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Identifiability and Regression Analysis of Biological Systems Models: Statistical and Mathematical Foundations and R Scripts

mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear;

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Statistical Optimization of Biological Systems

screening design, applying response surface methodology, and conducting regression modeling Demonstrates the statistical analysis and optimization;

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Parameter Redundancy and Identifiability

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be;

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Parameter Redundancy and Identifiability

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Statistical Models For Causal Analysis

This volume provides an overview of statistical models commonly used in the causal analyses of non-experimental data in the social and;

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Introductory Statistics with R

methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way;

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Statistical Methods In Biology

Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a;

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Advanced R Statistical Programming and Data Models

data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R;

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Mathematical Statistics

following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear;

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Handbook of Statistical Systems Biology

in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Beginning R Statistical Progmng Language

how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book;

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Foundations of Time Series Analysis and Prediction Theory

This book provides a mathematical foundation for prediction theory and time series analysis using the idea of regression (prediction) and;

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Handbook for Applied Modeling

explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

mathematical foundations and key decision-theoretic principles.;

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Applied Linear Statistical Models

and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be;

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An R Companion to Linear Statistical Models

course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays;

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An R Companion to Linear Statistical Models

course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays;

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Bilinear Regression Analysis

is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis;

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Flexible Bayesian Regression Modelling

econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for;

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Gaussian Process Regression Analysis for Functional Data

analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression;

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A Primer in Biological Data Analysis and Visualization Using R

R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;

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A Primer in Biological Data Analysis and Visualization Using R

R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;

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Understanding Regression Analysis

, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and;

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