Statistics Interactive

Probability

Explore distributions

10 min
Learning goals
  • •You can name and distinguish the most important distributions.
  • •You understand how parameters shape a distribution.
  • •You can interpret PDFs and CDFs.
0.0
1.0
Mean: 0.0000
Variance: 1.0000
Std. deviation: 1.0000
Formulas and worked example

Probability density function (PDF)

f(x)=1σ2π e−(x−μ)22σ2f(x) = \frac{1}{\sigma\sqrt{2\pi}} \, e^{-\frac{(x-\mu)^2}{2\sigma^2}}

Expected value

E(X)=μE(X) = \mu

Variance

Var(X)=σ2Var(X) = \sigma^2

Typical business example

Body heights, measurement errors, standardised test scores