I need to find 10000 ramdom # in the range of (0.078,0.0078) for mass and (2, 0.1) for diameter. Then calculate the density of the 10k and do a histogram of them. Can anybody help me please?. This is what I got so far but is not working.

clc, clear

%samples for the mass

a1 = 0.078;

b1 = 0.0078;

m = b1.*randn(10000,1)+a1;

%samples for the diameter 

a2 = 2;

b2 = 0.1;

d = b2.*randn(10000,1)+a2;

%finding the density

density = (m/((4/3).*pi.*(d/2).*(d/2).*(d/2)));

histogram(density)

Answer 1

[Azzi Abdelmalek]

You mean in the range of [0.0078 0.078] not [0.078, 0.0078], because 0.0078 is less then 0.078.

a1 = 0.078;
b1 = 0.0078;
m = a1.*rand(10000,1)+b1
min(m)
max(m)

[John D'Errico]

Please stop adding answers every time you want to make a simple comment. There is a link for comments. Use it.

Moved answer by Shirly into a comment:

"the values for my density are zeros and the histogram is blank"

Answer 2

[Image Analyst]

Try this:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;

% Create samples for the mass with specified mean and standard deviation.
meanMass = 0.078;
sdMass = 0.0078;
mass = sdMass .* randn(10000,1)+ meanMass;

% Create samples for the diameter with specified mean and standard deviation.  
meanDiameter = 2;
sdDiameter = 0.1;
diameters = sdDiameter .* randn(10000,1) + meanDiameter;

% Find the volumes
volumes = (4/3) * pi * (diameters/2) .^ 3;
% Find the density
density = mass ./ volumes;

histogram(density);
grid on;
title('Density Distribution', 'FontSize', 15);
xlabel('Density', 'FontSize', 15);
ylabel('Count', 'FontSize', 15);

[Shirly S]

It worked. Thank you so much!! If is not too much to ask, what was my mistake??

[Image Analyst]

Other than non-descriptive variable names, the main error was using m slash instead of m dot slash, which will do an element by element divide instead of a matrix inverse.

Also, please read this for your future posts.