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isputation
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There are 10 letters in ISPUTATION ( A1I1N1O1P3S1T1U1 )
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Scrabble results that can be created with an extra letter added to ISPUTATION
7 letters out of ISPUTATION
6 letters out of ISPUTATION
5 letters out of ISPUTATION
ANTIS
AUNTS
AUTOS
INAPT
INPUT
INTIS
IOTAS
NIPAS
OPSIN
OSTIA
PAINS
PAINT
PANTO
PANTS
PATIN
PATIO
PIANO
PIANS
PINAS
PINOT
PINTA
PINTO
PINTS
PIONS
PIOUS
PITAS
PITON
PITTA
POINT
POSIT
POUTS
PSOAI
PUNAS
PUNTO
PUNTS
PUTON
PUTTI
PUTTO
PUTTS
SAINT
SANTO
SATIN
SITUP
SNOUT
SPAIT
SPOUT
SPUTA
STAIN
STINT
STOAI
STOAT
STOPT
STOUP
STOUT
STUNT
STUPA
SUINT
SUTTA
TAINS
TAINT
TANTO
TAPIS
TAUNT
TAUON
TAUTS
TINTS
TIPIS
TITAN
TITIS
TOAST
TOITS
TONUS
TOPIS
TOUTS
TUNAS
UNAIS
UNAPT
UNITS
4 letters out of ISPUTATION
AINS
AITS
ANIS
ANTI
ANTS
ANUS
APOS
ATOP
AUNT
AUTO
INIA
INTI
INTO
IONS
IOTA
NAOI
NAOS
NAPS
NIPA
NIPS
NISI
NITS
NOTA
NOUS
NUTS
OAST
OATS
ONUS
OPTS
OPUS
OUST
OUTS
PAIN
PANS
PANT
PAST
PATS
PIAN
PIAS
PINA
PINS
PINT
PION
PISO
PITA
PITS
POIS
PONS
POST
POTS
POUT
PTUI
PUNA
PUNS
PUNT
PUTS
PUTT
SAIN
SATI
SNAP
SNIP
SNIT
SNOT
SOAP
SOUP
SPAN
SPAT
SPIN
SPIT
SPOT
SPUN
STAT
STOA
STOP
STOT
STUN
SUIT
TAIN
TANS
TAOS
TAPS
TATS
TAUS
TAUT
TINS
TINT
TIPI
TIPS
TITI
TITS
TOIT
TONS
TOPI
TOPS
TOST
TOTS
TOUT
TUIS
TUNA
TUNS
TUPS
TUTS
UNAI
UNIT
UNTO
UPAS
UPON
UTAS
3 letters out of ISPUTATION
2 letters out of ISPUTATION
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Isputation might refer to |
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In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias, make the handling and analysis of the data more arduous, and create reductions in efficiency. Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data. Imputation theory is constantly developing and thus requires consistent attention to new information regarding the subject. There have been many theories embraced by scientists to account for missing data but the majority of them introduce large amounts of bias. A few of the well known attempts to deal with missing data include: hot deck and cold deck imputation; listwise and pairwise deletion; mean imputation; regression imputation; last observation carried forward; stochastic imputation; and multiple imputation. |