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"Kepulauan Seribu memiliki potensi besar dalam sektor pariwisata dan telah ditetapkan sebagai Kawasan Strategis Pariwisata Nasional (KSPN) melalui PP Nomor 50 Tahun 2011 tentang Rencana Induk Pembangunan Kepariwisataan Nasional Tahun 2010–2025, serta masuk dalam 10 daerah prioritas KSPN berdasarkan PERPRES Nomor 3 Tahun 2016 tentang Percepatan Pelaksanaan Proyek Strategis Nasional. Sektor pariwisata merupakan pilar ekonomi utama di Kepulauan Seribu, di mana mayoritas penduduk bekerja di sektor jasa, terutama pariwisata, yang menyerap 30% tenaga kerja, dan 24% lainnya bekerja di sektor perdagangan, hotel, dan restoran yang dipengaruhi oleh aktivitas pariwisata, sesuai dengan Rencana Zonasi Wilayah Pesisir dan Pulau-Pulau Kecil (2022). Penelitian ini bertujuan untuk menganalisis pola perjalanan rekreasi, faktor-faktor yang mempengaruhinya, dan mengembangkan model persamaan bangkitan perjalanan dengan tujuan rekreasi di Kepulauan Seribu. Pendekatan kuantitatif dengan metode cross-sectional digunakan dalam pengumpulan data, di mana data primer dikumpulkan melalui kuesioner dan data sekunder diperoleh dari instansi terkait. Analisis regresi linear menunjukkan pendapatan keluarga kurang dari Rp1.500.000, antara Rp6.500.000 hingga Rp8.000.000, lebih dari Rp15.000.000, serta jumlah anggota keluarga yang bekerja memiliki pengaruh signifikan terhadap jumlah produksi perjalanan. Selain itu, luas wilayah wisata juga memiliki pengaruh signifikan terhadap jumlah atraksi perjalanan. Penelitian ini membantu perencana transportasi dan pembuat kebijakan dalam merencanakan dan mengembangkan transportasi penumpang di Kepulauan Seribu.

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Kepulauan Seribu has great potential in the tourism sector and has been designated as Kawasan Strategis Pariwisata Nasional (KSPN) through PP Nomor 50 Tahun 2011 tentang Rencana Induk Pembangunan Kepariwisataan Nasional Tahun 2010–2025, as well as included in the 10 KSPN priority areas based on PERPRES Nomor 3 Tahun 2016 tentang Percepatan Pelaksanaan Proyek Strategis Nasional. The tourism sector is the main economic pillar in Kepulauan Seribu, where the majority of the population works in the service sector, especially tourism, which absorbs 30% of the workforce, and another 24% work in the trade, hotel and restaurant sector which is influenced by tourism activities, according to Rencana Zonasi Wilayah Pesisir dan Pulau-Pulau Kecil (2022). This study aims to analyze recreational travel patterns, the factors that influence them, and develop a travel generation equation model with recreational purposes in Kepulauan Seribu. A quantitative approach with a cross-sectional method was used in data collection, where primary data was collected through questionnaires and secondary data was obtained from relevant agencies. Linear regression analysis showed that family income of less than IDR1,500,000, between IDR6,500,000 to IDR8,000,000, more than IDR15,000,000, and the number of working family members have a significant influence on the amount of trip production. In addition, the size of the tourist area also has a significant influence on the number of travel attractions. This research helps transportation planners and policy makers in planning and developing passenger transportation in Administrative District of Kepulauan Seribu."

""This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book’s overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"-- Provided by publisher."