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Ditemukan 116 dokumen yang sesuai dengan query
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Stilson, Donald W.
San Francisco, Cal.: Holden-Day , 1966
519.2 STI p
Buku Teks  Universitas Indonesia Library
cover
Blank, Leland T.
New York, NY: McGraw-Hill, 1980
519.5 BLA s
Buku Teks  Universitas Indonesia Library
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Daniel, Wayne W.
New York: John Wiley & Sons, 1995
311 DAN b
Buku Teks  Universitas Indonesia Library
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Samsubar Saleh
Yogyakarta: Liberty, 1988
310 SAM s
Buku Teks  Universitas Indonesia Library
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Samsubar Saleh
Yogyakarta: Unit Penerbit dan percetakan AMP YKPN, 1992
310 SAM s
Buku Teks  Universitas Indonesia Library
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Walpole, Ronald E.
New Jersey: Prentice-Hall, 2002
519.2 WAL p
Buku Teks  Universitas Indonesia Library
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Riyanto Dwihatma Setyawan
Abstrak :
Distribusi normal merupakan salah satu distribusi probabilitas data, yang banyak digunakan dalam berbagai bidang karena sifat ideal yang dimilikinya, yaitu distribusi probabilitas data-datanya terpusat di sekitar mean dan distribusi probabilitas data lainnya tersebar secara merata. Namun ada kasus-kasus tertentu di mana distribusi normal sebaiknya tidak digunakan karena akan menghasilkan analisis yang kurang sesuai, terutama ketika data memiliki kemencengan yang kuat dan mempunyai heavy-tail. Pada tugas akhir ini diperkenalkan distribusi probabilitas yang dapat memfasilitasi kemencengan data, yaitu distribusi skew-normal. Distribusi skew-normal merupakan bentuk perluasan dari distribusi normal dengan memasukkan parameter kemencengan. Tugas akhir ini memberikan penjelasan mengenai karakteristik-karakteristik dari distribusi skew-normal univariat dan perluasannya dengan memasukkan parameter location dan scale, serta distribusi skew-normal secara umum dalam bentuk multivariat. Karakteristik-karakteristik yang dimaksud adalah fungsi kepadatan probabilitas, fungsi distribusi, mean, variansi, fungsi pembangkit momen, dan sifat-sifatnya. ......The normal distribution is one of the probability distribution of data, which are widely used in various fields because of the nature of the ideal, namely the probability distribution of data centers around the distribution of average data and other probability is spread evenly. But there are certain cases where the normal distribution should not be used because it will produce less precise analysis, especially when the data has a strong skewness and heavy-tail. This final project will introduce a probability distribution which can facilitate the skewness of data, i.e skew-normal distribution. The skew-normal distribution is an extend form of normal distribution, allowing a skewness parameter. This final project will give an explanation about the chararteristics of the univariate skew-normal distribution and its extend to the location and scale family, and skew-normal distribution in general in multivariate form. The characteristics are probability density function, distribution function, mean, covariance, variance, moment generating function, and the properties of the distribution.
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2011
S740
UI - Skripsi Open  Universitas Indonesia Library
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Huber, Peter J.
Abstrak :
Here is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition.
Philadelphia: Society for Industrial and Applied Mathematics, 1996
e20448590
eBooks  Universitas Indonesia Library
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Csorgo, Miklos
Abstrak :
Provides a comprehensive theory of the approximations of quantile processes in light of recent advances, as well as some of their statistical applications.
Philadelphia: Society for Industrial and Applied Mathematics, 1983
e20451084
eBooks  Universitas Indonesia Library
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Konozsy, Laszlo
Abstrak :
This book gives a mathematical insight--including intermediate derivation steps-into engineering physics and turbulence modeling related to an anisotropic modification to the Boussinesq hypothesis (deformation theory) coupled with the similarity theory of velocity fluctuations. Through mathematical derivations and their explanations, the reader will be able to understand new theoretical concepts quickly, including how to put a new hypothesis on the anisotropic Reynolds stress tensor into engineering practice. The anisotropic modification to the eddy viscosity hypothesis is in the center of research interest, however, the unification of the deformation theory and the anisotropic similarity theory of turbulent velocity fluctuations is still missing from the literature. This book brings a mathematically challenging subject closer to graduate students and researchers who are developing the next generation of anisotropic turbulence models.
Switzerland: Springer Nature, 2019
e20505490
eBooks  Universitas Indonesia Library
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