Judul: Perkembangan Embrio dan Larva Ikan Silver Dollar (Metynnis schreitmuelleri)
Abstrak: Tulisan ini merupakan hasil penelitian masalah khusus yang dilaksanakan di Laboratorium Pengembangbiakan Ikan, Jurusan Budidaya Perairan, Fakultas Perikanan, Institut Pertanian Bogor, mulai dari Bulan Januari sampai Bulan April 1991. Penelitian ini bertujuan untuk mengetahui perkembangan embrio dan larva ikan silver dollar, mulai saat telur dibuahi sampai larva mencapai bentuk definitif (bentuk menyerupai induknya). Aspek yang diamati dan diukur adalah: diameter telur, diameter kuning telur larva, panjang total, tinggi badan, bobot larva, perkembangan sirip, pigmentasi, tingkat perkembangan telur (DR), laju kelangsungan hidup larva (SR), derajat penetasan (HR) dan laju kelangsungan hidup larva setelah 14 hari (SR-14)…dst
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Judul: Pemijahan dan Perkembangan Embrio serta Larva Ikan Rainbow (Melanotaenia patoti)
Abstrak: Penelitian ini dilakukan mulai bulan Februari sampai Juni 1991 di Laboratorium Pengembangbiakan Ikan, Fakultas Perikanan IPB, Darmaga. Penelitian ini bertujuan untuk mengamati tingkah laku pemijahan ikan, frekuensi pemijahan, perkembangan embrio dan larva sampai fase definitif. Induk ikan rainbow yang digunakan sebanyak 3 pasang yang berumur 7 bulan dan belum pernah dipijahkan. Sebagai media penempelan telur digunakan akar eceng gondok (Eichornia crassipes Mort.). Telur yang digunakan untuk pengamatan perkembangan embrio hidup dan utuh. Larutan Malachite Green, Methylene Blue dan garam dapur digunakan untuk pencegahan terhadap penyakit. Makanan yang diberikan kepada induk berupa cacing rambut (Tubifex sp), sedangkan untuk larva diberikan makanan buatan merek "Vita Fish" pada hari keempat dan nauplii Artemia mulai hari ketujuh. Alat yang digunakan antara lain adalah akuarium kaca (60 x 50 x 40 cm), akuarium kaca (20 x 20 x 20 cm), mikroskop compound, mikroskop dissecting, timbangan merek "Oertling" dan counter serta termometer. ...
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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