![](data:image/png;base64,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)
Hoofdstuk 13: Inductieve statistieken en verdelingen 380
•H
a
: m
1
ƒm
2
(m1:ƒm2)
•H
a
: m
1
<m
2
(m1:<m2)
•H
a
: m
1
>m
2
(m1:>m2)
In dit voorbeeld is:
SAMP1={12.207 16.869 25.05 22.429 8.456 10.589}
SAMP2={11.074 9.686 12.064 9.351 8.182 6.642}
Data Stats
Invoer: