Hello. I have a large set of temperature observations called tmax (26298×1 double) and I want to make a bell curve of all the data to see what it looks like. I've done a lot of browsing and can't find a simple way to do this. Does anyone have help on this? Thank you!
MATLAB: Creating a Bell Curve of Data
bell curvedistributionfiguregraphnormal distributionplot
Related Solutions
doc normcdfdoc normpdf
When you know what you want but not sure the name, try something like
>> lookfor normalrealmin - 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 frequencyconvertfrequnits - converts between Normalized, Hz, kHz, etchistfit - Histogram with superimposed fitted normal density.jbtest - Jarque-Bera hypothesis test of composite normality.lhsnorm - Generate a latin hypercube sample with a normal distributionlogncdf - 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...
It looks like you are looking at comparing the Cumulative Distribution Function (CDF) with the Empirical Cumulative Distribution Function (ECDF) which is not the same as a probability plot.
I'll start by generating some random numbers from an EVD as I do not have your data:
rng('default')a = gevrnd(0,107,399,100,1);
Next we need to fit the parameters of the distribution:
Mdl = fitdist(a,'GeneralizedExtremeValue'); % Returns a PD object (Requires MATLAB later than 2009)
You can then calculate the ECDF: [f,x] = ecdf(a); % plot(x,f)
Then calculate the CDF implied by the fitted parameters:
y = cdf(Mdl,x);hold onplot(x,y)hold off
If you indeed wanted to look at a probability plot, you should know that by default probplot compares the data with a normal distribution. To change this, pass in the name of the distribution:
figureprobplot('extreme value',a)
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