Use the tool above to plot statistical distributions online that you
can download as PDFs. The charts show the probability density (or mass) function
and the cumulative distribution function. You can also generate and plot
random samples from the distributions.
To get started, choose a distribution from the drop-down list and enter
parameter values. For additional help click on the
icon at the top right.
Supported distributions include:
Beta (Shape α, Shape β)
Binomial (Trials n, Probability p)
Cauchy (Location a, Scale γ)
Chi-squared (Degrees of freedom k)
Erlang (Shape k, Rate λ)
Exponential (Rate λ)
F (Degrees of freedom n, Degrees of freedom m)
Gamma (Shape k, Scale θ)
Laplace (Location μ, Scale s)
Levy (Location μ, Scale c)
Logarithmic (Probability p)
Logistic (Mean μ, Scale s)
Log-logistic (Shape α, Scale β)
Log-normal (Mean μ, Scale σ)
Negative binomial (Failures r, Probability of success p)
Normal (Mean μ, Scale σ)
Pareto (Scale k, Shape α)
Poisson (Rate λ)
Rayleigh (Scale σ)
Student's T (Degrees of freedom v)
Triangular (Maximum a, Minimum b, Mode c)
Uniform (Minimum a, Maximum b)
Weibull (Shape k, Scale λ)
Click on the "Details" button below the distribution selection to see full
parameterization details for each distribution.
Little issue when plotting the chi-squared distribution with 3 degrees of freedom and x = 10, for the CDF the result is: P(X≤10) = 1.10743 which is impossible (a probability can't be more than 1)
You can plot multiple distributions of the same type using the push-pin icon above and to the right of the charts -- but not distributions of different types at this point.
Did you see the "pin" function? There's a thumbtack icon that appears in the upper-right once you've plotted a distribution. This will let you display multiple plots of the same distribution type; if you mean plotting different distribution types at the same time that would be a challenge but IMore can put it on my list. Thanks for the comment.
Comments Leave a comment
Amazing tool. Thank you!
I think that the cumulative distribution function to Gamma is not right. The plot not make sense.
Compare with R results:
> dgamma(20, shape = 10, scale = 2)
[1] 0.06255502
> pgamma(20, shape = 10, scale = 2)
[1] 0.5420703
> qgamma(0.54, shape = 10, scale = 2)
[1] 19.96693
Very nice and great help.
I'm wondering if you can add Dirichlet distribution?
Leave a comment