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"This research is a correlation study between learning approach and achievement in mathematics of third grade science major of "X" Senior High School in Bandung. The samples selection for this research used simple random sampling method and the samples of this research are 60 students. The instrument used to collect the data about learning approach was adapted from questionnaire that was developed by John Biggs (1987). It is called The Revised Two-Factor Learning Process Questionnaire (R-LPQ-2F) that consist of 40 items. With the validation starting from 0.401 until 0.795 and the reliability is 0.638. Whereas the instrument used to measure achievement in mathematic is from final score of mathematic major. The collected data was mannered by Spearman's correlation using SPSS 15. From the final result we can see that the correlation between surface approach and mathematic final score is 0.062 and the correlation between deep approach and mathematic final score is 0.155. The conclusions are there is no significant correlation between surface approach and mathematic final score, and also there is no significant correlation between dep approach and mathematic final score"

"Model probit adalah salah satu jenis model pilihan diskrit dengan error yang diasumsikan saling bebas, berdistribusi normal, dan homoskesdastis. Model probit dengan komponen error dari observasi di suatu lokasi bergantung dengan komponen error dari observasi di lokasi lain disebut model probit spasial error. Parameter dari model probit spasial error akan ditaksir dengan metode maksimum likelihood parsial dalam dua cara. Cara pertama dengan memperhitungkan variansi dari error dalam pembentukan fungsi likelihood parsial. Cara kedua dengan membentuk n grup dari sejumlah 2n observasi dari lokasi berbeda, dimana setiap grup terdiri dari dua observasi di lokasi yang berbeda. Selanjutnya, dengan memperhatikan korelasi antar error di dalam grup, akan dibentuk fungsi kepadatan probabilitas dari setiap grup yang akan digunakan untuk membentuk fungsi likelihood parsial dalam penaksiran parameter.

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Probit Model is one of discrete choice model whose error is assumed to be independent, normal distributed, and homoscedastic. Probit model whose error component from observations on a location that depends on error components on the other location is called spatial error probit model. Parameters of spatial error probit model will be estimated by partial maximum likelihood in two ways. The first way is to take into account the variance of the error in the form of the partial likelihood function.

The second is to form n groups from 2n observations at different location, which each group consists of two observations at different location. Furthermore, by taking into account the correlation between errors in a group, the probability density function of each group will be formed, and later will be used to form a partial likelihood function in parameter estimation."

"ABSTRAKIn the present paper we consider a class of multiobjective B-vex programming problems involving differentiable B-vex n-set functions and establish duality results in terms of properly efficient solutions. Further, we relate the problem to a certain saddle point of a Lagrangian and show multiobjective fractional program as a special case of the main problem."

"ABSTRAKMinimum-time and smooth-steering control algorithms are developed from bobsled optimal control. Numerical solutions are obtained both for one-curve optimal control and whole-course piecewise optimal control with application to realistic three-dimensional track surface shapes. Specific results are calculated for the Lillehammer Olympic Track."

"This easy-to-follow textbook introduces the mathematical language, knowledge and problem-solving skills that undergraduates need to study computing. The language is in part qualitative, with concepts such as set, relation, function and recursion/induction, but it is also partly quantitative, with principles of counting and finite probability. Entwined with both are the fundamental notions of logic and their use for representation and proof. Features : teaches finite math as a language for thinking, as much as knowledge and skills to be acquired, uses an intuitive approach with a focus on examples for all general concepts, brings out the interplay between the qualitative and the quantitative in all areas covered, particularly in the treatment of recursion and induction, balances carefully the abstract and concrete, principles and proofs, specific facts and general perspectives, includes highlight boxes that raise common queries and clear confusions, and provides numerous exercises, with selected solutions."