow lognormal patternsSource: Queueing theory
🧫Cell sizes, particle diameters, and droplet sizes in aerosols are often lognormally distributedSource: Physics
🌍City populations (Zipf-like) and species abundances can be approximated by lognormal modelsSource: Ecology
📊Black-Scholes option pricing assumes lognormal distribution of stock returnsSource: Finance

How It Works

1. The Log-Normal Connection

X is lognormal iff ln(X) is normal. So μ and σ are the mean and SD of ln(X), not of X itself.

2. The PDF Shape

Right-skewed, starts at 0, peaks at the mode, and has a long right tail — no negative values.

3. Mean vs Median

Mean > median because of the right tail. High outliers pull the mean up more than the median.

4. CDF via Standard Normal

F(x) = Φ((ln(x)−μ)/σ). Transform to z-score in log-space, then use normal CDF.

5. Percentiles

x_q = exp(μ + σ × Φ⁻¹(q)). Inverse normal gives z, then exponentiate to get the value.

Expert Tips

When to Use Lognormal

Positive data, right-skewed, multiplicative effects (e.g., growth rates)

Parameter Interpretation

μ and σ are for ln(X). Median = exp(μ). Larger σ = more skew.

Lognormal vs Normal

Lognormal is for positive multiplicative data; normal for additive symmetric data

Fitting from Data

Estimate μ = mean(ln(x)), σ = sd(ln(x)) from your sample

Why Use This Calculator vs Other Tools?

FeatureThis CalculatorExcelRManual
PDF + CDF charts⚠️ Requires chart⚠️ Requires plot
Shaded P(a≤X≤b)⚠️ Manual
Percentiles✅ LOGNORM.INV✅ qlnorm⚠️ Complex
Mean, median, mode❌ Manual❌ Manual
7 presets

Frequently Asked Questions

When is data lognormally distributed?

When the logarithm of the data is normally distributed. Common for positive, right-skewed data: income, prices, sizes, durations, particle diameters.

What is the difference between μ and the mean of X?

μ is the mean of ln(X). The mean of X is exp(μ + σ²/2), which is always greater than exp(μ) = median when σ > 0.

Why is the lognormal distribution right-skewed?

Because it is the exponential of a normal. The exponential stretches the right tail — a few large values can be very large.

How do I fit a lognormal to my data?

Take the natural log of each observation. Compute mean and SD of ln(x). Those are μ and σ. Check normality of ln(x) with Q-Q plot.

What is the relationship to the normal distribution?

X ~ Lognormal(μ,σ²) iff ln(X) ~ Normal(μ,σ²). The lognormal is the exponentiation of a normal.

Can the lognormal have zero or negative values?

No. The support is (0, ∞). The PDF is zero for x ≤ 0.

When should I use lognormal vs exponential?

Lognormal has a peak and long tail; exponential decays from a peak at 0. Use lognormal for sizes, prices; exponential for memoryless waiting times.

How does σ affect the shape?

Larger σ increases skewness and variance. Small σ makes the lognormal look more like a normal (after log transform).

Lognormal by the Numbers

exp(μ)
Median
exp(μ−σ²)
Mode
exp(μ+σ²/2)
Mean
x > 0
Support

Disclaimer: This calculator uses standard normal CDF/inverse approximations. Results are for educational and professional reference. For critical applications (financial risk, reliability), verify against established statistical software.

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