Judul: Pemijahan, Embriologi dan Perkembangan Larva Ikan Menfis (Pterophyllum scalare)
Abstrak: Penelitian ini dilakukan mulai bulan Februari sampai Juli 1991 di Laboratorium Pengembangbiakan Ikan Fakultas Perikanan IPB, Darmaga. Penelitian ini bertujuan untuk mengamati tingkah laku induk ikan yang mijah, selang waktu pemijahan, fekunditas, telur yang diovulasikan, perkembangan embrio, perkembangan larva dan pertumbuhan juvenil ikan menfis. Induk ikan menfis yang digunakan sebanyak 5 pasang, strain marmer dan sleyer. Warna ikan adalah hitam, putih keperakan dan hijau zaitun. Telur yang digunakan untuk pengamatan perkembangan embrio masih hidup dan utuh. Larutan Methylene Blue, Novobiocin dan garam dapur digunakan untuk pencegahan terhadap penyakit. Makanan yang diberikan ke induk dan larva adalah cacing rambut dan nauplii Artemia sp.
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Judul: Embriologi dan perkembangan larva ikan beronang, siganus virgatus
Abstrak: Tulisan ini merupakan hasil penelitian masalah khusus yang dilaksanakan di laboratorium Sub Balai Penelitian Perikanan Laut Serang, mulai awal bulan Maret hingga pertengahan bulan Desember 1984. Penelitian ini bertujuan untuk mengetahui perkembangan embrio serta kelengkapan organ yang dimiliki larva ikan beronang hingga saat membuka mulut. Aspek-aspek yang diamati selama masa embrio meliputi stadia cleavage, gastrula, embrionik, dan hal-hal yang mempengaruhi perkembangannya. Sedangkan pengamatan larva meliputi panjang total tubuh, sirip, pigmentasi, kuning telur, usus, keadaan mulut, dan somit.
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Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis