This example shows how to generate a kernel
probability density estimate from sample data using the
Load the sample data.
This data contains miles per gallon (
measurements for different makes and models of cars, grouped by country
of origin (
Origin), model year (
and other vehicle characteristics.
ksdensity to generate a kernel probability
density estimate for the miles per gallon (
[f,xi] = ksdensity(MPG);
ksdensity uses a normal kernel
smoothing function and chooses an optimal bandwidth for estimating
normal densities, unless you specify otherwise.
Plot the kernel probability density estimate to visualize the |MPG| distribution.
figure; plot(xi,f,'LineWidth',2); title('Miles per Gallon'); xlabel('MPG');
The plot shows the pdf of the kernel distribution fit to the
across all makes of cars. The distribution is smooth and fairly symmetrical,
although it is slightly skewed with a heavier right tail.