Data Analysis - Matlab Code

Question

Mustafa Furkan SAHIN on 11 Dec 2022

0
Link

Direct link to this question

https://se.mathworks.com/matlabcentral/answers/1876017-data-analysis-matlab-code

Commented: Mustafa Furkan SAHIN on 13 Dec 2022

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

1 Comment
Show -1 older commentsHide -1 older comments

Mustafa Furkan SAHIN on 11 Dec 2022

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?

Sign in to comment.

Sign in to answer this question.

Answer 1

Torsten on 11 Dec 2022

1
Link

Direct link to this answer

https://se.mathworks.com/matlabcentral/answers/1876017-data-analysis-matlab-code#answer_1125727

Open in MATLAB Online

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

10 Comments
Show 8 older commentsHide 8 older comments

Torsten on 13 Dec 2022

Open in MATLAB Online

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	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	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	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	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	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
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	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
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
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	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	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	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
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
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	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	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	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	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
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
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
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	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
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	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	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	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
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
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	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	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
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
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
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
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
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	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	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	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
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
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
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	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
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	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
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	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	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
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
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	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
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
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
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	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
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	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	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
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	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
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
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	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	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	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
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	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	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
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
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
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	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
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	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
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
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	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
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
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	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
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
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	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	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
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
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	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	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
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	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
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	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
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
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	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
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
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
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
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	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
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
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
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
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	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
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
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	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
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
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	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	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
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
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
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
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
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
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
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
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
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
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
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
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	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	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	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	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	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	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	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	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	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	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	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
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	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	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	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	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
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	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	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
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	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
  32
Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1));
D_centered = Cn*D
D_centered = 145×32
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
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
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
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
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
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
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
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 on 13 Dec 2022

Edited: Torsten on 13 Dec 2022

Open in MATLAB Online

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 on 13 Dec 2022

Yes, it's correct. See above.

Mustafa Furkan SAHIN on 13 Dec 2022

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

Sign in to comment.

Answer 2

Steven Lord on 12 Dec 2022

0
Link

Direct link to this answer

https://se.mathworks.com/matlabcentral/answers/1876017-data-analysis-matlab-code#answer_1126367

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

2 Comments
Show NoneHide None

Torsten on 12 Dec 2022

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

Steven Lord on 12 Dec 2022

Open in MATLAB Online

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.

Sign in to comment.

Data Analysis - Matlab Code

1 Comment
Show -1 older commentsHide -1 older comments

Accepted Answer

10 Comments
Show 8 older commentsHide 8 older comments

More Answers (1)

2 Comments
Show NoneHide None

See Also

Categories

Tags

Products

Release

Community Treasure Hunt

Data Analysis - Matlab Code

1 Comment Show -1 older commentsHide -1 older comments

Accepted Answer

10 Comments Show 8 older commentsHide 8 older comments

More Answers (1)

2 Comments Show NoneHide None

See Also

Categories

Tags

Products

Release

Community Treasure Hunt

1 Comment
Show -1 older commentsHide -1 older comments

10 Comments
Show 8 older commentsHide 8 older comments

2 Comments
Show NoneHide None