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Survey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least three audiences: (1) Students seeking a more in-depth understanding of applied sampling either through a second semester-long course or by way of a supplementary reference; (2) Survey statisticians searching for practical guidance on how to apply concepts learned in theoretical or applied sampling courses; and (3) Social scientists and other survey practitioners who desire insight into the statistical thinking and steps taken to design, select, and weight random survey samples.
Several survey data sets are used to illustrate how to design samples, to make estimates from complex surveys for use in optimizing the sample allocation, and to calculate weights. Realistic survey projects are used to demonstrate the challenges and provide a context for the solutions. The book covers several topics that either are not included or are dealt with in a limited way in other texts. These areas include: sample size computations for multistage designs; power calculations related to surveys; mathematical programming for sample allocation in a multi-criteria optimization setting; nuts and bolts of area probability sampling; multiphase designs; quality control of survey operations; and statistical software for survey sampling and estimation. An associated R package, PracTools, contains a number of specialized functions for sample size and other calculations. The data sets used in the book are also available in PracTools, so that the reader may replicate the examples or perform further analyses.
With the proliferation of VHDL, the reference material also grew in the same order. Today there is good amount of scholarly literature including many books describing various aspects of VHDL. However, an in-depth review of these books reveals a different story. Many of them have emerged simply as an improved version of the manual. While some of them deal with the system design issues, they lack appropriate exemplifying to illustrate the concepts. Others give large number of examples, but lack the VLSI system design issues. In nutshell, the fact which gone unnoticed by most of the books, is the growth of the VLSI is not merely due to the language itself, but more due to the development of large number of third party tools useful from the FPGA or semicustom ASIC realization point of view. In the proposed book, the authors have synergized the VHDL programming with appropriate EDA tools so as to present a full proof system design to the readers. In this book along with the VHDL coding issues, the simulation and synthesis with the various tool sets enables the potential reader to visualize the final design. The VHDL design codes have been synthesized using different third party tools such as Xilinx Web pack Ver.11, Modelsim PE, Leonrado Spectrum and Synplify Pro. Mixed flow illustrated by using the above mentioned tools presents an insight to optimize the design with reference to the spatial, temporal and power metrics.
The Sufi Muslim orders to which the vast majority of Senegalese belong are the most significant institutions of social organization in the country. While studies of Islam and politics have tended to focus on the destabilizing force of religiously based groups, the author argues that in Senegal the orders have been a central component of a political system that has been among the most stable in Africa. Focusing on a regional administrative centre, he combines a detailed account of grassroots politics with an analysis of national and international forces to examine the ways in which the internal dynamics of the orders shape the exercise of power by the Senegalese. This is a major study that should be read by every student of Islam and politics as well as of Africa.
Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.
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