Fair coin question. How can I find the shaded area under the curve of A whole or a particular bin area to find specific probability of either heads or tails

1 Ansicht (letzte 30 Tage)
Hi, I am trying to develop a Matlab code from which I can enter a number of coins tossed and be able to obtain a normalized histogram from which I can tell the probability of either heads or tails. I want the x-labes to display TAILS and HEADS and the corresponding Bin area. I also want to have the formula from which I can calculate the shaded area of either heads or tails and tell the probability. This is my code.
Also I wish to super position of a Normal Gaussian bell and I want to demonstrate that by adding more coins, the probability approaches 0.5.
Please advise on how to change colors in the Bin area.
Thanks
clear all; %get rid of everything in memory
close all; %close all open figures
clc
%#Define the parameters
number of tossed coins n=10 %This number can vary
%S=["H", "T"]; %example of labels but I do not know how to implement it.
coin=randi([0, 1], 1, n) %probability range between 0 and 1; size of toss matrix is 1 row by n columns
Bins_Centers = [0 1]; %2 coins
d = diff(Bins_Centers)/2;
Bins_Edges = [Bins_Centers(1)-d(1), Bins_Centers(1:end-1)+d, Bins_Centers(end)+d(end)];
Bins_Edges(2:end) = Bins_Edges(2:end)+eps(Bins_Edges(2:end))
%Figure 1
screensize = get( groot, 'Screensize' );
f1=figure('Position', screensize)
ax1 = gca;
% edge= [0 1];%specify the edge of the histogram (as a vector)
h=hist(coin, Bins_Centers)
bar(Bins_Centers, h)
title(sprintf('Histogram generated from %d coin tosses', n));
xlabel(sprintf('Number of heads (0) and tails (1) in %d in coins', n));
ylabel('Count');
hold(ax1,'on')
%Figure 2
screensize = get( groot, 'Screensize' );
f2=figure('Position', screensize)
ax2 = gca;
freqs=histogram(coin, Bins_Edges);
title(sprintf('Histogram generated from %d coin tosses', n));
xlabel(sprintf('Number of heads (0) and tails (1) in %d in coins', n));
ylabel('Count');
hold(ax2,'on')
  4 Kommentare
Torsten
Torsten am 3 Mai 2023
Bearbeitet: Torsten am 3 Mai 2023
P(k tails in n trials) = P((n-k) heads in n trials)
This is reflected by
nchoosek(n,k) = nchoosek(n,n-k)

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