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"Two class pattern classification problems appeared in many applications. In some applications, the characteristic of themembers in a class is dissimilar. This paper proposed a classification system for this problem. The proposed system was developed based on the combination of kernel principal component analysis (KPCA) and support vector machines (SVMs). This system has been implemented in a two class face recognition problem. The average of the classificationrate in this face image classification is 82.5%."

"Laporan ini menganalisa tentang apakah mahasiswa internasional di Melbourne, Australia, sesungguhnya membayar jasa trem. Pada saat itu, terdapat isu tentang pelanggaran tiket trem oleh mahasiswa internasional, hal ini membuat kami memutuskan untuk membuat riset tentang jasa trem dan pelanggaran tiket trem oleh mahasiswa internasional. Model regresi pada laporan ini dibuat berdasarkan kuesioner yang dibagikan secara acak kepada 158 koresponden yang seluruhnya merupakan mahasiswa internasional yang pada saat itu tinggal dan belajar di Melbourne. Riset ini menghasilkan konklusi bahwa terdapat 65.08% mahasiswa internasional di Melbourne yang sesungguhnya membayar jasa trem dan persentase ini akan bertambah jika pemerintah Australia memberikan subsidi transportasi umum kepada mahasiswa internasional.

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This report analyze about whether international students in Melbourne, Australia, were actually pay for tram services. Lately, there had been an issue of tram fare invasion by international students, so we decided to plot our research around tram services and international student fare invasion. Regression model in this report is based on questionnaire that we distribute to 158 random correspondents, which all of them are international students that are currently studying and living in Melbourne. The research results in 65.08% of international students in Melbourne were actually paid for tram ticket and this number is likely to increase if the Australian government implied public transport concession policy for international students. "

"Multivariate Analysis for the Social Sciences provides clear guidelines combined with the insight needed to understand the methods and applications of multivariate statistics. This easy-to-follow book provides students in social, behavioral, and health science-whose focus is not primarily in mathematics-with an abundance of chapter-ending questions and answers, including: conceptual questions about the meaning of each method; questions that test the reader?s ability to carry out the computational procedures on simple datasets; and data analysis questions for using analytical packages to analyze both simplest case data and also to practice with more realistic datasets."

"Introduction -- Counterfactual framework and assumptions -- Conventional methods for data balancing -- Sample selection and related models -- Propensity score matching and related models -- Propensity score subclassification -- Propensity score weighting -- Matching estimators -- Propensity score analysis with nonparametric regression -- Propensity score analysis of categorical or continuous treatments -- Selection bias and sensitivity analysis -- Concluding remar"