![](data:image/png;base64,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)
TI-83 Plus Inductieve statistieken en verdelingen 460
ANOVA(
lijst1
,
lijst2
[
,
...
,
lijst20
]
)
In dit voorbeeld is:
L1={7 4 6 6 5}
L2={6 5 5 8 7}
L3={4 7 6 7 6}
Invoer:
,
Calculate: resultaat
van de berekening
Opmerking: SS is de som van de kwadraten en MS is het gemiddelde
kwadraat.