MCAR

AcronymDefinition
MCARMissing Completely At Random
MCARMilitary Construction, Army Reserve
MCARMaterial Corrective Action Report (quality)
MCARModified Car and Rally (automotive show)
MCARMarine Corps Air Station
MCARMachine Check Analysis and Recording
MCARMultiple Channel Aural Reception
MCARMultichannel Acoustic Relay
MCARMalaysian Civil Aviation Regulation (Malaysia)
MCARMobile Cosmetic Auto Repairs
MCARModel Civil Aviation Regulations
References in periodicals archive ?
Smiles and Smith (2004) suggested a 'monovalent cation ratio' (MCAR), which includes Na + K in the calculation of SAR, while Mg is treated as equal to Ca.
ML can account for exposure and outcome data missing at random (MAR) (Little and Rubin 2002) and is robust to distributional assumptions when missing data are MCAR (Litman et al.
Because the parameter space of the distribution of M is empty and MCAR is clearly met, the marginal distribution of [U.sub.obs] (here X) can be used by the ignorability principle for correct likelihood inference:
The framework assumes that a data analysis model has been established, that this model is included in the imputation model, and that the Little (1988) MCAR test (available in SPSS) confirms deletion would produce biased estimates--thus requiring a more rigorous strategy, such as imputation.
Missing completely at random (MCAR): The missing-data pattern R is independent of [Y.sub.obs] and [Y.sub.mis].
In the last few years, a new approach that integrates association rule mining and classification called associative classification has been proposed (Antonie, et al., 2003; Li, et al., 2001; Liu, et al., 1998; Ali, et al., 1997) A few accurate and effective classifiers based on associative classification have been presented in last few years, such as CMAR (Li, et al., 2001), CPAR (Yin and Han, 2003), CBA (Liu, et al., 1998) and MCAR (Thabtah, et al., 2005).
(4) MCAR = ([Na] + [K] + [NH4 ])/ [square root of ([Ca] +[Mg])/2] [([mmol.sub.c]/L).sup.1/2]
Missing completely at random test (MCAR; Little and Rubin, 2002) indicated that missing values were missing completely at random ([chi square] = 1608.6, df = 1574, p = 0.266).
As economic, social, and political variables are part of the dataset, we assume the data not to be missing completely at random (MCAR) but rather missing at random (MAR) (Little & Rubin, 2014).
In terms of accessing missing data, Little's MCAR test for study one indicated that the data was missing at random (i.e., no identifiable pattern exists to the missing data); [chi square](217) = 51.28, p = 1.000.