in MATLAB, find std with using for loop

I write a code for Naive Bayes Classification. But my code is working wrong. In the std calculation part, I want to work for all databases. I wrote it one by one but it is work only my database. I attached my code as pr.m

Help me, please. Where is my mistake? Here is my code: pr.m

	%Reading dataset and getting the inputs from user by using input operation
	matrix = xlsread("Transfusion.xlsx");
    %Find attribute numbers in the dataset
	attributesNum = size(matrix,2) - 1 ; 
    %create an empty matrix for inputs
	X = zeros(attributesNum,1);
    %İt is a for loop for take inputs from user
    for i=1:attributesNum
		values = input('Enter Value: ');
		X(i,1) = values;
    end
    
    %By using this, we create 2 matrices. class 0 and class 1

    mask = matrix(:,end) == 0;
    c0 = matrix(mask,:);
    c1 = matrix(~mask,:);
       
    
	%By using the input data we will first find the mean of each classes means
	%of columns. To do that we are going to use mean() operation. This
	%operation takes the mean of each column of each class
    
    m0=mean(c0(:,attributesNum));
    % disp(m1);
    m1=mean(c1(:,attributesNum));
    % disp(m2);
    for i=1:attributesNum
        for j=1:attributesNum
            m(i,j)= mean(c0(:,j));
            m(i,j)= mean(c1(:,j));

        end
    end
    zo=zeros(size(c0));
	%By using those mean values we are going to find centered data matrix of
	%the dataset for each column of each class
    meanC0 = m0;
    Z0 = c0' - meanC0';
    for i=1:attributesNum
        for j=1:attributesNum
            z0(i,j)= Z0(i,j);
        end
    end
  disp(z0);
    meanC1 = m0;
    Z1 = c1' - meanC1';
    for i=1:attributesNum
        for j=1:attributesNum
            z1(i,j)= Z1(i,j);
        end
    end
 
    
	%At this part we are calculating the standard deviation of each column of
	%each classas centered data matrix columns
	std11 = (std(Z11,1))^2;
	std12 = (std(Z12,1))^2;
	std13 = (std(Z13,1))^2;
	std14 = (std(Z14,1))^2;

	std21 = (std(Z21,1))^2;
	std22 = (std(Z22,1))^2;
	std23 = (std(Z23,1))^2;
	std24 = (std(Z24,1))^2;

	%In this part we are calculating the size of both classes and the whole
	%matrix. Then after we found those values we are going to find the
	%probability of each classes

    
	n1 = size(c0,1);
	n2 = size(c1,1);
	n = size(matrix,1);

	Pc0 = n1/n;
	Pc1 = n2/n;

	%In this part we compute the probability density function (pdf) values evaluated at the values in xi i=1,2,3,4 for the normal distribution with mean mu and standard deviation sigma.

	x1 = X(1,:);
	x2 = X(2,:);
	x3 = X(3,:);
	x4 = X(4,:);

	P11 = normpdf(x1,mean11,std11);
	P21 = normpdf(x2,mean12,std12);
	P31 = normpdf(x3,mean13,std13);
	P41 = normpdf(x4,mean14,std14);

	P12 = normpdf(x1,mean21,std21);
	P22 = normpdf(x2,mean22,std22);
	P32 = normpdf(x3,mean23,std23);
	P42 = normpdf(x4,mean24,std24);

	%Then we  are going to multiply all the probabilities of each classes with
	%probability density function of the class( For each class seperately).
	%Then we take the maximum of the two values that we calculate. Then answer
	%is the maximum one. Which one is maximum, it is the class what the user
	%input included.

	firstClass = Pc0*P11*P21*P31*P41;
	secondClass = Pc1*P12*P22*P32*P42;
    
    disp(firstClass);
    disp(secondClass);
    
    if firstClass>secondClass
        disp("According to input data, He/she did not donate blood in March 2017");
    elseif secondClass>firstClass
       disp("According to input data, He/she did donate blood in March 2017");
    else
        disp("no data");
    end

[Raj]

Can you attach you code here instead of putting it in dropbox?

[Eda Çelik]

Sure, I will attach it now.

[Eda Çelik]

I attached it