Judul: Viabilitas Tahap Blastosis pada Sapi
Abstrak: in vivo embryo centre in Cipelang was limited by number of donor and respond due to superovulation program.
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Judul: Pembentukan Blastosit secara Fertilisasi In Vitro pada Sapi dari Bangsa dan Konsentrasi Sperma yang Berbeda
Abstrak: PErkembangan embrio untuk mencapai tahap blastosit di pengaruhi oleh berbagai faktor seperti kebutuhan dan lingkungan embrio, variasi genetik, konsentrasi sperma dan lain-lain. Penelitian ini bertujuan untuk mengetahui Pengaruh bangsa dan konsentrasi sperina sapi dalam menghasilkan blastosit. Penelitian ini dilaksanakan di Balai Embrio Ternak Cipelang, Bogor, dari bulan Maret sampai April 2000. Materi penelitian yang digunakan adalah ovari bangsa sapi PO dan Brahman Cross yang diperoleh dari rumah potong hewan, semen beku sapi Brangus dengan konsentras 12,5 x 10⁶ dan 25 x 10⁶ dan sapi Angus dengan konsentrasi 12,5 x 10⁶ dari Balai lnseminasi Buatan Lembang. Oosit hasil aspirasi dimaturasi dalam media TCM-199 + 5% Calf Serum (CS) + antibiotik, diinkubasi selama 20 jam pada suhu 38,5⁰C dan 2% CO₂. Sperma dikapasitasi pada media BO ditambah heparin dan BSA. Fertilisasi dilakukan selama 5 jam pada suhu 38,5⁰C dan 2% CO₂. Setelah fertilisasi embrio dikultur pada suhu 38,5⁰C dan 5% CO₂ dalam media kultur CRlaa + 5%CS + antibiotik.
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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