Well if anyone has experience with matlab and probability, help would be greatly appreciated. The question is:

p = .2;

pn = (1-p);

mean = 50*p;

variance = mean*pn;

stddev = sqrt(variance);

x = rand([10000 50]);

y = zeros(size(x));

y(x>=0 & x < 0.2) = 1;

y(x>=0.2 & x<=1) = 0;

[vals,bins] = hist(y,[0:6]);

bar(bins,vals/(10000*(bins(2)-bins(1))));

x = 00:.5:6;

z = gauss_distribution(x,mean,stddev);

hold

plot(x,z,'r')

I dont think this is correct. Can anyone help me out?

**Binomial**: If y = sumation from i =1 to n of a random variable Xi and Xi are Bernoulli(p) RV's(equal to 1 with probability p and equal to 0 with probability of 1-p), then Y has the Binomial pmf. For n = 50and p = 0.2, generate 10,000Rv's Y and plot the histogram, overlaying the Binomial pmf. Also overlay a N(np,np(1-p)) distribution to show that the Binomial pmf may e approximated with a Gaussian pdf.**Here is my code:**p = .2;

pn = (1-p);

mean = 50*p;

variance = mean*pn;

stddev = sqrt(variance);

x = rand([10000 50]);

y = zeros(size(x));

y(x>=0 & x < 0.2) = 1;

y(x>=0.2 & x<=1) = 0;

[vals,bins] = hist(y,[0:6]);

bar(bins,vals/(10000*(bins(2)-bins(1))));

x = 00:.5:6;

z = gauss_distribution(x,mean,stddev);

hold

plot(x,z,'r')

**Here is what my code produces:**I dont think this is correct. Can anyone help me out?

Last edited: