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5 edition of Linear statistical analysis of discrete data found in the catalog.

Linear statistical analysis of discrete data

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  • 40 Currently reading

Published by Wiley in New York .
Written in English

    Subjects:
  • Contingency tables.

  • Edition Notes

    StatementMikel Aickin.
    SeriesWiley series in probability and mathematical statistics.
    Classifications
    LC ClassificationsQA277 .A5 1983
    The Physical Object
    Paginationxvi, 358 p. :
    Number of Pages358
    ID Numbers
    Open LibraryOL3168538M
    ISBN 100471097748
    LC Control Number83010609

      Along with describing the necessary statistical theory, the authors illustrate the practical application of the techniques to a large number of substantive problems, including how to organize data, conduct an analysis, produce informative graphs, and evaluate what the graphs reveal about the data. The first part of the book contains. The objective of a statistical analysis of longitudinal data is usually to model the expected value of the response variable as either a linear or nonlinear function of a set of explanatory variables. Statistical analysis of longitudinal data requires an accounting for possible between-subject heterogeneity and within-subject correlation.


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Linear statistical analysis of discrete data by Mikel Aickin Download PDF EPUB FB2

STATISTICAL METHODS FOR LINEAR STATISTICAL FORECASTING ANALYSIS OF DISCRETE DATA by B. Abraham, University of Waterloo, and by M.

Aickin J. Ledolter, University of Iowa This book analyzes the results of contingency tables, This book presents the statistical methods and models integrating theory, application, and computation.

The Statistical Analysis of Discrete Data provides an introduction to current statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prerequisites are linear algebra and elementary advanced calculus.

Classes of Statistical Problems 1 Examples 3 Review of Discrete Distributions 14 2 Univariate Discrete Responses 21 Binomial Responses 21 A. Point Estimation ofp, u, and A 22 The Estimator p Yn 22 Bayes Estimators of p 24 Gamma Minimax and Hierarchical Bayes Estimators ofp 28 Empirical Bayes and Pseudo Bayes Estimators.

Welcome to STAT Analysis of Discrete Data. Section. The focus of this class is a multivariate analysis of discrete data. Here we deal with data which are discretely measured responses such as counts, proportions, nominal variables, ordinal variables, discrete interval variables with few values, continuous variables grouped into a small.

10 DISCRETE DATA ANALYSIS where O i is the observed number in the sample that fall into the ith category (O i npˆ i), and E i np0 i is the number of individuals expected to be in the ith category when H 0 is true. The Pearson statistic can also be computed as the sum of the squared residuals: χ2 s Xr i1 Z2 i, where Z i (O i E i) E.

Course Objectives. To develop a critical approach to the analysis of contingency tables. To examine the basic ideas and methods of generalized linear models. To link logit and log-linear methods with generalized linear models. To develop basic facility in the analysis of discrete data using SASR.

Statistical Analysis Handbook A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools Discrete Distributions Binomial distribution Poisson regression for count data Non-linear regression Smoothing and Generalized Additive Models (GAM) File Size: 1MB.

A book that maybe useful is Categorical Data Analysis Using the SAS System, Second Edition by Stokes, Davis, and Koch (, SAS Institute). This book is not required, but can be helpful for graduate students who anticipate doing a lot of categorical data analysis in SAS.

This book was written by biostatisticians and has a strong biostatistical. - Buy Linear Statistical Analysis of Discrete Data (Wiley Series in Probability and Statistics Applied Probability and Statistics Section) book online at best prices in India on Read Linear Statistical Analysis of Discrete Data (Wiley Series in Probability and Statistics Applied Probability and Statistics Section) book reviews author details and more at Author: Mikel Aickin.

The Statistical Analysis of Discrete Data provides an introduction to cur­ rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data.

The book's mathematical prereq­ uisites are linear algebra and elementary advanced Cited by:   the data generated from this experiment would be discrete. Usually the term "discrete" would apply to a random variable (numeric rather than distinct categories of things), which took a countable number of distinct values.

An example of a discrete variable is a count (counts clearly satisfy the requirement of being countable). The Statistical Analysis of Discrete Data provides an introduction to cur­ rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq­ uisites are linear algebra and elementary advanced. The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced calculus.

Analysis of data obtained from discrete variables requires the use of specific statistical tests which are different from those used to assess continuous variables (such as cardiac output, blood pressure, or PaO 2) which can assume an infinite range of values. The analysis of continuous variables is.

Contingency table analysis and log-linear model: There are two primary methods available for the analysis of discrete response data. The first one applies to situations in which we have discrete explanatory variables and discrete responses and is known as Contingency Table Analysis.

The model for this is covered in detail in this section. This book introduces the statistical theory, analysis methods, and computation techniques for exact analysis of discrete data. After reviewing the relevant discrete distributions, the author develops the exact methods from the ground up in a conceptually integrated manner.

The book provides a host of useful topics and techniques for students in the niche of statistical analysis and data display. The book can adopted as textbook for an intermediate level statistics course.

Ahmed, Technometrics, Vol. 58 (3), August, ). Recent developments in statistical theory, computational algorithms, and social science methodology make it possible to analyze discrete data in a valid manner without recourse to models based on continuity (or normality) assumptions.

This chapter presents an overview of one approach to the analysis of discrete data, that based on log-linear Cited by:   Computational Statistics Data Analysis 16 () North-Holland Analysis of discrete data: Rerandomization methods and complexity Jenny Baglivo Boston College, Chestnut Hill, MAUSA Donald Olivier Harvard University School of Public Health, Boston, MAUSA Marcello Pagano Harvard University School of Public Health and Dana -Farber Cancer.

STATCategorical Data Analysis Generalized Linear Models - Lecture Notes The goal of this course is to teach methods for the analysis of discrete response data and to develop a general framework for the analysis of dis-crete data and other data types for which the assumptions of the classical linear model (CLM) do not hold.

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their book.

Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software by: "Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields.

A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject.

" --D. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level. This course starts with summarization and association measures for discrete data and then introduces the Generalised Linear Model as a unified regression tool for categorical countcontinuous data etc.

Alternative regression formulations for binary data based on the binary choice motivation are introduced. The course concludes with a review of Course Status: Ongoing. application to a broader range of data problems has been largely overlooked.

The purpose of this article is to show how Bayesian graphical models unify and simplify many standard discrete data problems such as Bayesian log linear modeling with either complete or incomplete data, closed population estimation, and double sampling.

Statistical Methods for Discrete Response, Time Series, and Panel Data Classical linear regression and time series models are workhorses of modern statistics, with applications in nearly all areas of data science. This course takes a more advanced look at both classical linear and linear regression models, including techniques for studying causality, and introduces the fundamental techniques.

On the other hand, there isn't necessarily any problem for the generalized linear model to handle count data, so long as the distribution used falls within the exponential family. The prototypical GLM for count data is Poisson regression.

That may be a good option for your data. A broad collection of data-analysis procedures and techniques are presented, covering a wide variety of biological research, such as physiology, genetics, ecology, behavior, morphology.

; The most comprehensive treatment available includes coverage of the basics of statistical analysis, and also the following topics rarely or never found in statistics books for biologists. It begins with a treatment of random processes in discrete time and proceeds to develop such fundamental topics as Wiener filtering, linear prediction, ARMA models, and subspace methods with a depth that is rare even in books more specifically devoted to these subjects.

An introduction to methods of higher order moments is also s: 3. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data Volume of Chapman HallCRC Texts in Statistical Science: Authors: Michael Friendly, David Meyer: Edition: illustrated: Publisher: CRC Press, ISBN:Length: pages: SubjectsAuthors: Michael Friendly, David Meyer.

Analysis of correlated data. Statistical analysis of longitudinal data requires methods that can properly account for the intra-subject cor-relation of response measurements. If such correlation is ignored then inferences such as statistical tests or con dence intervals can be grossly invalid.

Time-varying covariates. My primary research interests have been in categorical data analysis. Books. Foundations of Statistics for Data Scientists, with R and Python, CRC Press ().

Foundations of Linear and Generalized Linear Models, Wiley (). Strength in Numbers: The Rising of Academic Statistics Departments in the U. Springer (), co-edited with Xiao. Applied Generalized Linear Mixed Models: Continuous and Discrete Data For the Social and Behavioral Sciences Febru Multiple regression and ANOVA dominated statistical analysis of data in the social be re-analyzed later in the book.

In Sectiona general overview of estimation. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (Chapman HallCRC Texts in Statistical Science Book ) - Kindle edition by Friendly, Michael, Meyer, David.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Discrete Data Analysis with R Reviews: 6.

An Applied Treatment of Modern Graphical Methods for Analyzing Categorical Data. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data.

It explains how to use graphical methods for exploring data, spotting unusual Reviews: 6. Description. Geophysical Data Analysis: Discrete Inverse Theory is an introductory text focusing on discrete inverse theory that is concerned with parameters that either are truly discrete or can be adequately approximated as discrete.

Organized into 12 chapters, the books opening chapters provide a general background of inverse problems and. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, ) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical.

Book Description. Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data.

The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. "A 'must-have' book for anyone expecting to do research andor applications in categorical data analysis.

" Statistics in Medicine on Categorical Data Analysis, First Edition. The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences.

Linear Statistical Analysis Of Discrete Data (Probability Mathematical Statistics) Mikel Aickin, AIDS to Teaching And Learning (The Commonwealth and International Library. Education and Educational Research Division)|H. Coppen, The World Market for Nicotine and Its Salts: A Global Trade Perspective|Icon Group, Manufacturing Powerlessness in the Black Diaspora: Inner-City Youth and 10().

SinceGeophysical Data Analysis has filled the need for a short, concise reference on inverse theory for individuals who have an intermediate background in science and mathematics.

The new edition maintains the accessible and succinct manner for which it is known, with the addition of: MATLAB examples and problem setsAdvanced color graphicsCoverage of new topics, including .A good part of the book can be understood without very specialized statistical knowledge.

It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level 5/5(4). Discrete and Continuous Data Stats Assignment Homework Help.

Discrete and Continuous Data. Statistical inference through the analysis of observational studies or designed experiments is used in many scientific areas. The data gathered may be discrete or continuous, depending on the area of application.

For example, a chemical engineer may.