Adding Means to a matrix

I have 500*30 data matrix woith computed neans means 1*30 matrix . is there a way i can add all the column means in the latter to all the elements in the former. please help

Answer 1

[Voss]

Like this?

D = rand(500,30); % data
M = mean(D,1);    % mean of each column of data
whos D M

  Name        Size             Bytes  Class     Attributes

  D         500x30            120000  double              
  M           1x30               240  double              

% add each column to its mean:
result = D+M

result = 500x30

1.3120 0.9836 1.0394 0.7667 0.9814 0.8004 0.9753 0.6494 1.2657 0.5960 0.6222 1.4424 1.3174 1.1643 1.1265 0.7037 0.9213 0.9340 1.2963 1.0443 0.9834 0.5769 1.1242 0.5363 0.6212 0.8625 1.1283 1.1723 0.6862 0.6154 0.9852 0.5541 0.5909 1.3173 1.2458 1.0429 1.0127 0.8964 0.9000 0.9453 1.2488 0.8662 0.9984 0.8365 0.6174 0.9993 1.2898 0.9565 0.8885 0.5414 0.7077 0.8411 1.1811 0.6820 1.2467 0.7416 1.1344 1.1624 1.1259 1.3528 1.3350 0.8911 1.4933 0.9698 0.9478 0.6346 1.0940 0.5987 1.0179 1.0424 0.9792 1.3247 0.7250 1.4247 1.4086 1.1145 1.4746 0.6653 1.3605 0.6518 1.3063 1.4197 0.9216 1.1212 0.8123 0.9358 0.5731 0.8654 1.3047 0.8543 0.8401 1.2081 0.6872 0.5464 1.3056 1.3332 0.6344 1.0987 0.8508 0.8427 1.0010 1.3674 0.7532 0.8286 1.2486 0.7172 0.9975 0.8890 0.7491 0.7753 0.5483 0.7889 0.9812 0.7461 0.6505 0.7486 1.1513 0.9585 1.0650 0.8361 1.5055 1.4685 0.5741 1.0606 1.4039 0.7600 1.3011 1.3747 0.8826 1.4895 0.9448 0.7519 1.0620 1.0039 1.3561 0.7855 1.3943 0.6000 1.2513 0.6094 1.2646 1.0534 1.0667 1.3392 1.2012 0.7724 1.4248 1.4145 0.9847 0.5737 1.1253 0.6324 0.5180 1.3628 0.9043 1.4413 0.6683 1.1466 0.5966 1.1236 0.9366 1.1199 0.8404 0.5605 1.2325 0.9307 0.9865 1.1496 1.3045 0.8823 0.5675 0.5186 1.0427 1.1673 1.1042 1.4839 1.0412 1.3252 1.2867 0.9163 1.1311 0.8907 1.0784 1.3239 1.4506 1.4177 0.5801 0.8631 0.7527 0.8415 1.2373 1.0401 1.4399 0.8119 0.7225 0.6026 1.4024 0.6272 1.0162 0.5957 1.0932 1.1936 0.9376 1.1012 0.8544 1.1679 0.4956 0.7298 0.5985 1.3618 1.1360 0.9975 1.2324 1.2314 1.4730 1.1757 0.7307 1.4316 1.2189 0.5458 1.0379 0.5405 0.6956 0.5078 1.3944 0.6476 0.9721 1.3996 0.5584 1.1030 0.9968 0.8026 1.0379 1.1317 0.7286 0.9263 1.1791 1.2647 0.6494 1.3204 0.9394 0.5497 1.0721 1.2309 0.5532 1.1609 1.3285 0.7922 0.9332 0.8536 1.1920 1.2420 0.9282 0.9045 0.8093 1.3166 1.4909 0.9111 0.8105 0.7374 0.8272 0.7439 0.5558 1.3502 1.0819 1.4941 0.8443 0.5287 0.8577 0.7865 1.0862 0.8086 0.8009 0.9057 0.5128 1.1596 0.9445 0.5409 0.5257 1.0469 0.5941 1.2461 0.6806 1.3322 0.7766 1.4211 0.6462 1.3683 0.8177 0.5827 0.9178 1.1176 0.8053 1.3772 0.5721 1.2862 0.7199 0.5641 1.0340 0.9563

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Or like this?

% append means to the bottom of the matrix:
result = [D; M]

result = 501x30

0.8061 0.4732 0.5314 0.2704 0.4757 0.2781 0.4934 0.1498 0.7604 0.0999 0.1189 0.9217 0.8366 0.6618 0.6255 0.2076 0.4107 0.4513 0.7922 0.5535 0.4684 0.0929 0.6190 0.0367 0.1081 0.3574 0.6624 0.6813 0.1686 0.1142 0.4793 0.0438 0.0829 0.8211 0.7402 0.5206 0.5308 0.3967 0.3946 0.4492 0.7454 0.3455 0.5176 0.3340 0.1164 0.5031 0.7793 0.4738 0.3844 0.0506 0.1927 0.3571 0.6759 0.1824 0.7336 0.2365 0.6685 0.6714 0.6083 0.8517 0.8292 0.3808 0.9853 0.4735 0.4422 0.1123 0.6121 0.0990 0.5125 0.5463 0.4758 0.8040 0.2443 0.9222 0.9076 0.6184 0.9640 0.1826 0.8564 0.1611 0.7913 0.9357 0.4164 0.6216 0.2992 0.4307 0.1072 0.3743 0.7871 0.3531 0.3342 0.6978 0.1792 0.0502 0.8000 0.8108 0.1525 0.5991 0.3455 0.3466 0.4976 0.8467 0.2725 0.3261 0.7476 0.2210 0.4869 0.4063 0.2450 0.2846 0.0333 0.3049 0.4760 0.2465 0.1374 0.2435 0.6854 0.4674 0.5475 0.3349 0.9996 0.9581 0.0661 0.5644 0.8982 0.2377 0.8193 0.8750 0.3772 0.9934 0.4414 0.2312 0.5813 0.5014 0.8551 0.2894 0.8837 0.1173 0.7472 0.1187 0.7495 0.5695 0.5615 0.8396 0.6881 0.2673 0.9589 0.9234 0.4672 0.0725 0.6194 0.1220 0.0100 0.8665 0.3987 0.9190 0.1864 0.6470 0.0912 0.6274 0.4332 0.5992 0.3596 0.0580 0.7315 0.4345 0.4760 0.6669 0.8004 0.3916 0.0524 0.0346 0.5376 0.6677 0.5911 0.9788 0.5753 0.8342 0.7691 0.4151 0.6252 0.3803 0.5704 0.8277 0.9449 0.8954 0.0982 0.3635 0.2474 0.3454 0.7339 0.5194 0.9591 0.3094 0.2215 0.1065 0.8918 0.1445 0.5121 0.1049 0.5781 0.7096 0.4324 0.6016 0.3413 0.6628 0.0297 0.2387 0.0810 0.8606 0.6301 0.4871 0.7244 0.7351 0.9674 0.6534 0.2488 0.9320 0.7135 0.0497 0.5345 0.0198 0.2149 0.0053 0.8934 0.1514 0.4616 0.9169 0.0543 0.6123 0.4818 0.3187 0.5328 0.6321 0.2156 0.4212 0.7132 0.7736 0.1319 0.8192 0.4335 0.0393 0.5641 0.7347 0.0475 0.6386 0.8466 0.2925 0.4278 0.3575 0.6886 0.7213 0.4475 0.4020 0.3083 0.8204 0.9804 0.4284 0.3064 0.2466 0.3122 0.2599 0.0506 0.8506 0.5688 0.9890 0.3785 0.0377 0.3401 0.2853 0.5803 0.2983 0.2928 0.4094 0.0072 0.6372 0.4626 0.0412 0.0204 0.5508 0.0907 0.7254 0.1998 0.8297 0.2756 0.9250 0.1357 0.8855 0.3136 0.0920 0.4028 0.6336 0.3002 0.8776 0.0591 0.7811 0.2540 0.0730 0.5164 0.4551

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