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Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model

This book addresses the prediction of the Water Quality Index (WQI) at Perak River, State of Perak, Malaysia, using a fuzzy multiple linear;

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Optimization Based Model Using Fuzzy and Other Statistical Techniques Towards Environmental Sustainability

concerning environmental sustainability. The examples include using fuzzy weighted multivariate regression to predict the water quality index at;

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Conventional and Fuzzy Regression

treats fuzzy linear regression based on trapezoidal membership functions. In concrete terms, three possible models with trapezoidal fuzzy;

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Modeling in Mathematics

and its application to energy-growth consumption problem. This book also discusses about intuitionistic fuzzy weighted linear regression model;

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Regression Analysis and Linear Models

independent variables. It is used in forecasting and prediction. The models used to understand and build relationships, using the linear predictor;

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Applied Regression Modeling

: Simple linear regression models, including the least squares criterion, how to evaluate model fit, and estimationprediction Multiple linear;

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Advanced Linear Models

prediction. The authors discuss the application of statistical theories and methodologies to various linear models such as the linear regression;

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Transportation Technologies

service quality: a case study of Dar-es Salaam city, Tanzania; intermodal terminal localization using a linear programming approach: the case;

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Deterioration and Optimal Rehabilitation Modelling for Urban Water Distribution Systems

of the distribution system. This book presents a whole-life cost optimisation model for the rehabilitation of water distribution systems. It;

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Simultaneous Inference in Regression

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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Simultaneous Inference in Regression

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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Developing VO2max Prediction Models Using Machine Learning Methods

The purpose of this work is to develop VO2max prediction models by using non-exercise, submaximal and hybrid variables by using Support;

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Linear Regression

This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application;

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Linear Regression Analysis

fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using;

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An Introduction to Generalized Linear Models

focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;

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Handbook of Regression Methods

diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression;

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An Introduction to Fuzzy Sets

An Introduction to Fuzzy Sets provides a comparison of the quality of life in urban, intermediate and rural NUTS III regions in Portugal;

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Statistical Methods for the Analysis of Biomedical Data

The new edition adds a chapter on multiple linear regression in biomedical research, with sections including the multiple linear;

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A Primer on Linear Models

of the general linear model, including multiple regression models, one-way ANOVA, mixed-effects models, and time series models. It then;

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A Primer on Linear Models

of the general linear model, including multiple regression models, one-way ANOVA, mixed-effects models, and time series models. It then;

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Prediction of Software Reliability Using Neural Network & Fuzzy Logic

reliability growth models in various applications. The reported results using neural networks and fuzzy logic can improve the software reliability;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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Univariate and Multivariate General Linear Models

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory;

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Univariate and Multivariate General Linear Models

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory;

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Statistical Methods for Geographers

, extensions of the simple linear model to multiple regression and its assumptions, stepwise logit regression, and canonical and discriminant analysis.;

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