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標(biāo)題: Titlebook: A First Course in Multivariate Statistics; Bernard Flury Textbook 1997 Springer Science+Business Media New York 1997 Multivariate statisti [打印本頁]

作者: HEMI 時(shí)間: 2025-3-21 17:36
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作者: stress-response 時(shí)間: 2025-3-21 23:16
The Multivariate Normal Distribution,118 elements are radioactive, while there also exist some radioactive forms of the stable elements, such as .C, .K and .H. The smallest particle of an element is called an atom of that element. The names of these elements are shortened to one- or two-letter symbols that are displayed on the “periodi

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作者: 迎合 時(shí)間: 2025-3-23 03:05

作者: Ptosis 時(shí)間: 2025-3-23 07:14
The Multivariate Normal Distribution,nuous lattice structures, which when they dissolve, allow the particles to move about separately as positive ions if they have lost an electron(s) or negative ions if they gained electron(s). Covalent compounds exist as groups of atoms (known as molecules), with a fixed ratio of different atoms. The

作者: intuition 時(shí)間: 2025-3-23 11:43
Linear Principal Component Analysis,luoroscopic studies of the human esophagus were made using a 5 per cent suspension of bismuth in water. The bismuth nitrate is highly poisonous in large quantities and was quickly replaced by bismuth carbonate. In 1898, RIEDER administered the bismuth in the form of tablets or cookies; HOLZKNECHT ma

作者: 諷刺滑稽戲劇 時(shí)間: 2025-3-23 17:04
Back Matterely achieve much spotlight or receive external scrutiny..In this chapter, three individuals reflect on their leadership decisions, experiences, and results in their cabinet- and director-level positions at an HBCU. In examining their application of leadership theory in this setting, they discuss lea

作者: Electrolysis 時(shí)間: 2025-3-23 18:57
1431-875X algorithm and principal component analysis * Examples from bMy goal in writing this book has been to provide teachers and students of multi- variate statistics with a unified treatment ofboth theoretical and practical aspects of this fascinating area. The text is designed for a broad readership, inc

作者: 討好美人 時(shí)間: 2025-3-24 00:47
https://doi.org/10.1007/978-3-658-26522-9 which distribution generated the data. In the words of my colleague R.B. Fisher: “In probability, God gives us the parameters and we figure out what is going to happen. In statistics, things have already happened, and we are trying to figure out how God set the parameters.”

作者: 反復(fù)拉緊 時(shí)間: 2025-3-24 02:31
https://doi.org/10.1007/978-3-8349-4361-3ll give rather less attention to aspects of hypotheses testing than traditional textbooks on multivariate statistics. In particular, we will largely ignore problems like optimality criteria or power of tests. Instead, we will focus on a heuristic foundation to the ..-test methodology, for which we are well prepared from Chapter 5.

作者: Meditative 時(shí)間: 2025-3-24 09:56
Wie Sie sich professionell in Szene setzen,. Suppose that the distribution of . in the .th group is given by a ..(.), which may be regarded as the conditional . of ., given .. Assume for simplicity that . is continuous with sample space ?. in each group. Then the joint . of . and ., as seen from Sec tion 2.8, is

作者: compose 時(shí)間: 2025-3-24 10:47
https://doi.org/10.1007/978-3-322-82403-5e the sum of squared deviations of the points from the line, deviation being measured orthogonally to the line. We will discuss Pearson’s approach in Section 8.3; however, it will be treated in a somewhat more abstract way by studying approximations of multivariate random vectors using the criterion of mean-squared error.

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作者: lymphoma 時(shí)間: 2025-3-25 02:54
https://doi.org/10.1007/978-3-658-26522-9idence regions for mean vectors, and related problems for discriminant functions. Then Chapter 7 resumes the classification theory on a more abstract level and gives brief introductions to related topics, such as logistic regression and multivariate analysis of variance.

作者: 匍匐 時(shí)間: 2025-3-25 04:38

作者: Harridan 時(shí)間: 2025-3-25 08:08
https://doi.org/10.1007/978-3-658-26522-9Before introducing the multivariate normal distribution, let us briefly review some important results about the univariate normal.

作者: 加入 時(shí)間: 2025-3-25 13:18
https://doi.org/10.1007/978-3-658-26522-9ng one variable at a time may not be optimal for classification purposes. Hence, it is important that we establish some terminology and acquire a basic knowledge of bivariate and multivariate distribution theory. In particular, we shall discuss notions such as independence of random variables, covar

作者: APNEA 時(shí)間: 2025-3-25 18:19
https://doi.org/10.1007/978-3-658-26522-9ertain distributions, and try to find out what is likely to happen and what is unlikely. Conversely, in statistics we observe data and try to find out which distribution generated the data. In the words of my colleague R.B. Fisher: “In probability, God gives us the parameters and we figure out what

作者: Reverie 時(shí)間: 2025-3-25 20:51
https://doi.org/10.1007/978-3-658-26522-9tly descriptive level, ignoring questions of statistical inference. The mathematical level of Chapter 5 is moderate, and all concepts are explained at great length, hoping that even students without a strong mathematical background will be able to master most of the material. Chapter 6 gives an intr

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作者: Celiac-Plexus 時(shí)間: 2025-3-26 09:29
https://doi.org/10.1007/978-3-322-82403-5 on the aspect of approximation: Given a .-variate random vector (or a “system of points in space,” in Pearson’s terminology), find an optimal approximation in a linear subspace of lower dimension. More specifically, Pearson studied the problem of fitting a line to multivariate data so as to minimiz

作者: Fulminate 時(shí)間: 2025-3-26 15:47
,Den Endspurt intelligent managen — das ,e of normal components. An introductory example has already been given in Chapter 1 (Example 1.3) and has been discussed to some extent in Section 2.8, where the relevant terminology has been established. Before turning to the mathematical setup, let us look at yet another example to motivate the th

作者: PALSY 時(shí)間: 2025-3-26 17:04