Data Analysis - Matlab Code

Question

Mustafa Furkan SAHIN el 11 de Dic. de 2022

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Comentada: Mustafa Furkan SAHIN el 13 de Dic. de 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

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Mustafa Furkan SAHIN el 11 de Dic. de 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?

Iniciar sesión para comentar.

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Answer 1

Torsten el 11 de Dic. de 2022

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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

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Torsten el 13 de Dic. de 2022

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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 el 13 de Dic. de 2022

Editada: Torsten el 13 de Dic. de 2022

Abrir en 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 el 13 de Dic. de 2022

Yes, it's correct. See above.

Mustafa Furkan SAHIN el 13 de Dic. de 2022

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

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Answer 2

Steven Lord el 12 de Dic. de 2022

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https://es.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.

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Torsten el 12 de Dic. de 2022

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

Steven Lord el 12 de Dic. de 2022

Abrir en 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.

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