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unbiasedn

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The answer UNBIASEDN has 0 possible clue(s) in existing crosswords.

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There are 9 letters in UNBIASEDN ( A1B3D2E1I1N1S1U1 )

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8 letters out of UNBIASEDN

UNBIASED

7 letters out of UNBIASEDN

BANDIES BASINED BEDUINS BUNNIES DUENNAS SUBDEAN SUBIDEA UNBASED UNBENDS UNBINDS UNDINES

6 letters out of UNBIASEDN

ABIDES ABUSED ADIEUS BANNED BEDUIN BENNIS BIASED BINNED BUSIED DAUBES DUENNA ENNUIS INANES INDUES INSANE NUBIAS NUDIES SABINE SAINED SIENNA SINNED SUNDAE SUNNED UNBANS UNBEND UNBIND UNDIES UNDINE UNSAID

5 letters out of UNBIASEDN

ABIDE ABUSE ADIEU AIDES ANISE ASIDE BANDS BANED BANES BANNS BASED BASIN BAUDS BEADS BEANS BEAUS BENDS BENNI BIDES BINDS BINES BUNAS BUNDS BUNNS BUSED DAUBE DAUBS DEANS DINES DUNES ENNUI IDEAS INANE INDUE INNED NABES NABIS NIDES NIDUS NINES NUBIA NUDES NUDIE SABED SABIN SANED SEDAN SENNA SNIDE SUNNA UNAIS UNBAN UNBID USNEA

4 letters out of UNBIASEDN

ABED AIDE AIDS AINS ANDS ANES ANIS ANUS BADE BADS BAND BANE BANI BANS BASE BAUD BEAD BEAN BEAU BEDS BEDU BEND BENS BIAS BIDE BIDS BIND BINE BINS BISE BUDS BUNA BUND BUNN BUNS DABS DAIS DANS DAUB DEAN DEBS DENI DENS DIBS DIES DINE DINS DUBS DUES DUNE DUNS ENDS IDEA IDES INNS ISBA NABE NABS NANS NEBS NIBS NIDE NINE NUBS NUDE NUNS SABE SADE SADI SAID SAIN SAND SANE SEND SIDE SINE SNED SNIB SNUB SUBA SUED SUNN UNAI UNBE UNDE USED

3 letters out of UNBIASEDN

ABS ADS AID AIN AIS AND ANE ANI BAD BAN BAS BED BEN BES BID BIN BIS BUD BUN BUS DAB DAN DEB DEN DIB DIE DIN DIS DUB DUE DUI DUN EAU EDS END ENS IDS INN INS NAB NAE NAN NEB NIB NUB NUN NUS SAB SAD SAE SAU SEA SEI SEN SIB SIN SUB SUE SUN UNS USE

2 letters out of UNBIASEDN

AB AD AE AI AN AS BA BE BI DE ED EN ES ID IN IS NA NE NU SI UN US

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Definitions of unbiasedn in various dictionaries:

UNBIASEDN - In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parame...

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Unbiasedn might refer to
In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased. In statistics, "bias" is an objective property of an estimator, and while not a desired property, it is not pejorative, unlike the ordinary English use of the term "bias".
* Bias can also be measured with respect to the median, rather than the mean (expected value), in which case one distinguishes median-unbiased from the usual mean-unbiasedness property. Bias is related to consistency in that consistent estimators are convergent and asymptotically unbiased (hence converge to the correct value as the number of data points grows arbitrarily large), though individual estimators in a consistent sequence may be biased (so long as the bias converges to zero); see bias versus consistency.
* All else being equal, an unbiased estimator is preferable to a biased estimator, but in practice all else is not equal, and biased estimators are frequently used, generally with small bias. When a biased estimator is used, bounds of the bias are calculated. A biased estimator may be used for various reasons: because an unbiased estimator does not exist without further assumptions about a population or is difficult to compute (as in unbiased estimation of standard deviation); because an estimator is median-unbiased but not mean-unbiased (or the reverse); because a biased estimator gives a lower value of some loss function (particularly mean squared error) compared with unbiased estimators (notably in shrinkage estimators); or because in some cases being unbiased is too strong a condition, and the only unbiased estimators are not useful. Further, mean-unbiasedness is not preserved under non-linear transformations, though median-unbiasedness is (see effect of transformations); for example, the sample variance is an unbiased estimator for the population variance, but its square root, the sample standard deviation, is a biased estimator for the population standard deviation. These are all illustrated below.
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