how to find the mean of normally distributed data

I want to find the normally distributed data with different sample size and find the mean of it. My code works perfectly fine but I want to know if there is better ways to code such problem, such as finding mean right away without setting it to new variable

Here is my work.

M1= normrnd (80,15,[1 10]); %normal dist, mean 80 std 15 sample 10

M2= normrnd (80,15, [1 20]); %normal dist, mean 80 std 15 sample 20

M3= normrnd (80,15, [1 50]); %normal dist, mean 80 std 15 sample 50

M4= normrnd (80,15, [1 100]); %normal dist, mean 80 std 15 sample 100

M5= normrnd (80,15, [1 200]); %normal dist, mean 80 std 15 sample 200

b1=mean (M1); % find mean of each matrix

b2=mean (M2);

b3=mean (M3);

b4=mean (M4);

b5=mean (M5);

meanM= [10,b1;20,b2;50,b3;100,b4;200,b5]; %matrix of sample size vs mean

disp(' sample size mean of data')

disp(meanM)

Answer 1

[Image Analyst]

No - it can't get much simpler, though you could do it differently. You're doing it right. There are other ways but for only 5 cases, it doesn't really simplify things any.

Answer 2

[Roger Stafford]

To reduce the number of lines you could do this:

   I = [10;20;50;100;200];
   CI = [0;cumsum(I)];
   CR = cumsum([0;normrnd(80,15,CI(end),1)]);
   meanM = [I,(CR(CI(2:end)+1)-CR(CI(1:end-1)+1))./I];

However, as Image Analyst indicates, for only five cases it may not be worth the extra effort in thinking it out.