MATLAB: Generate random lognormal distributed numbers

lognormal distribuationrandom number generator

How I can generate random lognormal distribution data,on the range of [2000 2500] with the mode of 2400?I dont have the quantities of mean and standard deviation!

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The mode of a lognormal is

exp(mu-sigma^2)

where mu and sigma are the location and scale parameters. It looks like you will have an infinite number of choices available to you, to make the mode equal to 2400.

Next, you ask for the distribution over a finite range. Well, the lognormal has an infinite range. So, you could just remove values outside that range -- but then the remaining values will no longer be strictly lognormally distributed.

So, what you are asking for isn't strictly possible. You have some decisions to make. Maybe after you have made those decisions, you can use lognrnd (from the Statistics and Machine Learning Toolbox) to generate some random draws.

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MATLAB: How to go about finding the standard normal probability based on the z-score

doc normcdf
doc normpdf

When you know what you want but not sure the name, try something like

>> lookfor normal
realmin                        - Smallest positive normalized floating point number.
randn                          - Normally distributed pseudorandom numbers.
sprandn                        - Sparse normally distributed random matrix.
surfnorm                       - Surface normals.
isonormals                     - Isosurface normals.
cde                            - cd elliptic function with normalized complex argument.
sne                            - sn elliptic function with normalized complex argument.
addfreqcsmenu                  - Add a cs menu to switch between linear and normalized frequency
convertfrequnits               - converts between Normalized, Hz, kHz, etc
histfit                        - Histogram with superimposed fitted normal density.
jbtest                         - Jarque-Bera hypothesis test of composite normality.
lhsnorm                        - Generate a latin hypercube sample with a normal distribution
logncdf                        - Lognormal cumulative distribution function (cdf).
lognfit                        - Parameter estimates and confidence intervals for lognormal data.
logninv                        - Inverse of the lognormal cumulative distribution function (cdf).
lognlike                       - Negative log-likelihood for the lognormal distribution.
lognpdf                        - Lognormal probability density function (pdf).
lognrnd                        - Random arrays from the lognormal distribution.
lognstat                       - Mean and variance for the lognormal distribution.
mvncdf                         - Multivariate normal cumulative distribution function (cdf).
mvnpdf                         - Multivariate normal probability density function (pdf).
mvnrnd                         - Random vectors from the multivariate normal distribution.
normcdf                        - Normal cumulative distribution function (cdf).
normfit                        - Parameter estimates and confidence intervals for normal data.
norminv                        - Inverse of the normal cumulative distribution function (cdf).
normlike                       - Negative log-likelihood for the normal distribution.
normpdf                        - Normal probability density function (pdf).
normplot                       - Displays a normal probability plot.
normrnd                        - Random arrays from the normal distribution.
normspec                       - Plots normal density between specification limits.
normstat                       - Mean and variance for the normal distribution.
logn3fit                       - Fit a 3-param lognormal dist'n using cumulative probabilities.
wgtnormfit                     - Fitting example for a weighted normal distribution.
wgtnormfit2                    - Fitting example for a weighted normal distribution (log(sigma) parameterization).
>>

Judicious search terms help but seeing the list of things related to "normal" lets you find the two functions of interest (plus a lot more depending upon which toolboxes are available, maybe) that might be of use/interest...

MATLAB: How to check whether the distribution of the data is a right/left skewed normal distribution using kolmogorov-smirnov test

If your data points were positive, they would look like Gamma or Chi-square distribution. Type distributionFitter in the command window and to open the distribution fitter app. There you will have options to easily fit multiple distributions and find the right one.

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Related Solutions

MATLAB: How to go about finding the standard normal probability based on the z-score

MATLAB: How to check whether the distribution of the data is a right/left skewed normal distribution using kolmogorov-smirnov test

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