Data Analysis - Matlab Code

I have a dataset(145 rows, 32 columns without id and attributes https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluation+Dataset ). I can read it in matlab with code. But I can't find the centered data matrix. I can find centered data matrix in another example.

D = randi([-5, 5] , 4,2] generate 8 numbers from -5 to 5 and I use size() ones() etc.

onesd = ones(size(D,1)1)

meand= mean(D)

D=onesd*meand then i find the centered data matrix

how can i write this dataset i have with centered data matrix

[Mustafa Furkan SAHIN]

clc

clear

dataset = xlsread('DATA.csv','DATA');

[m,n] = size(dataset)

Mean = mean(dataset)

Size = size(dataset, 1)

dataset2 = repmat(Mean,Size,1)

CenterMatrix = dataset - dataset2

Is this my code correct? Is such use acceptable?

Answer 1

[Torsten]

D = randi([-5, 5],4,2)

D = 4×2

4 -1 -2 -5 -4 -1 -2 -3

Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1))

Cn = 4×4

0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500

D_centered = Cn*D

D_centered = 4×2

5.0000 1.5000 -1.0000 -2.5000 -3.0000 1.5000 -1.0000 -0.5000

mean(D_centered,1)

ans = 1×2

0 0

[Mustafa Furkan SAHIN]

thank you for answering but I couldn't figure out how to run this command for a 145 row 32 column dataset

edit:I understood the logic of the formula. I tried and found the answer for Higher Education Students Performance Evaluation Dataset

[Image Analyst]

D = randi([-5, 5], 145, 32); % a 145 row 32 column dataset.
Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1));
D_centered = Cn*D;
results = mean(D_centered,1)

results = 1×32

1.0e-14 * 0.0515 -0.0809 -0.0784 -0.0557 0.0294 -0.0527 0.0760 0.0263 0.0760 0.0600 -0.0343 0.0012 0.0058 -0.0178 0.0662 -0.0025 -0.0196 0.0735 -0.1176 -0.0208 -0.0098 0.0184 -0.0061 0.0098 -0.1017 -0.0472 -0.0049 0.0319 0.0319 -0.0576

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[Mustafa Furkan SAHIN]

Thank you for answering.

Actually i mean understood how to apply a centered data matrix to the dataset(Higher Education Students Performance Evaluation Dataset) But I don't understand why we write between -5 and 5. Is it default?

[Torsten]

But I don't understand why we write between -5 and 5. Is it default?

It's an example.

You wrote yourself that you tested it for D = randi([-5, 5] , 4,2].

Instead of the line

D = randi([-5, 5] , 4,2]

use your own data matrix from above as D.

[Mustafa Furkan SAHIN]

I understood the logic of the formula. I tried and found the answer for Higher Education Students Performance Evaluation Dataset (145 rows and 32columns). Thanks Torsten

[Mustafa Furkan SAHIN]

This is my file, I encountered an error

[Torsten]

Works without problems (see below).

D=[2	2	3	3	1	2	2	1	1	1	1	2	3	1	2	5	3	2	2	1	1	1	1	1	3	2	1	2	1	1	1	1
2	2	3	3	1	2	2	1	1	1	2	3	2	1	2	1	2	2	2	1	1	1	1	1	3	2	3	2	2	3	1	1
2	2	2	3	2	2	2	2	4	2	2	2	2	1	2	1	2	1	2	1	1	1	1	1	2	2	1	1	2	2	1	1
1	1	1	3	1	2	1	2	1	2	1	2	5	1	2	1	3	1	2	1	1	1	1	2	3	2	2	1	3	2	1	1
2	2	1	3	2	2	1	3	1	4	3	3	2	1	2	4	2	1	1	1	1	1	2	1	2	2	2	1	2	2	1	1
2	2	2	3	2	2	2	2	1	1	3	3	2	1	2	3	1	1	2	1	1	1	1	1	1	2	1	2	4	4	1	2
1	2	2	4	2	2	2	1	1	3	1	3	1	1	2	4	2	2	2	2	1	2	1	1	3	3	3	3	4	4	1	5
1	1	2	3	1	1	1	2	2	3	4	3	1	1	4	3	1	2	2	1	1	1	3	1	3	2	2	1	1	1	1	2
2	1	3	3	2	1	1	1	1	3	2	4	2	1	2	4	1	2	2	1	1	1	1	1	3	2	2	2	4	3	1	5
2	1	2	3	2	2	1	3	4	2	1	2	3	1	2	3	2	2	2	1	1	2	1	1	2	2	2	2	1	2	1	0
1	1	1	3	2	2	2	3	2	3	3	4	2	1	3	2	1	1	1	1	1	2	1	1	2	2	2	2	1	1	1	2
1	1	1	4	1	1	2	4	2	3	5	5	1	1	3	2	3	3	3	1	3	1	3	2	3	1	3	3	4	3	1	0
1	1	1	4	2	2	2	1	1	1	3	5	4	2	2	2	3	2	2	1	1	1	1	1	2	2	2	3	4	2	1	0
2	1	2	5	2	2	2	1	1	1	2	2	2	1	2	3	1	2	1	1	1	2	1	1	3	2	3	3	4	2	1	1
3	2	2	4	1	1	2	3	4	2	3	1	2	1	4	1	2	2	2	1	1	1	1	1	2	3	2	1	4	4	1	2
2	2	2	3	2	2	2	1	1	2	4	4	2	1	3	1	2	2	2	2	3	2	1	1	3	2	2	3	2	2	1	2
1	1	2	5	2	1	2	1	1	1	2	2	4	1	2	3	2	2	2	1	1	2	1	1	3	2	3	3	4	3	1	1
2	2	2	3	2	2	2	1	1	1	2	2	2	1	2	1	2	2	2	2	1	2	1	1	2	2	2	2	2	2	1	2
1	1	2	4	2	2	2	3	1	1	2	2	5	1	2	5	5	3	2	2	1	2	1	1	3	1	3	3	3	3	1	2
1	2	1	3	2	2	1	2	2	2	3	3	3	1	2	4	4	2	2	1	2	1	1	1	3	2	2	3	2	3	1	3
1	2	2	5	1	2	1	1	4	2	3	3	3	2	2	3	4	2	2	2	1	1	1	2	3	1	2	3	4	4	1	1
1	2	2	5	2	2	1	1	4	2	2	2	4	1	2	3	3	2	2	1	1	1	1	1	3	1	3	3	3	3	1	1
2	2	2	3	1	2	1	1	1	1	1	1	4	2	2	4	3	3	3	1	1	1	1	2	3	1	2	3	3	3	1	3
3	2	2	2	1	1	2	5	1	1	1	4	3	1	2	1	3	2	3	1	1	2	1	1	3	2	3	3	3	3	1	1
2	2	2	3	2	2	2	2	1	1	3	1	3	1	2	4	1	2	2	1	1	1	1	1	2	1	3	2	4	4	1	2
2	2	2	3	2	2	1	1	1	2	1	4	3	1	2	4	2	2	2	1	1	1	1	1	2	1	3	2	1	2	1	3
2	2	2	3	2	1	1	1	1	1	4	4	4	1	2	4	2	3	2	1	1	1	1	1	3	3	3	2	2	1	1	1
1	2	1	3	1	2	2	1	1	1	1	1	4	1	2	3	3	2	2	1	1	2	1	1	3	1	2	1	2	1	1	1
3	2	2	3	2	2	1	1	4	2	2	2	4	1	2	1	2	2	2	1	1	1	1	1	3	2	3	3	5	4	1	3
2	2	3	4	2	2	2	1	4	2	3	3	2	1	4	1	2	2	2	1	1	1	1	1	3	2	2	2	4	3	1	5
2	2	2	5	1	1	1	1	1	2	1	1	5	1	2	4	2	2	2	1	1	1	2	1	2	3	3	3	5	4	1	5
3	2	2	3	1	2	2	1	1	2	1	2	4	1	2	3	2	2	3	1	1	1	1	1	3	3	2	3	4	3	1	3
2	1	2	3	2	2	2	2	1	1	2	2	2	1	2	1	2	2	2	1	1	2	1	1	2	2	2	1	2	3	1	1
2	1	2	3	1	2	1	1	1	1	1	2	5	1	2	3	2	1	2	1	1	1	1	1	1	3	2	2	2	3	1	2
1	2	1	3	2	2	1	2	1	1	3	3	3	1	2	4	2	1	1	1	1	2	1	1	2	2	3	1	4	1	1	2
1	2	1	4	2	2	2	3	1	1	1	2	5	1	2	3	2	2	2	1	1	2	1	1	2	1	3	1	1	1	1	1
2	2	3	4	1	2	1	3	1	1	1	1	2	2	4	3	1	1	3	1	1	2	2	1	2	1	2	1	4	3	1	2
2	2	2	3	1	1	1	2	2	3	3	3	1	1	2	3	2	2	2	1	1	1	1	1	3	2	3	3	5	4	1	1
2	2	2	5	2	2	2	1	1	1	3	3	1	1	2	4	3	2	2	1	1	1	2	1	2	2	2	2	4	3	1	2
2	1	2	3	2	2	1	1	1	2	1	3	1	1	2	4	1	2	1	1	1	1	1	1	2	2	2	3	2	1	1	1
1	2	1	3	2	2	2	2	1	1	2	3	1	1	2	5	1	2	2	1	1	1	1	1	3	1	2	1	2	3	1	1
3	2	2	3	1	2	2	2	1	2	1	4	2	1	2	2	3	2	2	1	1	2	1	1	3	2	2	3	4	3	1	1
2	2	2	3	2	1	2	1	4	2	4	4	2	1	3	1	2	2	1	1	1	1	1	1	2	1	3	1	2	3	1	1
1	2	2	3	2	2	1	1	1	1	2	3	3	1	2	1	2	2	2	1	1	2	1	1	3	1	3	3	3	2	1	4
2	2	3	3	2	2	1	1	1	1	1	3	2	1	2	5	2	2	2	1	1	1	2	1	3	2	2	1	4	3	1	1
1	2	2	3	2	2	1	4	1	1	2	3	5	1	2	4	3	2	2	1	1	1	1	1	2	2	2	1	4	3	1	3
2	2	2	3	2	2	1	1	1	1	1	2	3	1	2	5	2	2	2	1	1	1	1	1	2	2	3	1	4	2	1	5
2	2	2	3	2	2	1	1	1	2	4	3	1	1	5	4	2	2	2	1	1	2	1	1	2	1	3	2	5	3	1	3
1	2	2	3	2	1	1	1	1	2	3	3	3	1	2	2	1	1	2	1	1	1	1	1	3	2	3	2	2	2	1	1
1	2	1	4	2	2	2	2	4	1	2	3	2	1	2	4	2	2	2	1	1	1	2	1	2	2	2	1	3	2	1	2
2	2	2	3	2	2	1	2	2	1	1	1	2	1	2	3	2	2	2	1	1	2	1	1	2	2	2	3	3	3	1	1
2	1	3	3	1	1	2	1	1	1	1	2	5	1	2	1	1	2	2	1	1	2	1	1	2	3	3	1	3	3	1	4
2	1	2	3	1	2	2	2	1	1	1	3	4	1	2	1	3	1	1	1	1	1	1	2	2	2	2	1	4	2	1	1
2	1	2	3	2	1	2	2	1	1	1	1	5	1	2	1	1	2	2	1	1	2	1	1	2	2	2	2	3	2	1	5
2	2	2	3	2	2	2	3	4	2	1	4	2	1	4	2	2	1	1	1	1	1	1	2	3	1	3	1	5	3	1	3
3	2	2	3	1	2	1	4	1	2	1	1	5	3	2	1	1	2	2	1	1	1	3	3	1	3	3	3	5	4	1	3
2	2	2	3	2	1	2	1	1	1	1	3	5	1	2	4	4	2	3	1	1	1	1	2	3	2	3	3	5	4	1	5
2	2	2	3	1	1	2	1	1	1	4	2	3	1	3	3	2	3	2	1	1	1	1	1	3	2	3	1	5	4	1	4
3	2	2	3	2	2	1	3	1	1	1	3	5	2	2	1	3	2	2	1	1	1	1	1	3	2	2	2	5	4	1	3
2	2	2	3	2	1	1	1	1	1	4	4	3	1	2	3	3	2	2	1	1	1	1	1	3	3	2	2	4	3	1	5
2	1	2	3	2	2	2	5	2	1	6	1	3	1	2	3	1	2	2	1	1	1	1	1	1	3	3	1	2	1	1	2
1	2	3	3	2	1	2	2	1	2	1	3	4	1	2	3	3	1	2	1	3	2	1	1	3	2	2	2	4	4	1	5
2	2	2	3	2	2	2	1	4	2	2	3	2	1	2	4	3	2	2	1	1	1	1	1	2	3	3	2	5	4	1	3
2	2	2	4	2	2	1	2	1	1	1	3	3	1	2	1	1	1	2	1	1	1	3	1	2	2	2	1	3	3	1	5
2	2	3	5	2	2	2	1	1	1	2	2	2	1	2	1	1	2	2	1	3	1	2	1	3	2	3	1	4	3	1	3
1	2	2	3	2	2	1	3	1	1	2	4	3	1	2	4	1	1	2	1	3	2	2	1	2	1	2	1	2	2	1	2
2	2	2	3	2	2	1	1	1	1	3	2	5	1	2	5	2	2	2	1	1	1	1	1	3	2	2	3	5	4	2	5
2	2	3	3	1	1	2	3	1	2	3	4	4	2	2	2	2	2	2	1	1	2	1	1	2	2	1	2	2	3	2	1
2	1	2	4	1	2	2	1	1	1	2	3	5	3	2	5	1	2	2	1	1	2	1	1	2	2	3	2	4	3	3	5
2	1	2	4	2	2	1	1	1	1	2	2	2	1	2	3	1	1	2	1	1	1	2	1	3	2	3	1	3	2	3	5
1	2	2	4	2	1	1	1	1	1	4	3	1	2	4	2	2	2	2	1	1	1	1	1	2	2	3	1	5	4	3	7
1	1	3	4	2	2	2	1	1	3	1	3	5	1	2	4	2	2	1	1	1	1	1	1	2	3	3	3	2	2	3	6
1	2	2	3	2	1	1	1	1	2	1	1	2	1	2	3	2	3	3	1	1	1	1	1	2	2	3	2	3	3	3	6
2	2	2	4	2	2	2	1	1	2	1	3	4	1	2	4	3	1	1	2	1	1	1	2	3	2	3	1	5	3	3	6
1	2	2	4	2	2	2	1	1	1	2	2	3	1	2	1	2	2	2	2	1	1	2	1	3	2	3	2	3	3	3	7
1	2	2	4	2	1	2	1	1	3	1	5	4	1	2	2	3	3	1	1	1	1	1	1	2	2	2	2	4	1	3	7
2	2	1	2	2	1	2	2	1	1	5	4	3	1	4	3	2	3	2	1	1	1	3	2	2	1	2	3	2	2	4	4
1	2	1	2	2	2	1	2	2	2	6	5	2	1	4	2	2	2	2	2	1	2	3	1	2	2	2	2	1	1	4	7
2	1	2	4	1	1	2	1	1	1	1	3	4	1	2	4	2	3	3	1	1	2	1	1	3	3	2	2	2	2	4	4
2	2	2	4	2	2	2	1	1	1	1	3	4	1	2	4	1	2	2	1	3	1	1	1	3	3	3	2	4	4	4	3
2	1	2	3	1	2	1	1	2	1	1	1	2	1	2	3	1	1	1	1	3	1	1	1	2	3	1	2	4	2	5	4
3	2	2	3	1	2	2	1	1	2	1	2	4	1	2	3	2	2	2	1	1	1	1	1	3	3	2	3	5	4	5	3
2	2	2	4	1	2	1	2	1	1	3	1	3	1	2	3	2	2	3	1	1	1	1	1	3	3	2	3	4	3	5	7
2	2	3	3	2	2	2	3	1	1	4	4	2	1	1	2	1	2	2	1	1	1	3	1	3	2	3	3	1	2	5	7
3	2	3	3	1	2	1	3	1	2	4	2	5	3	2	1	1	2	2	1	1	1	3	3	3	3	3	3	5	4	5	7
1	2	2	5	2	2	2	2	1	1	1	1	5	1	2	1	1	1	1	1	1	1	1	1	3	2	3	3	4	3	5	4
2	2	2	4	2	2	2	1	4	2	3	3	2	1	4	1	2	2	2	1	1	1	1	1	3	2	3	2	5	4	5	5
2	2	2	3	2	1	2	2	1	1	3	4	2	1	1	1	2	2	2	1	1	1	2	1	2	2	2	2	1	1	6	6
1	2	2	4	2	1	1	1	1	1	4	4	1	1	3	2	2	2	2	1	1	1	1	2	3	1	2	3	5	3	6	6
2	2	2	3	2	2	2	1	1	1	3	5	3	1	2	2	2	2	2	1	1	2	1	1	3	1	3	3	3	2	6	6
2	1	2	3	2	1	1	1	2	3	3	3	1	1	4	2	2	2	2	1	1	1	1	1	2	3	2	3	4	2	6	6
2	2	2	5	1	1	1	1	1	2	4	1	5	1	2	3	2	2	2	1	1	1	1	1	2	3	3	2	5	4	6	6
1	2	2	3	2	2	2	1	1	1	3	4	4	1	3	2	3	2	2	1	1	1	1	1	3	2	3	3	2	2	6	7
1	2	2	1	1	2	1	1	1	2	1	2	4	1	2	3	5	2	1	1	1	1	1	1	3	3	3	2	2	2	6	4
2	2	2	3	2	2	1	1	1	1	3	2	5	1	2	5	2	2	3	1	1	1	1	1	3	2	2	3	5	4	6	6
1	2	3	5	1	1	1	1	2	3	3	4	1	1	2	1	2	2	3	1	1	1	1	1	3	2	2	1	4	3	7	5
1	2	2	4	2	1	1	1	2	3	2	2	3	1	4	2	2	3	2	1	1	1	1	1	3	2	3	2	2	3	7	7
1	2	2	4	1	2	2	1	1	3	3	3	4	1	2	3	2	2	2	1	1	1	1	1	3	2	2	1	3	3	7	6
1	2	2	4	2	1	2	1	4	2	2	4	2	1	2	5	2	3	2	1	1	1	1	1	3	1	2	1	4	4	7	7
2	2	2	3	2	2	2	1	4	2	3	4	2	1	2	1	3	2	2	1	1	1	1	1	3	2	3	2	2	2	7	7
1	2	2	4	2	2	2	1	2	3	4	1	2	1	3	3	2	2	2	1	1	1	1	1	2	2	2	1	3	3	7	6
1	2	2	4	2	2	1	2	1	3	2	3	1	1	2	4	2	2	2	1	1	1	1	1	3	3	2	1	2	3	7	7
1	2	2	3	2	2	1	1	1	2	3	1	1	2	4	4	2	2	2	2	1	1	1	1	3	1	2	1	3	4	7	7
1	2	1	4	2	2	1	5	1	2	1	3	4	1	2	1	2	3	2	1	1	1	1	1	2	2	3	2	4	4	7	7
1	1	2	3	2	2	2	1	2	2	4	4	1	1	3	2	2	2	2	1	1	1	1	1	2	3	2	2	1	1	7	3
1	2	2	4	1	1	2	1	1	1	2	2	4	1	2	4	2	3	2	1	1	2	1	2	2	1	3	1	3	3	7	7
1	2	2	4	2	1	2	1	1	2	2	2	1	1	2	1	3	2	2	1	1	1	3	2	2	2	2	1	4	4	7	7
1	1	2	4	1	1	2	1	1	3	1	3	5	1	2	4	2	2	2	1	1	1	2	1	2	3	2	1	4	2	7	6
2	1	1	5	2	1	2	2	2	1	1	2	1	3	1	5	3	3	2	1	1	2	1	1	2	3	1	2	3	3	7	6
1	2	1	3	2	1	1	1	2	2	4	4	2	3	2	5	3	2	2	1	1	1	2	2	3	2	3	1	4	4	7	7
2	2	2	3	2	2	2	2	4	2	4	4	2	2	1	5	2	2	2	1	1	2	1	1	2	2	2	1	2	3	8	2
1	1	1	5	2	1	2	1	1	2	3	3	1	1	3	2	1	2	2	2	2	1	1	1	3	1	3	3	4	3	8	2
2	1	3	3	1	2	2	1	3	3	1	1	2	1	2	1	1	1	1	2	3	1	2	1	3	3	3	1	4	2	8	2
2	1	3	3	2	2	2	1	4	2	4	4	1	1	3	2	2	2	3	2	3	2	2	1	2	2	1	1	1	2	8	1
2	1	2	5	1	1	2	4	1	1	1	1	1	1	2	1	2	2	2	2	2	1	1	1	2	2	1	2	1	1	8	2
2	1	2	5	1	2	1	1	1	1	1	1	2	1	2	2	3	3	2	2	3	1	2	2	3	2	3	2	2	2	8	1
2	1	2	5	2	2	2	1	1	1	1	1	5	1	2	4	3	3	3	2	1	1	1	1	3	3	3	1	2	3	8	1
3	1	1	3	2	1	2	1	1	2	4	4	1	1	1	3	2	2	3	2	1	2	1	1	2	3	2	1	2	3	8	1
1	2	2	5	2	1	1	1	4	2	1	2	3	1	4	4	1	2	2	1	1	1	3	1	3	3	2	2	3	3	8	1
2	1	2	4	2	1	2	1	1	2	3	3	2	3	2	5	5	2	2	1	1	1	1	2	2	2	3	1	3	3	8	2
2	1	1	3	1	1	1	2	2	3	3	3	2	1	3	4	1	1	1	1	1	2	2	1	3	3	3	2	2	2	8	1
2	1	2	3	1	1	1	1	2	1	1	1	5	3	2	4	5	3	3	1	3	1	2	1	1	1	1	1	1	1	8	0
1	2	2	5	2	1	2	1	1	1	3	3	2	1	2	1	2	1	2	1	2	1	2	1	3	2	2	1	4	3	8	2
2	1	3	3	2	2	2	3	1	1	3	2	4	1	1	1	2	2	3	2	1	1	1	1	3	3	2	1	1	1	8	1
1	1	2	4	1	1	1	1	1	3	2	3	2	1	4	3	4	2	2	2	1	1	2	2	3	3	2	1	3	3	9	3
1	1	2	5	1	1	2	1	1	3	1	1	5	1	2	3	3	2	3	2	1	1	1	2	3	2	3	1	1	3	9	2
1	1	1	4	1	1	1	3	2	3	4	6	2	1	4	3	4	2	3	1	1	1	2	1	3	3	3	1	2	2	9	3
1	1	2	4	2	2	2	1	4	3	3	2	2	1	3	4	2	1	1	2	1	1	1	1	3	2	2	1	2	2	9	1
1	1	2	4	2	1	1	1	4	3	3	4	2	1	4	2	2	1	1	2	1	1	1	1	3	2	2	1	2	2	9	0
1	1	2	3	1	1	2	1	1	1	1	1	3	1	2	4	4	2	3	2	1	1	2	2	3	2	3	2	2	3	9	3
1	1	2	3	1	1	2	1	1	1	1	2	3	1	2	4	4	2	2	2	1	1	1	2	3	1	3	2	2	3	9	1
1	1	1	5	2	1	2	1	2	2	4	3	1	1	3	2	4	1	3	2	1	1	1	2	3	2	3	1	5	3	9	4
1	1	1	5	2	1	2	1	1	2	1	2	3	1	2	1	2	2	3	2	1	1	2	1	3	2	3	1	5	4	9	3
1	1	2	5	2	2	1	1	1	1	1	1	1	1	2	3	3	1	2	2	1	1	1	1	3	3	3	2	5	3	9	3
1	1	2	4	2	1	2	1	2	3	1	1	2	1	4	3	2	2	2	2	1	1	1	1	2	1	2	1	2	3	9	1
2	1	2	3	1	1	2	1	4	2	3	3	1	1	2	1	2	3	2	1	3	1	1	1	3	3	2	1	3	2	9	2
1	1	2	3	1	1	1	1	2	3	3	3	3	1	3	4	2	2	2	2	1	1	3	1	3	2	2	1	2	2	9	0
1	1	1	5	2	1	2	1	1	1	2	2	2	1	2	1	2	2	2	1	1	1	1	1	3	1	3	1	2	4	9	2
1	1	2	4	1	1	1	5	2	3	3	1	1	2	4	5	2	2	3	2	3	1	2	1	3	2	3	1	1	3	9	0
1	1	2	4	1	2	1	2	2	3	3	3	1	1	5	4	1	1	2	2	1	2	2	1	2	3	2	1	1	2	9	0
2	1	2	3	1	1	2	1	1	2	1	2	2	2	2	4	3	3	2	1	1	1	1	1	2	1	2	1	3	3	9	5
1	1	2	4	2	2	2	1	4	2	1	1	5	1	2	1	3	2	2	2	1	2	1	1	3	2	2	1	5	3	9	5
1	1	1	4	2	2	2	1	1	1	3	4	4	1	2	4	2	2	2	1	1	1	1	1	3	3	2	1	4	3	9	1
2	1	2	4	1	1	1	5	2	3	4	4	1	1	3	3	2	2	1	1	1	1	2	1	2	1	2	1	5	3	9	4
1	1	1	5	2	2	2	3	1	1	3	1	5	1	2	4	3	1	1	1	1	1	2	1	3	2	3	1	5	4	9	3
];
size(D)

ans = 1×2

145 32

Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1));
D_centered = Cn*D

D_centered = 145×32

0.3793 0.4000 1.0552 -0.5724 -0.6621 0.4000 0.4207 -0.6276 -0.6207 -0.7310 -1.2828 -0.6345 0.1931 -0.1724 -0.3586 2.1931 0.8000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 -1.3931 0.1931 -2.1241 -1.7241 0.3793 0.4000 1.0552 -0.5724 -0.6621 0.4000 0.4207 -0.6276 -0.6207 -0.7310 -0.2828 0.3655 -0.8069 -0.1724 -0.3586 -1.8069 -0.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 0.6069 0.1931 -1.1241 0.2759 0.3793 0.4000 0.0552 -0.5724 0.3379 0.4000 0.4207 0.3724 2.3793 0.2690 -0.2828 -0.6345 -0.8069 -0.1724 -0.3586 -1.8069 -0.2000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 -0.5448 -0.0552 -1.3931 -0.8069 -1.1241 -0.7241 -0.6207 -0.6000 -0.9448 -0.5724 -0.6621 0.4000 -0.5793 0.3724 -0.6207 0.2690 -1.2828 -0.6345 2.1931 -0.1724 -0.3586 -1.8069 0.8000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 0.8345 0.4552 -0.0552 -0.3931 -0.8069 -0.1241 -0.7241 0.3793 0.4000 -0.9448 -0.5724 0.3379 0.4000 -0.5793 1.3724 -0.6207 2.2690 0.7172 0.3655 -0.8069 -0.1724 -0.3586 1.1931 -0.2000 -0.9448 -1.0138 -0.2138 -0.2069 -0.2414 0.6621 -0.1655 -0.5448 -0.0552 -0.3931 -0.8069 -1.1241 -0.7241 0.3793 0.4000 0.0552 -0.5724 0.3379 0.4000 0.4207 0.3724 -0.6207 -0.7310 0.7172 0.3655 -0.8069 -0.1724 -0.3586 0.1931 -1.2000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 -1.5448 -0.0552 -1.3931 0.1931 0.8759 1.2759 -0.6207 0.4000 0.0552 0.4276 0.3379 0.4000 0.4207 -0.6276 -0.6207 1.2690 -1.2828 0.3655 -1.8069 -0.1724 -0.3586 1.1931 -0.2000 0.0552 -0.0138 0.7862 -0.2069 0.7586 -0.3379 -0.1655 0.4552 0.9448 0.6069 1.1931 0.8759 1.2759 -0.6207 -0.6000 0.0552 -0.5724 -0.6621 -0.6000 -0.5793 0.3724 0.3793 1.2690 1.7172 0.3655 -1.8069 -0.1724 1.6414 0.1931 -1.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 1.6621 -0.1655 0.4552 -0.0552 -0.3931 -0.8069 -2.1241 -1.7241 0.3793 -0.6000 1.0552 -0.5724 0.3379 -0.6000 -0.5793 -0.6276 -0.6207 1.2690 -0.2828 1.3655 -0.8069 -0.1724 -0.3586 1.1931 -1.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 -0.3931 0.1931 0.8759 0.2759 0.3793 -0.6000 0.0552 -0.5724 0.3379 0.4000 -0.5793 1.3724 2.3793 0.2690 -1.2828 -0.6345 0.1931 -0.1724 -0.3586 0.1931 -0.2000 0.0552 -0.0138 -0.2138 -0.2069 0.7586 -0.3379 -0.1655 -0.5448 -0.0552 -0.3931 0.1931 -2.1241 -0.7241

results = mean(D_centered,1)

results = 1×32

1.0e-14 * -0.2090 -0.2439 -0.2494 -0.0298 -0.2616 -0.2421 -0.2315 -0.0441 -0.0300 -0.1562 -0.0937 -0.1495 0.0028 -0.0992 -0.2485 -0.0466 -0.1729 -0.2456 -0.2427 -0.1441 -0.0778 -0.1648 -0.1047 -0.1227 -0.1926 -0.2309 -0.2210 -0.1158 -0.0435 -0.1629

[Mustafa Furkan SAHIN]

     DATA=[2	2	3	3	1	2	2	1	1	1	1	2	3	1	2	5	3	2	2	1	1	1	1	1	3	2	1	2	1	1	1	1
2	2	3	3	1	2	2	1	1	1	2	3	2	1	2	1	2	2	2	1	1	1	1	1	3	2	3	2	2	3	1	1
2	2	2	3	2	2	2	2	4	2	2	2	2	1	2	1	2	1	2	1	1	1	1	1	2	2	1	1	2	2	1	1
1	1	1	3	1	2	1	2	1	2	1	2	5	1	2	1	3	1	2	1	1	1	1	2	3	2	2	1	3	2	1	1
2	2	1	3	2	2	1	3	1	4	3	3	2	1	2	4	2	1	1	1	1	1	2	1	2	2	2	1	2	2	1	1
2	2	2	3	2	2	2	2	1	1	3	3	2	1	2	3	1	1	2	1	1	1	1	1	1	2	1	2	4	4	1	2
1	2	2	4	2	2	2	1	1	3	1	3	1	1	2	4	2	2	2	2	1	2	1	1	3	3	3	3	4	4	1	5
1	1	2	3	1	1	1	2	2	3	4	3	1	1	4	3	1	2	2	1	1	1	3	1	3	2	2	1	1	1	1	2
2	1	3	3	2	1	1	1	1	3	2	4	2	1	2	4	1	2	2	1	1	1	1	1	3	2	2	2	4	3	1	5
2	1	2	3	2	2	1	3	4	2	1	2	3	1	2	3	2	2	2	1	1	2	1	1	2	2	2	2	1	2	1	0
1	1	1	3	2	2	2	3	2	3	3	4	2	1	3	2	1	1	1	1	1	2	1	1	2	2	2	2	1	1	1	2
1	1	1	4	1	1	2	4	2	3	5	5	1	1	3	2	3	3	3	1	3	1	3	2	3	1	3	3	4	3	1	0
1	1	1	4	2	2	2	1	1	1	3	5	4	2	2	2	3	2	2	1	1	1	1	1	2	2	2	3	4	2	1	0
2	1	2	5	2	2	2	1	1	1	2	2	2	1	2	3	1	2	1	1	1	2	1	1	3	2	3	3	4	2	1	1
3	2	2	4	1	1	2	3	4	2	3	1	2	1	4	1	2	2	2	1	1	1	1	1	2	3	2	1	4	4	1	2
2	2	2	3	2	2	2	1	1	2	4	4	2	1	3	1	2	2	2	2	3	2	1	1	3	2	2	3	2	2	1	2
1	1	2	5	2	1	2	1	1	1	2	2	4	1	2	3	2	2	2	1	1	2	1	1	3	2	3	3	4	3	1	1
2	2	2	3	2	2	2	1	1	1	2	2	2	1	2	1	2	2	2	2	1	2	1	1	2	2	2	2	2	2	1	2
1	1	2	4	2	2	2	3	1	1	2	2	5	1	2	5	5	3	2	2	1	2	1	1	3	1	3	3	3	3	1	2
1	2	1	3	2	2	1	2	2	2	3	3	3	1	2	4	4	2	2	1	2	1	1	1	3	2	2	3	2	3	1	3
1	2	2	5	1	2	1	1	4	2	3	3	3	2	2	3	4	2	2	2	1	1	1	2	3	1	2	3	4	4	1	1
1	2	2	5	2	2	1	1	4	2	2	2	4	1	2	3	3	2	2	1	1	1	1	1	3	1	3	3	3	3	1	1
2	2	2	3	1	2	1	1	1	1	1	1	4	2	2	4	3	3	3	1	1	1	1	2	3	1	2	3	3	3	1	3
3	2	2	2	1	1	2	5	1	1	1	4	3	1	2	1	3	2	3	1	1	2	1	1	3	2	3	3	3	3	1	1
2	2	2	3	2	2	2	2	1	1	3	1	3	1	2	4	1	2	2	1	1	1	1	1	2	1	3	2	4	4	1	2
2	2	2	3	2	2	1	1	1	2	1	4	3	1	2	4	2	2	2	1	1	1	1	1	2	1	3	2	1	2	1	3
2	2	2	3	2	1	1	1	1	1	4	4	4	1	2	4	2	3	2	1	1	1	1	1	3	3	3	2	2	1	1	1
1	2	1	3	1	2	2	1	1	1	1	1	4	1	2	3	3	2	2	1	1	2	1	1	3	1	2	1	2	1	1	1
3	2	2	3	2	2	1	1	4	2	2	2	4	1	2	1	2	2	2	1	1	1	1	1	3	2	3	3	5	4	1	3
2	2	3	4	2	2	2	1	4	2	3	3	2	1	4	1	2	2	2	1	1	1	1	1	3	2	2	2	4	3	1	5
2	2	2	5	1	1	1	1	1	2	1	1	5	1	2	4	2	2	2	1	1	1	2	1	2	3	3	3	5	4	1	5
3	2	2	3	1	2	2	1	1	2	1	2	4	1	2	3	2	2	3	1	1	1	1	1	3	3	2	3	4	3	1	3
2	1	2	3	2	2	2	2	1	1	2	2	2	1	2	1	2	2	2	1	1	2	1	1	2	2	2	1	2	3	1	1
2	1	2	3	1	2	1	1	1	1	1	2	5	1	2	3	2	1	2	1	1	1	1	1	1	3	2	2	2	3	1	2
1	2	1	3	2	2	1	2	1	1	3	3	3	1	2	4	2	1	1	1	1	2	1	1	2	2	3	1	4	1	1	2
1	2	1	4	2	2	2	3	1	1	1	2	5	1	2	3	2	2	2	1	1	2	1	1	2	1	3	1	1	1	1	1
2	2	3	4	1	2	1	3	1	1	1	1	2	2	4	3	1	1	3	1	1	2	2	1	2	1	2	1	4	3	1	2
2	2	2	3	1	1	1	2	2	3	3	3	1	1	2	3	2	2	2	1	1	1	1	1	3	2	3	3	5	4	1	1
2	2	2	5	2	2	2	1	1	1	3	3	1	1	2	4	3	2	2	1	1	1	2	1	2	2	2	2	4	3	1	2
2	1	2	3	2	2	1	1	1	2	1	3	1	1	2	4	1	2	1	1	1	1	1	1	2	2	2	3	2	1	1	1
1	2	1	3	2	2	2	2	1	1	2	3	1	1	2	5	1	2	2	1	1	1	1	1	3	1	2	1	2	3	1	1
3	2	2	3	1	2	2	2	1	2	1	4	2	1	2	2	3	2	2	1	1	2	1	1	3	2	2	3	4	3	1	1
2	2	2	3	2	1	2	1	4	2	4	4	2	1	3	1	2	2	1	1	1	1	1	1	2	1	3	1	2	3	1	1
1	2	2	3	2	2	1	1	1	1	2	3	3	1	2	1	2	2	2	1	1	2	1	1	3	1	3	3	3	2	1	4
2	2	3	3	2	2	1	1	1	1	1	3	2	1	2	5	2	2	2	1	1	1	2	1	3	2	2	1	4	3	1	1
1	2	2	3	2	2	1	4	1	1	2	3	5	1	2	4	3	2	2	1	1	1	1	1	2	2	2	1	4	3	1	3
2	2	2	3	2	2	1	1	1	1	1	2	3	1	2	5	2	2	2	1	1	1	1	1	2	2	3	1	4	2	1	5
2	2	2	3	2	2	1	1	1	2	4	3	1	1	5	4	2	2	2	1	1	2	1	1	2	1	3	2	5	3	1	3
1	2	2	3	2	1	1	1	1	2	3	3	3	1	2	2	1	1	2	1	1	1	1	1	3	2	3	2	2	2	1	1
1	2	1	4	2	2	2	2	4	1	2	3	2	1	2	4	2	2	2	1	1	1	2	1	2	2	2	1	3	2	1	2
2	2	2	3	2	2	1	2	2	1	1	1	2	1	2	3	2	2	2	1	1	2	1	1	2	2	2	3	3	3	1	1
2	1	3	3	1	1	2	1	1	1	1	2	5	1	2	1	1	2	2	1	1	2	1	1	2	3	3	1	3	3	1	4
2	1	2	3	1	2	2	2	1	1	1	3	4	1	2	1	3	1	1	1	1	1	1	2	2	2	2	1	4	2	1	1
2	1	2	3	2	1	2	2	1	1	1	1	5	1	2	1	1	2	2	1	1	2	1	1	2	2	2	2	3	2	1	5
2	2	2	3	2	2	2	3	4	2	1	4	2	1	4	2	2	1	1	1	1	1	1	2	3	1	3	1	5	3	1	3
3	2	2	3	1	2	1	4	1	2	1	1	5	3	2	1	1	2	2	1	1	1	3	3	1	3	3	3	5	4	1	3
2	2	2	3	2	1	2	1	1	1	1	3	5	1	2	4	4	2	3	1	1	1	1	2	3	2	3	3	5	4	1	5
2	2	2	3	1	1	2	1	1	1	4	2	3	1	3	3	2	3	2	1	1	1	1	1	3	2	3	1	5	4	1	4
3	2	2	3	2	2	1	3	1	1	1	3	5	2	2	1	3	2	2	1	1	1	1	1	3	2	2	2	5	4	1	3
2	2	2	3	2	1	1	1	1	1	4	4	3	1	2	3	3	2	2	1	1	1	1	1	3	3	2	2	4	3	1	5
2	1	2	3	2	2	2	5	2	1	6	1	3	1	2	3	1	2	2	1	1	1	1	1	1	3	3	1	2	1	1	2
1	2	3	3	2	1	2	2	1	2	1	3	4	1	2	3	3	1	2	1	3	2	1	1	3	2	2	2	4	4	1	5
2	2	2	3	2	2	2	1	4	2	2	3	2	1	2	4	3	2	2	1	1	1	1	1	2	3	3	2	5	4	1	3
2	2	2	4	2	2	1	2	1	1	1	3	3	1	2	1	1	1	2	1	1	1	3	1	2	2	2	1	3	3	1	5
2	2	3	5	2	2	2	1	1	1	2	2	2	1	2	1	1	2	2	1	3	1	2	1	3	2	3	1	4	3	1	3
1	2	2	3	2	2	1	3	1	1	2	4	3	1	2	4	1	1	2	1	3	2	2	1	2	1	2	1	2	2	1	2
2	2	2	3	2	2	1	1	1	1	3	2	5	1	2	5	2	2	2	1	1	1	1	1	3	2	2	3	5	4	2	5
2	2	3	3	1	1	2	3	1	2	3	4	4	2	2	2	2	2	2	1	1	2	1	1	2	2	1	2	2	3	2	1
2	1	2	4	1	2	2	1	1	1	2	3	5	3	2	5	1	2	2	1	1	2	1	1	2	2	3	2	4	3	3	5
2	1	2	4	2	2	1	1	1	1	2	2	2	1	2	3	1	1	2	1	1	1	2	1	3	2	3	1	3	2	3	5
1	2	2	4	2	1	1	1	1	1	4	3	1	2	4	2	2	2	2	1	1	1	1	1	2	2	3	1	5	4	3	7
1	1	3	4	2	2	2	1	1	3	1	3	5	1	2	4	2	2	1	1	1	1	1	1	2	3	3	3	2	2	3	6
1	2	2	3	2	1	1	1	1	2	1	1	2	1	2	3	2	3	3	1	1	1	1	1	2	2	3	2	3	3	3	6
2	2	2	4	2	2	2	1	1	2	1	3	4	1	2	4	3	1	1	2	1	1	1	2	3	2	3	1	5	3	3	6
1	2	2	4	2	2	2	1	1	1	2	2	3	1	2	1	2	2	2	2	1	1	2	1	3	2	3	2	3	3	3	7
1	2	2	4	2	1	2	1	1	3	1	5	4	1	2	2	3	3	1	1	1	1	1	1	2	2	2	2	4	1	3	7
2	2	1	2	2	1	2	2	1	1	5	4	3	1	4	3	2	3	2	1	1	1	3	2	2	1	2	3	2	2	4	4
1	2	1	2	2	2	1	2	2	2	6	5	2	1	4	2	2	2	2	2	1	2	3	1	2	2	2	2	1	1	4	7
2	1	2	4	1	1	2	1	1	1	1	3	4	1	2	4	2	3	3	1	1	2	1	1	3	3	2	2	2	2	4	4
2	2	2	4	2	2	2	1	1	1	1	3	4	1	2	4	1	2	2	1	3	1	1	1	3	3	3	2	4	4	4	3
2	1	2	3	1	2	1	1	2	1	1	1	2	1	2	3	1	1	1	1	3	1	1	1	2	3	1	2	4	2	5	4
3	2	2	3	1	2	2	1	1	2	1	2	4	1	2	3	2	2	2	1	1	1	1	1	3	3	2	3	5	4	5	3
2	2	2	4	1	2	1	2	1	1	3	1	3	1	2	3	2	2	3	1	1	1	1	1	3	3	2	3	4	3	5	7
2	2	3	3	2	2	2	3	1	1	4	4	2	1	1	2	1	2	2	1	1	1	3	1	3	2	3	3	1	2	5	7
3	2	3	3	1	2	1	3	1	2	4	2	5	3	2	1	1	2	2	1	1	1	3	3	3	3	3	3	5	4	5	7
1	2	2	5	2	2	2	2	1	1	1	1	5	1	2	1	1	1	1	1	1	1	1	1	3	2	3	3	4	3	5	4
2	2	2	4	2	2	2	1	4	2	3	3	2	1	4	1	2	2	2	1	1	1	1	1	3	2	3	2	5	4	5	5
2	2	2	3	2	1	2	2	1	1	3	4	2	1	1	1	2	2	2	1	1	1	2	1	2	2	2	2	1	1	6	6
1	2	2	4	2	1	1	1	1	1	4	4	1	1	3	2	2	2	2	1	1	1	1	2	3	1	2	3	5	3	6	6
2	2	2	3	2	2	2	1	1	1	3	5	3	1	2	2	2	2	2	1	1	2	1	1	3	1	3	3	3	2	6	6
2	1	2	3	2	1	1	1	2	3	3	3	1	1	4	2	2	2	2	1	1	1	1	1	2	3	2	3	4	2	6	6
2	2	2	5	1	1	1	1	1	2	4	1	5	1	2	3	2	2	2	1	1	1	1	1	2	3	3	2	5	4	6	6
1	2	2	3	2	2	2	1	1	1	3	4	4	1	3	2	3	2	2	1	1	1	1	1	3	2	3	3	2	2	6	7
1	2	2	1	1	2	1	1	1	2	1	2	4	1	2	3	5	2	1	1	1	1	1	1	3	3	3	2	2	2	6	4
2	2	2	3	2	2	1	1	1	1	3	2	5	1	2	5	2	2	3	1	1	1	1	1	3	2	2	3	5	4	6	6
1	2	3	5	1	1	1	1	2	3	3	4	1	1	2	1	2	2	3	1	1	1	1	1	3	2	2	1	4	3	7	5
1	2	2	4	2	1	1	1	2	3	2	2	3	1	4	2	2	3	2	1	1	1	1	1	3	2	3	2	2	3	7	7
1	2	2	4	1	2	2	1	1	3	3	3	4	1	2	3	2	2	2	1	1	1	1	1	3	2	2	1	3	3	7	6
1	2	2	4	2	1	2	1	4	2	2	4	2	1	2	5	2	3	2	1	1	1	1	1	3	1	2	1	4	4	7	7
2	2	2	3	2	2	2	1	4	2	3	4	2	1	2	1	3	2	2	1	1	1	1	1	3	2	3	2	2	2	7	7
1	2	2	4	2	2	2	1	2	3	4	1	2	1	3	3	2	2	2	1	1	1	1	1	2	2	2	1	3	3	7	6
1	2	2	4	2	2	1	2	1	3	2	3	1	1	2	4	2	2	2	1	1	1	1	1	3	3	2	1	2	3	7	7
1	2	2	3	2	2	1	1	1	2	3	1	1	2	4	4	2	2	2	2	1	1	1	1	3	1	2	1	3	4	7	7
1	2	1	4	2	2	1	5	1	2	1	3	4	1	2	1	2	3	2	1	1	1	1	1	2	2	3	2	4	4	7	7
1	1	2	3	2	2	2	1	2	2	4	4	1	1	3	2	2	2	2	1	1	1	1	1	2	3	2	2	1	1	7	3
1	2	2	4	1	1	2	1	1	1	2	2	4	1	2	4	2	3	2	1	1	2	1	2	2	1	3	1	3	3	7	7
1	2	2	4	2	1	2	1	1	2	2	2	1	1	2	1	3	2	2	1	1	1	3	2	2	2	2	1	4	4	7	7
1	1	2	4	1	1	2	1	1	3	1	3	5	1	2	4	2	2	2	1	1	1	2	1	2	3	2	1	4	2	7	6
2	1	1	5	2	1	2	2	2	1	1	2	1	3	1	5	3	3	2	1	1	2	1	1	2	3	1	2	3	3	7	6
1	2	1	3	2	1	1	1	2	2	4	4	2	3	2	5	3	2	2	1	1	1	2	2	3	2	3	1	4	4	7	7
2	2	2	3	2	2	2	2	4	2	4	4	2	2	1	5	2	2	2	1	1	2	1	1	2	2	2	1	2	3	8	2
1	1	1	5	2	1	2	1	1	2	3	3	1	1	3	2	1	2	2	2	2	1	1	1	3	1	3	3	4	3	8	2
2	1	3	3	1	2	2	1	3	3	1	1	2	1	2	1	1	1	1	2	3	1	2	1	3	3	3	1	4	2	8	2
2	1	3	3	2	2	2	1	4	2	4	4	1	1	3	2	2	2	3	2	3	2	2	1	2	2	1	1	1	2	8	1
2	1	2	5	1	1	2	4	1	1	1	1	1	1	2	1	2	2	2	2	2	1	1	1	2	2	1	2	1	1	8	2
2	1	2	5	1	2	1	1	1	1	1	1	2	1	2	2	3	3	2	2	3	1	2	2	3	2	3	2	2	2	8	1
2	1	2	5	2	2	2	1	1	1	1	1	5	1	2	4	3	3	3	2	1	1	1	1	3	3	3	1	2	3	8	1
3	1	1	3	2	1	2	1	1	2	4	4	1	1	1	3	2	2	3	2	1	2	1	1	2	3	2	1	2	3	8	1
1	2	2	5	2	1	1	1	4	2	1	2	3	1	4	4	1	2	2	1	1	1	3	1	3	3	2	2	3	3	8	1
2	1	2	4	2	1	2	1	1	2	3	3	2	3	2	5	5	2	2	1	1	1	1	2	2	2	3	1	3	3	8	2
2	1	1	3	1	1	1	2	2	3	3	3	2	1	3	4	1	1	1	1	1	2	2	1	3	3	3	2	2	2	8	1
2	1	2	3	1	1	1	1	2	1	1	1	5	3	2	4	5	3	3	1	3	1	2	1	1	1	1	1	1	1	8	0
1	2	2	5	2	1	2	1	1	1	3	3	2	1	2	1	2	1	2	1	2	1	2	1	3	2	2	1	4	3	8	2
2	1	3	3	2	2	2	3	1	1	3	2	4	1	1	1	2	2	3	2	1	1	1	1	3	3	2	1	1	1	8	1
1	1	2	4	1	1	1	1	1	3	2	3	2	1	4	3	4	2	2	2	1	1	2	2	3	3	2	1	3	3	9	3
1	1	2	5	1	1	2	1	1	3	1	1	5	1	2	3	3	2	3	2	1	1	1	2	3	2	3	1	1	3	9	2
1	1	1	4	1	1	1	3	2	3	4	6	2	1	4	3	4	2	3	1	1	1	2	1	3	3	3	1	2	2	9	3
1	1	2	4	2	2	2	1	4	3	3	2	2	1	3	4	2	1	1	2	1	1	1	1	3	2	2	1	2	2	9	1
1	1	2	4	2	1	1	1	4	3	3	4	2	1	4	2	2	1	1	2	1	1	1	1	3	2	2	1	2	2	9	0
1	1	2	3	1	1	2	1	1	1	1	1	3	1	2	4	4	2	3	2	1	1	2	2	3	2	3	2	2	3	9	3
1	1	2	3	1	1	2	1	1	1	1	2	3	1	2	4	4	2	2	2	1	1	1	2	3	1	3	2	2	3	9	1
1	1	1	5	2	1	2	1	2	2	4	3	1	1	3	2	4	1	3	2	1	1	1	2	3	2	3	1	5	3	9	4
1	1	1	5	2	1	2	1	1	2	1	2	3	1	2	1	2	2	3	2	1	1	2	1	3	2	3	1	5	4	9	3
1	1	2	5	2	2	1	1	1	1	1	1	1	1	2	3	3	1	2	2	1	1	1	1	3	3	3	2	5	3	9	3
1	1	2	4	2	1	2	1	2	3	1	1	2	1	4	3	2	2	2	2	1	1	1	1	2	1	2	1	2	3	9	1
2	1	2	3	1	1	2	1	4	2	3	3	1	1	2	1	2	3	2	1	3	1	1	1	3	3	2	1	3	2	9	2
1	1	2	3	1	1	1	1	2	3	3	3	3	1	3	4	2	2	2	2	1	1	3	1	3	2	2	1	2	2	9	0
1	1	1	5	2	1	2	1	1	1	2	2	2	1	2	1	2	2	2	1	1	1	1	1	3	1	3	1	2	4	9	2
1	1	2	4	1	1	1	5	2	3	3	1	1	2	4	5	2	2	3	2	3	1	2	1	3	2	3	1	1	3	9	0
1	1	2	4	1	2	1	2	2	3	3	3	1	1	5	4	1	1	2	2	1	2	2	1	2	3	2	1	1	2	9	0
2	1	2	3	1	1	2	1	1	2	1	2	2	2	2	4	3	3	2	1	1	1	1	1	2	1	2	1	3	3	9	5
1	1	2	4	2	2	2	1	4	2	1	1	5	1	2	1	3	2	2	2	1	2	1	1	3	2	2	1	5	3	9	5
1	1	1	4	2	2	2	1	1	1	3	4	4	1	2	4	2	2	2	1	1	1	1	1	3	3	2	1	4	3	9	1
2	1	2	4	1	1	1	5	2	3	4	4	1	1	3	3	2	2	1	1	1	1	2	1	2	1	2	1	5	3	9	4
1	1	1	5	2	2	2	3	1	1	3	1	5	1	2	4	3	1	1	1	1	1	2	1	3	2	3	1	5	4	9	3
];
     DATA(:,32)=[]; %I took out the output note because I'm trying to find centered data matrix
     meanDATA=mean(DATA); %I got mean from the data set
     DATAnorm=DATA-meanDATA; %then I subtracted it from the data
     newmean=mean(DATAnorm); %again I need to take mean of the new dataset called DATAnorm
     scatter(DATA,meanDATA,'red');
     hold on
     title('Blue is scatter of centered data matrix','Purple is old data matrix');
     scatter(DATAnorm,newmean,'blue');
     hold off

so is this code correct?

[Torsten]

Yes, it's correct. See above.

[Mustafa Furkan SAHIN]

That's great. Thanks for taking the time,Torsten

Answer 2

[Steven Lord]

I would use the normalize function or the Normalize Data task in Live Editor.

[Torsten]

It's only asked for mean 0, not standard deviation 1. Is this also possible with "normalize" ?

[Steven Lord]

The normalize function can center without scaling:

format longg
x = rand(1, 10);
n = normalize(x, 'center');
[mean(n), std(n)]

ans = 1×2

-1.11022302462516e-17 0.266647564148699

scale without centering:

s = normalize(x, 'scale');
[mean(s), std(s)]

ans = 1×2

2.19316158923735 1

or both center and scale.

z = normalize(x); % 'zscore' is the default
[mean(z), std(z)]

ans = 1×2

-5.55111512312578e-18 1

There are other normalization options as well.