Penaksiran Komponen Variansi Dengan Metode Maksimum Likelihood Pada Rancangan Acak Lengkap Untuk Data Seimbang - Raupong, Raupong (Jurnal Matematika, Statistika dan Komputasi, 2018)
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" 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