我想平均数据 但是,数据的长度彼此不同 数据的数量是七 我如何通过matlab解决这个问题?
x_1= 6.45805700000000
6.45805700000000
6.53780000000000
6.57767200000000
6.71722200000000
6.87670800000000
7.17574400000000
7.43490900000000
7.81368900000000
8.31208300000000
8.79054100000000
9.42848500000000
10.0066220000000
10.7442450000000
11.5018040000000
12.2992340000000
13.1166000000000
13.9140310000000
14.7313970000000
15.5088910000000
16.2465140000000
16.9243300000000
17.6220810000000
18.1204750000000
18.6587410000000
19.1571350000000
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19.8947580000000
20.1937940000000
20.3732160000000
20.5526380000000
20.6523160000000
20.7719310000000
20.7719310000000
20.8118020000000
20.7519950000000
20.7320590000000
20.7121240000000
20.5725730000000
20.4330230000000
20.3333440000000
20.2735370000000
20.0343080000000
19.9146930000000
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19.4362350000000
19.1770700000000
18.9577770000000
18.6388050000000
18.2799610000000
17.9011820000000
17.6021460000000
17.2233660000000
16.8645230000000
16.5256150000000
16.1069640000000
15.6883130000000
15.3892770000000
15.0503690000000
14.7114610000000
14.3924890000000
14.0735170000000
13.8542230000000
13.6947370000000
13.4355730000000
13.2960220000000
13.1564720000000
13.0567930000000
12.9770500000000
12.9571140000000
12.8375000000000
12.8175640000000
12.7776930000000
12.7577570000000
12.7178850000000
12.7178850000000
12.5982710000000
12.5982710000000
12.5583990000000
12.5783350000000
12.5384630000000
12.4188490000000
12.3789770000000
12.3191700000000
12.2593630000000
12.1796200000000
12.0799410000000
11.9802620000000
11.8606480000000
11.7809050000000
11.7011620000000
11.6612900000000
11.5018040000000
11.4818680000000
11.3622540000000
11.3622540000000
11.2625750000000
11.1828320000000
11.0831530000000
11.0233460000000
10.9236670000000
10.8439240000000
10.7442450000000
10.7043740000000
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10.5249520000000
10.4850800000000
10.4053370000000
10.3255940000000
10.2458510000000
10.2259160000000
10.1063010000000
10.0664290000000
9.92687900000000
9.88700800000000
9.80726500000000
9.70758600000000
9.66771400000000
9.62784300000000
9.56803600000000
9.50822800000000
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9.42848500000000
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9.32880600000000
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9.20919200000000
9.14938500000000
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9.06964200000000
8.96996300000000
8.96996300000000
8.89022000000000
8.81047700000000
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8.73073400000000
8.71079800000000
8.65099100000000
8.61111900000000
8.61111900000000
8.49150500000000
8.51144000000000
8.39182600000000
8.41176200000000
8.31208300000000
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8.21240400000000
8.17253200000000
8.11272500000000
8.07285400000000
8.07285400000000
7.97317500000000
7.91336800000000
7.89343200000000
7.85356000000000
7.75388100000000
7.71401000000000
7.67413800000000
7.61433100000000
7.61433100000000
7.57446000000000
7.59439500000000
7.47478100000000
7.45484500000000
7.41497400000000
7.35516600000000
7.29535900000000
7.25548800000000
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7.13587300000000
7.09600100000000
7.03619400000000
6.99632300000000
6.91658000000000
6.87670800000000
6.81690100000000
6.73715800000000
6.69728600000000
6.63747900000000
6.59760700000000
6.53780000000000
6.45805700000000
6.49792900000000
6.39825000000000
6.37831400000000
6.27863500000000
6.25870000000000
x_2 = 6.25870000000000
6.27863500000000
6.29857100000000
6.25870000000000
6.25870000000000
6.31850700000000
6.43812100000000
6.57767200000000
6.81690100000000
7.03619400000000
7.35516600000000
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8.23234000000000
8.71079800000000
9.30887100000000
9.94681500000000
10.6046950000000
11.3024460000000
12.0201340000000
12.7178850000000
13.4754440000000
14.2130670000000
14.9307540000000
15.5088910000000
16.2066430000000
16.7449080000000
17.2632380000000
17.7416960000000
18.1603470000000
18.4992550000000
18.7384840000000
19.0175840000000
19.1770700000000
19.3564920000000
19.4561710000000
19.4761070000000
19.5558500000000
19.5359140000000
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19.5159780000000
19.4960430000000
19.4561710000000
19.3963640000000
19.3166210000000
19.1371990000000
19.0375200000000
18.9179060000000
18.6786770000000
18.4793190000000
18.2400900000000
18.0207970000000
17.6619530000000
17.3629170000000
17.0040730000000
16.6452290000000
16.3461930000000
15.9275420000000
15.6285060000000
15.2297910000000
14.8908830000000
14.5918460000000
14.2928100000000
13.9937740000000
13.7346090000000
13.5352510000000
13.3358940000000
13.1166000000000
12.9969860000000
12.8375000000000
12.8175640000000
12.6780140000000
12.6381420000000
12.5384630000000
12.5185280000000
12.4985920000000
12.4985920000000
12.5384630000000
12.4587200000000
12.4387850000000
12.4387850000000
12.3989130000000
12.3789770000000
12.2792990000000
12.2593630000000
12.2194910000000
12.1596840000000
12.0600050000000
12.0001980000000
11.8805830000000
11.8606480000000
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11.6612900000000
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11.5018040000000
11.4220610000000
11.5217400000000
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11.2426390000000
11.1429600000000
11.0632170000000
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10.9037310000000
10.8638600000000
10.7841170000000
10.6844380000000
10.6046950000000
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10.4452090000000
10.3654660000000
10.3056590000000
10.2458510000000
10.1860440000000
10.1262370000000
10.0464940000000
9.98668600000000
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9.86707200000000
9.80726500000000
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9.64777900000000
9.48829200000000
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9.54810000000000
9.42848500000000
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9.32880600000000
9.26899900000000
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9.20919200000000
9.14938500000000
9.10951300000000
9.08957700000000
9.00983400000000
8.98989900000000
8.95002700000000
8.93009100000000
8.83041200000000
8.77060500000000
8.71079800000000
8.65099100000000
8.65099100000000
8.57124800000000
8.59118300000000
8.49150500000000
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8.39182600000000
8.41176200000000
8.33201800000000
8.27221100000000
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8.15259700000000
8.11272500000000
8.07285400000000
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8.01304600000000
7.99311100000000
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7.89343200000000
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7.81368900000000
7.81368900000000
7.79375300000000
7.77381700000000
7.73394600000000
7.75388100000000
7.71401000000000
7.67413800000000
7.67413800000000
7.63426700000000
7.59439500000000
7.61433100000000
7.51465200000000
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7.43490900000000
7.41497400000000
7.33523100000000
7.33523100000000
7.27542300000000
7.19568000000000
7.11593700000000
7.09600100000000
7.05613000000000
6.97638700000000
6.91658000000000
6.87670800000000
6.79696500000000
6.81690100000000
6.73715800000000
6.65741500000000
6.61754300000000
6.51786400000000
6.51786400000000
6.43812100000000
6.43812100000000
6.37831400000000
x_3=6.37831400000000
6.37831400000000
6.43812100000000
6.37831400000000
6.41818600000000
6.47799300000000
6.63747900000000
6.83683700000000
7.07606600000000
7.41497400000000
7.73394600000000
8.17253200000000
8.77060500000000
9.30887100000000
9.90694300000000
10.6046950000000
11.3423180000000
12.0799410000000
12.8773710000000
13.6548660000000
14.4124250000000
15.1899190000000
15.9076060000000
16.5854220000000
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18.2799610000000
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19.1371990000000
19.4561710000000
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20.0343080000000
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20.3532800000000
20.4130870000000
20.5725730000000
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20.5327020000000
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18.6587410000000
18.3597040000000
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16.6452290000000
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14.6715900000000
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14.0535810000000
13.8342880000000
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13.1365360000000
13.0966650000000
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13.0368570000000
12.9770500000000
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12.8773710000000
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12.7577570000000
12.6979500000000
12.6182060000000
12.5185280000000
12.4587200000000
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11.6014830000000
11.5217400000000
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11.1429600000000
11.0632170000000
10.9635390000000
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10.7043740000000
10.6046950000000
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10.4452090000000
10.3854020000000
10.2857230000000
10.2059800000000
10.0863650000000
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10.0464940000000
9.90694300000000
9.84713600000000
9.84713600000000
9.72752200000000
9.70758600000000
9.68765000000000
9.58797100000000
9.54810000000000
9.54810000000000
9.48829200000000
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9.36867800000000
9.36867800000000
9.34874200000000
9.30887100000000
9.24906300000000
9.24906300000000
9.18925600000000
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9.12944900000000
9.08957700000000
9.06964200000000
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8.91015500000000
8.91015500000000
8.89022000000000
8.85034800000000
8.79054100000000
8.79054100000000
8.73073400000000
8.65099100000000
8.71079800000000
8.61111900000000
8.59118300000000
8.51144000000000
8.43169700000000
8.45163300000000
8.39182600000000
8.35195400000000
8.35195400000000
8.29214700000000
8.25227500000000
8.17253200000000
8.17253200000000
8.11272500000000
8.07285400000000
7.97317500000000
7.95323900000000
7.93330300000000
7.83362500000000
7.77381700000000
7.75388100000000
7.69407400000000
7.65420300000000
7.61433100000000
7.55452400000000
7.43490900000000
7.39503800000000
7.33523100000000
7.29535900000000
7.23555200000000
7.13587300000000
7.11593700000000
7.05613000000000
7.03619400000000
6.93651500000000
6.89664400000000
6.93651500000000
6.83683700000000
x_4=6.83683700000000
6.85677200000000
6.85677200000000
6.91658000000000
7.01625800000000
7.15580900000000
7.31529500000000
7.51465200000000
7.79375300000000
8.19246800000000
8.57124800000000
9.08957700000000
9.60790700000000
10.2657870000000
10.8638600000000
11.5416760000000
12.2992340000000
13.0368570000000
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14.4921680000000
15.2098550000000
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17.1834950000000
17.6619530000000
18.2201540000000
18.6587410000000
19.0574560000000
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20.0941150000000
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20.5127660000000
20.5327020000000
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20.3532800000000
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20.1539230000000
20.0741800000000
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19.7751430000000
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18.9976490000000
18.6786770000000
18.4394480000000
18.1404110000000
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16.4458720000000
16.1269000000000
15.7879920000000
15.4889560000000
15.2497260000000
14.9905620000000
14.7712680000000
14.5320390000000
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14.2529390000000
14.0535810000000
13.9937740000000
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13.8143520000000
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13.7146730000000
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10.9236670000000
10.8040530000000
10.7442450000000
10.6445660000000
10.5249520000000
10.4252730000000
10.3654660000000
10.3255940000000
10.1860440000000
10.1063010000000
9.98668600000000
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9.76739300000000
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8.65099100000000
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8.45163300000000
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6.59760700000000
6.59760700000000
6.57767200000000
6.51786400000000
6.49792900000000
6.47799300000000
6.45805700000000
x_5=6.45805700000000
6.47799300000000
6.47799300000000
6.53780000000000
6.61754300000000
6.67735000000000
6.81690100000000
7.07606600000000
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8.51144000000000
9.02977000000000
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10.2259160000000
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11.6214190000000
12.4188490000000
13.1365360000000
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x_6=6.83683700000000
6.83683700000000
6.91658000000000
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11.2227030000000
11.2027680000000
11.1828320000000
11.0632170000000
10.9834740000000
10.9436030000000
10.8638600000000
10.8239880000000
10.7641810000000
10.7043740000000
10.6445660000000
10.6645020000000
10.5448880000000
10.5249520000000
10.4452090000000
10.4252730000000
10.3455300000000
10.2857230000000
10.2259160000000
10.2458510000000
10.1461730000000
10.1262370000000
10.0464940000000
10.0066220000000
9.94681500000000
9.90694300000000
9.86707200000000
9.74745700000000
9.76739300000000
9.70758600000000
9.68765000000000
9.60790700000000
9.56803600000000
9.50822800000000
9.44842100000000
9.36867800000000
9.38861400000000
9.26899900000000
9.24906300000000
9.18925600000000
9.14938500000000
9.04970600000000
9.02977000000000
8.93009100000000
8.89022000000000
8.83041200000000
8.77060500000000
8.75066900000000
8.69086200000000
8.65099100000000
8.63105500000000
8.57124800000000
8.59118300000000
8.57124800000000
8.49150500000000
8.47156900000000
8.45163300000000
8.41176200000000
8.37189000000000
8.35195400000000
8.33201800000000
8.29214700000000
8.29214700000000
8.25227500000000
8.27221100000000
8.21240400000000
8.25227500000000
8.23234000000000
8.19246800000000
8.15259700000000
8.17253200000000
8.07285400000000
8.05291800000000
8.03298200000000
7.97317500000000
7.91336800000000
7.95323900000000
7.85356000000000
7.81368900000000
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7.73394600000000
7.67413800000000
7.61433100000000
7.59439500000000
7.51465200000000
7.43490900000000
7.35516600000000
7.39503800000000
7.25548800000000
7.21561600000000
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7.01625800000000
6.99632300000000
6.95645100000000
6.89664400000000
6.87670800000000
6.83683700000000
6.81690100000000
6.73715800000000
x_7=6.73715800000000
6.77702900000000
6.75709400000000
6.79696500000000
6.83683700000000
6.93651500000000
7.05613000000000
7.27542300000000
7.53458800000000
7.87349600000000
8.31208300000000
8.83041200000000
9.34874200000000
9.96675100000000
10.6645020000000
11.3821900000000
12.1397480000000
12.9571140000000
13.7744800000000
14.5719110000000
15.3294690000000
16.0670920000000
16.8047150000000
17.4825310000000
18.1204750000000
18.6986120000000
19.2169420000000
19.7153360000000
20.1140510000000
20.3931520000000
20.6523160000000
20.8915450000000
21.0510320000000
21.1706460000000
21.2703250000000
21.3899390000000
21.3500680000000
21.3500680000000
21.3301320000000
21.2503890000000
21.1706460000000
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21.0310960000000
20.9114810000000
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20.4728950000000
20.2536010000000
20.0941150000000
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18.6786770000000
18.3597040000000
18.0008610000000
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16.9243300000000
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15.3892770000000
15.1699830000000
14.9307540000000
14.7712680000000
14.6117820000000
14.4323600000000
14.3725530000000
14.2529390000000
14.1333240000000
14.0735170000000
14.1133880000000
13.9738380000000
13.9539020000000
13.9339660000000
13.8542230000000
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13.8741590000000
13.8542230000000
13.7944160000000
13.8143520000000
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13.7146730000000
13.6548660000000
13.5551870000000
13.5153160000000
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12.3989130000000
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11.7210970000000
11.6612900000000
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11.3821900000000
11.2825110000000
11.2625750000000
11.1628960000000
11.0632170000000
11.0632170000000
10.9635390000000
10.9037310000000
10.7841170000000
10.6844380000000
10.6645020000000
10.6046950000000
10.5050160000000
10.4850800000000
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10.3455300000000
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10.1661080000000
10.0863650000000
10.0863650000000
10.0265580000000
9.98668600000000
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9.92687900000000
9.86707200000000
9.78732900000000
9.74745700000000
9.74745700000000
9.64777900000000
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9.60790700000000
9.58797100000000
9.46835700000000
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9.24906300000000
9.20919200000000
9.16932000000000
9.14938500000000
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9.02977000000000
9.02977000000000
8.95002700000000
8.91015500000000
8.83041200000000
8.85034800000000
8.79054100000000
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8.77060500000000
8.75066900000000
8.73073400000000
8.67092600000000
8.61111900000000
8.59118300000000
8.51144000000000
8.45163300000000
8.43169700000000
8.35195400000000
8.19246800000000
8.23234000000000
8.11272500000000
8.09278900000000
8.07285400000000
7.95323900000000
7.89343200000000
7.91336800000000
7.81368900000000
7.75388100000000
7.71401000000000
7.65420300000000
7.61433100000000
7.61433100000000
7.59439500000000
7.63426700000000
7.59439500000000
7.63426700000000
7.57446000000000
数据阵列的长度不同。 我想平均这7个数据。
答案 0 :(得分:2)
如果需要找到存储在多个向量中的值的平均值,首先将两个向量的所有值相加。然后使用length()来查找每个向量中有多少条目。添加长度,您将拥有总条目数。然后,您可以将总和除以条目数以得到平均值。 具体如何做到这一点取决于你的数据(你有多少向量,你是否总是有相同的数字)。
因此,如果您的变量是a,b,c,d,e,f,g:
sumOfVectors = sum(a) + sum(b) + ... etc
numberOfItems = length(a) + length(b) + ... etc
averageAllData = sumOfVectors/numberOfItems
您还可以通过循环使用任意数量的项目。
答案 1 :(得分:1)
使用mean
并连接所有向量:
mean([x1 x2 x3 x4 x5 x6 x7]);
如果它们是列向量垂直连接:
mean([x1; x2; x3; x4; x5; x6; x7]);