Judul: Analisa kelimpahan dan pola pertumbuhan populasi tetrahymena pyriformis dalam media dan temperatur inkubasi yang berbeda
Abstrak: Penelitian dilakukan sejak tanggal 6 September 1988 hingga tanggal 17 Nopember 1988 yang dibagi dalam dua periode, yaitu penelitian pendahuluan dan penelitian pokok. Tempat penelitian di Laboratorium hama dan penyalit ikan, Balai Penelitian Perikanan Air Tawar (BALITKANWAR), Bogor. Tujuan Penelitian ini adalah untuk mengetahui kelimpahan dan pola pertumbuhan populasi T. pyriformis dalam media dan temperatur inkubasi yang berbeda.
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Judul: Studi analisis pertumbuhan dan kelimpahan populasi Tetrahymena sp. Akibat pengaruh perlakuan pH di lLaboratorium
Abstrak: Penelitian ini dilakukan dari tanggal 1 Maret 2 Mei 1988 di Laboratorium Hama Penyakit Balai Penelitian Perikanan Air Tawar Bogor. Tujuan penelitian adalah untuk mengetahui pengaruh perlakuan pH pada suatu media kultur tertentu terhadap perkembangan populasi Tetrahymena sp. Penelitian ini dilakukan dengan menggunakan bahan uji Tetrahymena sp. pada padat penebaran + 166 sel/ml (5000 sel/wadah). Media yang digunakan adalah larutan media baku dari Wolf dan Markiw (1982) yang diencerkan 1: 10 dengan aquades. Metode yang digunakan adalah Rancangan Acak Lengkap (RAL) terdiri dari 9 perlakuan pH, yaitu 4,0; 4,5; 5,0; 5,5; 6,0; 6,5; 7,0; 7,5 dan 8,0 dengan 3 ulangan. Berdasarkan hasil analisa statistik menunjukan bahwa pengaruh perlakuan pH media terhadap pertumbuhan populasi Tetrahymena sp. tampak berbeda nyata dari hari ke 1 7, dengan demikian berarti kondisi pH media merupakan salah satu faktor yang dapat mempengaruhi kelangsungan hidup populasi Tetrahymena sp. dalam suatu kultur, sehingga dari penelitian ini diperoleh perbedaan lama hidup populasi, bentuk grafik pertumbuhan populasi dan puncak pertumbuhan populasi Tetrahymena sp. dari tiap perlakuan pH.
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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