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| multiple regression in matlab: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| multiple regression in matlab: Simultaneous Inference in Regression Wei Liu, 2010-10-19 Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods of parameter estimation and hypothesis testing. Simultaneous Inference in Regression provides a thorough overview of the construction methods and applications of simultaneous confidence bands for various inferentia |
| multiple regression in matlab: Introduction to Chemical Engineering Computing Bruce A. Finlayson, 2006-02-10 An innovative introduction to chemical engineering computing As chemical engineering technology advances, so does the complexity of the problems that arise. The problemsthat chemical engineers and chemical engineering students face today can no longer be answered with programs written on a case-by-case basis. Introduction to Chemical Engineering Computing teaches professionalsand students the kinds of problems they will have to solve, the types of computer programs needed to solve these problems, and how to ensure that the problems have been solved correctly. Each chapter in Introduction to Chemical Engineering Computing contains a description of the physicalproblem in general terms and in a mathematical context, thorough step-by-step instructions, numerous examples,and comprehensive explanations for each problem and program. This indispensable text features Excel,MATLAB(r), Aspen PlusTM, and FEMLAB programs and acquaints readers with the advantages of each. Perfect for students and professionals, Introduction to Chemical Engineering Computing gives readers the professional tools they need to solve real-world problems involving: * Equations of state * Vapor-liquid and chemical reaction equilibria * Mass balances with recycle streams * Mass transfer equipment * Process simulation * Chemical reactors * Transfer processes in 1D * Fluid flow in 2D and 3D * Convective diffusion equations in 2D and 3D |
| multiple regression in matlab: MATLAB for Machine Learning Giuseppe Ciaburro, 2017-08-24 Extract patterns and knowledge from your data in easy way using MATLABAbout This Book* Get your first steps into machine learning with the help of this easy-to-follow guide* Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB* Understand how your data works and identify hidden layers in the data with the power of machine learning.Who This Book Is ForThis book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.What You Will Learn* Learn the introductory concepts of machine learning.* Discover different ways to transform data using SAS XPORT, import and export tools,* Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.* Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.* Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.* Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.* Learn feature selection and extraction for dimensionality reduction leading to improved performance.In DetailMATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.Style and approachThe book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. |
| multiple regression in matlab: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| multiple regression in matlab: Applied Linear Statistical Models with Student CD Michael Kutner, Christopher Nachtsheim, John Neter, William Li, 2004-08-10 Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and Comments to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding. |
| multiple regression in matlab: Linear Regression With Matlab James V Stone, 2022-02-15 Linear regression is the first step, and often the only step, required to fit a simple model to data. Supported by a Glossary and tutorial appendices, this is an ideal introduction to regression analysis. Includes Matlab code. |
| multiple regression in matlab: Computational Statistics Handbook with MATLAB Wendy L. Martinez, Angel R. Martinez, 2007-12-20 As with the bestselling first edition, Computational Statistics Handbook with MATLAB, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as |
| multiple regression in matlab: MATLAB Machine Learning Michael Paluszek, Stephanie Thomas, 2016-12-28 This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then providescomplete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning. |
| multiple regression in matlab: Gaussian Processes for Machine Learning Carl Edward Rasmussen, Christopher K. I. Williams, 2005-11-23 A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. |
| multiple regression in matlab: Fitting Models to Biological Data Using Linear and Nonlinear Regression Harvey Motulsky, Arthur Christopoulos, 2004-05-27 Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists. |
| multiple regression in matlab: Linear Time Series with MATLAB and OCTAVE Víctor Gómez, 2019-10-04 This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details. |
| multiple regression in matlab: A Primer of Multivariate Statistics Richard J. Harris, 2001-05-01 Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. |
| multiple regression in matlab: Statistics With Matlab L. Marvin, 2017-11-02 Regression models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals This book develops the linear model of regression taking into account the stages of identification, estimation, diagnosis and prediction. The most important content is the following: - Parametric Regression Analysis - Choose a Regression Function - Linear Regression - Prepare Data - Choose a Fitting Method - Choose a Model or Range of Models - Fit Model to Data - Examine Quality and Adjust the Fitted Model - Predict or Simulate Responses to New Data - Share Fitted Models - Linear Regression Workflow - Linear Regression with Interaction Effects - Interpret Linear Regression Results - Cook's Distance - Coefficient Standard Errors and Confidence Intervals - Coefficient Covariance and Standard Errors - Coefficient Confidence Intervals - Coefficient of Determination (R-Squared) - Durbin-Watson Test - F-statistic - Assess Fit of Model Using F-statistic - t-statistic - Assess Significance of Regression Coefficients Using t-statistic - Hat Matrix and Leverage - Residuals - Assess Model Assumptions Using Residuals - Summary of Output and Diagnostic Statistics - Wilkinson Notation - Linear Mixed-Effects Model Examples - Generalized Linear Model Examples - Generalized Linear Mixed-Effects Model Examples - Repeated Measures Model Examples - Stepwise Regression - Stepwise Regression to Select Appropriate Models - Compare large and small stepwise models - Robust Regression - Reduce Outlier Effects - Robust Regression versus Standard Least-Squares Fit - Ridge Regression - Lasso and Elastic Net - Wide Data via Lasso and Parallel Computing - Partial Least Squares - Linear Mixed-Effects Models - Estimating Parameters in Linear Mixed-Effects Models - Fit Mixed-Effects Spline Regression |
| multiple regression in matlab: Fog Computing for Intelligent Cloud IoT Systems Chandan Banerjee, Anupam Ghosh, Rajdeep Chakraborty, Ahmed A. Elngar, 2024-07-03 FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources. Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service. Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage. This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered. Audience The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems. |
| multiple regression in matlab: Transportation and Environmental Geotechnics Kasinathan Muthukkumaran, Deendayal Rathod, Evangelin Ramani Sujatha, M. Muthukumar, 2022-12-10 This book comprises the select peer-reviewed proceedings of the Indian Geotechnical Conference (IGC) 2021. The contents focus on Geotechnics for Infrastructure Development and Innovative Applications. This book covers topics related application of natural and artificial geosynthetics in shallow foundation bearing capacity enhancement, highway & railway pavements, high speed rail and geo-environmental applications. Topics also covered related to simulation of geosynthetic encased stone column, application of geosynthetic for ground improvement, pore size distribution of compacted expansive soils, MICP, landfills, among others. This book is of interest to those in academia and industry. |
| multiple regression in matlab: Predictive Modeling of Drug Sensitivity Ranadip Pal, 2016-11-15 Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies. - Applies mathematical and computational approaches to biological problems - Covers all aspects of drug sensitivity modeling, starting from initial data generation to final experimental validation - Includes the latest results on drug sensitivity modeling that is based on updated research findings - Provides information on existing data and software resources for applying the mathematical and computational tools available |
| multiple regression in matlab: Automated Data Analysis Using Excel Brian D. Bissett, 2020-08-18 This new edition covers some of the key topics relating to the latest version of MS Office through Excel 2019, including the creation of custom ribbons by injecting XML code into Excel Workbooks and how to link Excel VBA macros to customize ribbon objects. It now also provides examples in using ADO, DAO, and SQL queries to retrieve data from databases for analysis. Operations such as fully automated linear and non-linear curve fitting, linear and non-linear mapping, charting, plotting, sorting, and filtering of data have been updated to leverage the newest Excel VBA object models. The text provides examples on automated data analysis and the preparation of custom reports suitable for legal archiving and dissemination. Functionality Demonstrated in This Edition Includes: Find and extract information raw data files Format data in color (conditional formatting) Perform non-linear and linear regressions on data Create custom functions for specific applications Generate datasets for regressions and functions Create custom reports for regulatory agencies Leverage email to send generated reports Return data to Excel using ADO, DAO, and SQL queries Create database files for processed data Create tables, records, and fields in databases Add data to databases in fields or records Leverage external computational engines Call functions in MATLAB® and Origin® from Excel |
| multiple regression in matlab: Teaching Biostatistics in Medicine and Allied Health Sciences Damian J. J. Farnell, Renata Medeiros Mirra, 2023-06-16 This book discusses the theory and practice of teaching biostatistics to students in the life sciences, in particular medical and dental trainees and researchers, as well as its crucial importance to biomedical research and evidence-based health care. Specific tools and resources to biostatistics teaching (e.g., “R shiny”) are described, and how they can be used effectively to increase interaction with students and improve engagement with the subject. The book is structured into three parts: teaching and learning of statistics in medicine and allied health sciences; the move to online learning and online learning methods, especially in light of the impact of COVID-19; and computer tools and resources. It provides a unique insight into teaching biostatistics to medical and dental students from some of the most prominent biostatisticians who also have a very strong interest in biostatistics pedagogy. Biostatistics teaching is important for maintaining the quality of biomedical research and also in evidence-based medicine, both of which are key to the health and well-being of the world population. This book is particularly useful to readers who are new to the field of biostatistics teaching as well as to more experienced teachers as it presents the latest accounts of the teaching and learning of biostatistics, recent experiences of increased use of online teaching, and useful computer resources and tools for teaching biostatistics. |
| multiple regression in matlab: Statistical Digital Signal Processing and Modeling Monson H. Hayes, 1996-04-19 This new text responds to the dramatic growth in digital signal processing (DSP) over the past decade, and is the product of many years of teaching an advanced DSP course at Georgia Tech. While the focal point of the text is signal modeling, it integrates and explores the relationships of signal modeling to the important problems of optimal filtering, spectrum estimation, and adaptive filtering. Coverage is equally divided between the theory and philosophy of statistical signal processing, and the algorithms that are used to solve related problems. The text reflects the author's philosophy that a deep understanding of signal processing is accomplished best through working problems. For this reason, the book is loaded with worked examples, homework problems, and MATLAB computer exercises. While the examples serve to illustrate the ideas developed in the book, the problems seek to motivate and challenge the student and the computer exercises allow the student to experiment with signal processing algorithms on complex signals. Professor Hayes is recognized as a leader in the signal processing community, particularly for his work in signal reconstruction and image processing. This text is suitable for senior/graduate level courses in advanced DSP or digital filtering found in Electrical Engineering Departments. Prerequisites include basic courses in DSP and probability theory. |
| multiple regression in matlab: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models Jorge Garza Ulloa, 2021-11-30 Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB® and IBM Watson®. - Provides an introduction to Cognitive science, cognitive computing and human cognitive relation to help in the solution of AI Biomedical engineering problems - Explain different Artificial Intelligence (AI) including evolutionary algorithms to emulate natural evolution, reinforced learning, Artificial Neural Network (ANN) type and cognitive learning and to obtain many AI models for Biomedical Engineering problems - Includes coverage of the evolution Artificial Intelligence through Machine Learning (ML), Deep Learning (DL), Cognitive Computing (CC) using MATLAB® as a programming language with many add-on MATLAB® toolboxes, and AI based commercial products cloud services as: IBM (Cognitive Computing, IBM Watson®, IBM Watson Studio®, IBM Watson Studio Visual Recognition®), and others - Provides the necessary tools to accelerate obtaining results for the analysis of injuries, illness, and neurologic diseases that can be detected through the static, kinetics and kinematics, and natural body language data and medical imaging techniques applying AI using ML-DL-CC algorithms with the objective of obtaining appropriate conclusions to create solutions that improve the quality of life of patients |
| multiple regression in matlab: Spatial Analysis Methods and Practice George Grekousis, 2020-06-11 An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results. |
| multiple regression in matlab: Introduction to Multivariate Calibration Alejandro C. Olivieri, 2018-08-20 This book offers an introductory-level guide to the complex field of multivariate analytical calibration, with particular emphasis on real applications such as near infrared spectroscopy. It presents intuitive descriptions of mathematical and statistical concepts, illustrated with a wealth of figures and diagrams, and consistently highlights physicochemical interpretation rather than mathematical issues. In addition, it describes an easy-to-use and freely available graphical interface, together with a variety of appropriate examples and exercises. Lastly, it discusses recent advances in the field (figures of merit, detection limit, non-linear calibration, method comparison), together with modern literature references. |
| multiple regression in matlab: Multiple Classifier Systems Jón Atli Benediktsson, Josef Kittler, 2009-06-02 This book constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. The 52 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 70 initial submissions. The papers are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, stacked generalization and active learning, concept drift, missing values and random forest, SVM ensembles, fusion of graphics, concepts and categorical data, clustering, and finally theory, methods and applications of MCS. |
| multiple regression in matlab: Handbook of Food Process Design, 2 Volume Set Jasim Ahmed, Mohammad Shafiur Rahman, 2012-05-21 In the 21st Century, processing food is no longer a simple or straightforward matter. Ongoing advances in manufacturing have placed new demands on the design and methodology of food processes. A highly interdisciplinary science, food process design draws upon the principles of chemical and mechanical engineering, microbiology, chemistry, nutrition and economics, and is of central importance to the food industry. Process design is the core of food engineering, and is concerned at its root with taking new concepts in food design and developing them through production and eventual consumption. Handbook of Food Process Design is a major new 2-volume work aimed at food engineers and the wider food industry. Comprising 46 original chapters written by a host of leading international food scientists, engineers, academics and systems specialists, the book has been developed to be the most comprehensive guide to food process design ever published. Starting from first principles, the book provides a complete account of food process designs, including heating and cooling, pasteurization, sterilization, refrigeration, drying, crystallization, extrusion, and separation. Mechanical operations including mixing, agitation, size reduction, extraction and leaching processes are fully documented. Novel process designs such as irradiation, high-pressure processing, ultrasound, ohmic heating and pulsed UV-light are also presented. Food packaging processes are considered, and chapters on food quality, safety and commercial imperatives portray the role process design in the broader context of food production and consumption. |
| multiple regression in matlab: Ecological Informatics Friedrich Recknagel, 2013-06-29 Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery |
| multiple regression in matlab: Trends in Environmental Sustainability and Green Energy Jinkeun Kim, Zhe Chen, 2023-03-07 This book consists of selected and peer-reviewed papers of 2022 5th International Conference on Green Energy and Environment Engineering (CGEEE 2022), held in Jeju Island, Korea during July 28-30, 2022. CGEEE 2022 provides a forum for researchers and practitioners in the field of green energy and environment engineering to share ideas, designs, and experiments results. Various topics are covered in this book, such as carbon capture and storage, solar energy, biomass, biofuel, geothermal, sustainable cities, energy savings for vehicular technology, solid waste management, waste minimization, water treatment and reclamation, environmental restoration, and ecological engineering. This book will be beneficial to researchers, educators, practitioners, and policymakers working in the related fields. |
| multiple regression in matlab: Textile Engineering Anindya Ghosh, Bapi Saha, Prithwiraj Mal, 2021-12-22 Focusing on the importance of the application of statistical techniques, this book covers the design of experiments and stochastic modeling in textile engineering. Textile Engineering: Statistical Techniques, Design of Experiments and Stochastic Modeling focuses on the analysis and interpretation of textile data for improving the quality of textile processes and products using various statistical techniques. FEATURES Explores probability, random variables, probability distribution, estimation, significance test, ANOVA, acceptance sampling, control chart, regression and correlation, design of experiments and stochastic modeling pertaining to textiles Presents step-by-step mathematical derivations Includes MATLAB® codes for solving various numerical problems Consists of case studies, practical examples and homework problems in each chapter This book is aimed at graduate students, researchers and professionals in textile engineering, textile clothing, textile management and industrial engineering. This book is equally useful for learners and practitioners in other scientific and technological domains. |
| multiple regression in matlab: Multivariate Time Series Analysis and Applications William W. S. Wei, 2018-12-31 An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering. |
| multiple regression in matlab: Advances in Computing and Data Sciences Mayank Singh, Vipin Tyagi, P.K. Gupta, Jan Flusser, Tuncer Ören, 2023-08-23 This book constitutes the refereed proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023, held in Kolkata, India, during April 27–28, 2023. The 47 full papers included in this book were carefully reviewed and selected from 22 submissions. The papers focus on advances of next generation computing technologies in the areas of advanced computing and data sciences. |
| multiple regression in matlab: Geospatial Optimization of Solar Energy Jay Doorga, Soonil Rughooputh, Ravindra Boojhawon, 2022-02-11 This book provides a comprehensive guide on how geographic information systems (GIS) can be used to optimize solar energy resources. A collection of the latest cutting-edge research is presented which seeks to address the most pressing issues faced by policymakers regarding the planning and exploitation of solar energy. Scientifically robust models are developed to guide researchers on identifying optimum sites for the implementation of solar energy projects. Each methodology presented is accompanied by global case studies, ranging from the small islands of Hawaii and Mauritius to larger countries such as India and Spain. This book is primarily targeted to researchers aspiring to unveil and optimize the solar resource potential of their countries for the benefit of a wider audience, ranging from architects, agro-industrialists, climatologists, and energy experts. |
| multiple regression in matlab: Computer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering Gerard A. Ateshian, Kristin M. Myers, João Manuel R. S. Tavares, 2020-03-31 This book gathers selected, extended and revised contributions to the 16th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, and the 4th Conference on Imaging and Visualization (CMBBE 2019), held on August 14-16, 2019, in New York City, USA. It reports on cutting-edge models and algorithms for studying various tissues and organs in normal and pathological conditions; innovative imaging and visualization techniques; and the latest diagnostic tools. Further topics addressed include: numerical methods, machine learning approaches, FEM models, and high-resolution imaging and real-time visualization methods applied for biomedical purposes. Given the scope of its coverage, the book provides graduate students and researchers with a timely and insightful snapshot of the latest research and current challenges in biomedical engineering, computational biomechanics and biological imaging, as well as a source of inspiration for future research and cross-disciplinary collaborations. |
| multiple regression in matlab: Advanced Computational Intelligence Methods for Processing Brain Imaging Data Kaijian Xia, Yizhang Jiang, Yu-Dong Zhang, Mohammad Khosravi, Yuanpeng Zhang, 2022-11-09 |
| multiple regression in matlab: Engineering Biostatistics Brani Vidakovic, 2017-11-06 Provides a one-stop resource for engineers learning biostatistics using MATLAB® and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB® for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references. Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS also includes: parallel coverage of classical and Bayesian approaches, where appropriate substantial coverage of Bayesian approaches to statistical inference material that has been classroom-tested in an introductory statistics course in bioengineering over several years exercises at the end of each chapter and an accompanying website with full solutions and hints to some exercises, as well as additional materials and examples Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches. |
| multiple regression in matlab: Soft Computing in Textile Engineering Abhijit Majumdar, 2010-11-29 Soft computing refers to a collection of computational techniques which study, model and analyse complex phenomena. As many textile engineering problems are inherently complex in nature, soft computing techniques have often provided optimum solutions to these cases. Although soft computing has several facets, it mainly revolves around three techniques; artificial neural networks, fuzzy logic and genetic algorithms. The book is divided into five parts, covering the entire process of textile production, from fibre manufacture to garment engineering. These include soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture, textile properties and applications and textile quality evaluation. - Covers the entire process of textile production, from fibre manufacture to garment engineering including artificial neural networks, fuzzy logic and genetic algorithms - Examines soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture - Specifically reviews soft computing in relation to textile properties and applications featuring garment modelling and sewing machines |
| multiple regression in matlab: Energy Informatics Bo Nørregaard Jørgensen, Luiz Carlos Pereira da Silva, Zheng Ma, 2023-12-01 This two-volume set LNCS 14467-14468 constitutes the proceedings of the First Energy Informatics Academy Conference, EI.A 2023,held in Campinas, Brazil, in December 2023. The 39 full papers together with 8 short papers included in these volumes were carefully reviewed and selected from 53 submissions. The conference focuses on the application of digital technology and information management to facilitate the global transition towards sustainable and resilient energy systems. |
| multiple regression in matlab: , |
| multiple regression in matlab: Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare Ilker Ozsahin, 2021-11-18 This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development. |
| multiple regression in matlab: Computers for Chemistry Mr. Rohit Manglik, 2024-03-23 EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels. |
| multiple regression in matlab: Soft Computing and Geospatial Techniques in Water Resources Engineering Manish Pandey, K. V. Jayakumar, Manali Pal, Vijay P. Singh, 2024-12-01 This book comprises proceedings of the 28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering (HYDRO 2023). It focuses on emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. The book covers a range of topics including, but not limited to, satellite-derived data for hydrologic applications, Geospatial Information System (GIS) and Remote Sensing (RS) applications in water resources management, rainfall and streamflow prediction, hydro-informatics, data-driven and artificial intelligent-based hydrological modelling, optimization of water resources systems. The book presents these topics in the form of illustrations and tables, thereby providing the readers with an in-depth insight into the recent research. It also addresses fundamental concepts and studies in the field of soft computing and geospatial techniques in water resources engineering, making it a valuable resource for researchers and professionals working in the fields of hydraulics, water resources and coastal engineering. |
英語「multiple」の意味・読み方・表現 | Weblio英和辞書
「multiple」が名詞として使われる場合、ある数に別の数を掛けた結果として得られる数を指す。具体的な例を以下に示す。 ・例文 1. Six is a multiple of three.（6 …
「Multiple」に関連した英語例文の一覧と使い方 - Weblio
He will connect multiple storage devices to multiple host computers. 例文帳に追加 彼が複数のストレージ機器を複数のホストコンピュータに接続する - 京大-NICT …
「相関係数」の英語・英語例文・英語表現 - Weblio和英辞書
｢相関係数｣は英語でどう表現する？【単語】a correlation coefficient...【例文】a multiple correlation coefficient...【その他の表現】correlation coefficient …
英語「specification」の意味・使い方・読み方 | Weblio英和辞書
「specification」の意味・翻訳・日本語 - 詳述、列挙、明細、明細事項、(建物・車などの)設計書、仕様書(しようしよ)｜Weblio英和・和英辞書
英語「multiplier」の意味・使い方・読み方 | Weblio英和辞書
「multiplier」の意味・翻訳・日本語 - (掛け算の)乗数、法｜Weblio英和・和英辞書
英語「multiple」の意味・読み方・表現 | Weblio英和辞書
「multiple」が名詞として使われる場合、ある数に別の数を掛けた結果として得られる数を指す。具体的な例を以下に示す。 ・例文 1. Six is a multiple of three.（6は3の倍数である。） 2. …
「Multiple」に関連した英語例文の一覧と使い方 - Weblio
He will connect multiple storage devices to multiple host computers. 例文帳に追加 彼が複数のストレージ機器を複数のホストコンピュータに接続する - 京大-NICT 日英中基本文データ
「相関係数」の英語・英語例文・英語表現 - Weblio和英辞書
｢相関係数｣は英語でどう表現する？【単語】a correlation coefficient...【例文】a multiple correlation coefficient...【その他の表現】correlation coefficient called {partial correlation …
英語「specification」の意味・使い方・読み方 | Weblio英和辞書
「specification」の意味・翻訳・日本語 - 詳述、列挙、明細、明細事項、(建物・車などの)設計書、仕様書(しようしよ)｜Weblio英和・和英辞書
英語「multiplier」の意味・使い方・読み方 | Weblio英和辞書
「multiplier」の意味・翻訳・日本語 - (掛け算の)乗数、法｜Weblio英和・和英辞書
英語「inspection」の意味・使い方・読み方 | Weblio英和辞書
「inspection」の意味・翻訳・日本語 - 精査、点検、検査、(書類の)閲覧、(公式・正式の)視察、監察、検閲、査閲｜Weblio英和・和英辞書
英語「charm」の意味・使い方・読み方 | Weblio英和辞書
「charm」の意味・翻訳・日本語 - 魅力、人を引きつける力、(女の)器量、色香、なまめかしさ、(まじないの)魔力、魔法、護符、魔よけ、お守り｜Weblio英和・和英辞書
英語「order」の意味・使い方・読み方 | Weblio英和辞書
「order」の意味・翻訳・日本語 - 順序、順、語順、整理、整頓(せいとん)、整列、(…の)状態、調子、(社会の)秩序、治安 ...
英語「round」の意味・読み方・表現 | Weblio英和辞書
the computer rounds the value to next highest multiple of 4 コンピュータは,次の 高位の 4の倍数に丸める
英語「applicant」の意味・使い方・読み方 | Weblio英和辞書
「applicant」の意味・翻訳・日本語 - 志願者、出願者、申し込み者、応募者、候補者｜Weblio英和・和英辞書
