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| multiple linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression 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 linear regression matlab: A First Course in Systems Biology Eberhard Voit, 2017-09-05 A First Course in Systems Biology is an introduction for advanced undergraduate and graduate students to the growing field of systems biology. Its main focus is the development of computational models and their applications to diverse biological systems. The book begins with the fundamentals of modeling, then reviews features of the molecular inventories that bring biological systems to life and discusses case studies that represent some of the frontiers in systems biology and synthetic biology. In this way, it provides the reader with a comprehensive background and access to methods for executing standard systems biology tasks, understanding the modern literature, and launching into specialized courses or projects that address biological questions using theoretical and computational means. New topics in this edition include: default modules for model design, limit cycles and chaos, parameter estimation in Excel, model representations of gene regulation through transcription factors, derivation of the Michaelis-Menten rate law from the original conceptual model, different types of inhibition, hysteresis, a model of differentiation, system adaptation to persistent signals, nonlinear nullclines, PBPK models, and elementary modes. The format is a combination of instructional text and references to primary literature, complemented by sets of small-scale exercises that enable hands-on experience, and large-scale, often open-ended questions for further reflection. |
| multiple linear regression matlab: Machine Learning for Social Transformation Jyotsna Kumar Mandal, Debashis De, 2025-01-02 The book includes original unpublished contributions presented at the Eighth International Conference on Emerging Applications of Information Technology (EAIT 2024), organized by Computer Society of India, Kolkata Chapter during 12 – 13 January 2024. The Theme of the conference is “Machine Learning for Social Transformation”. The book covers the topics such as computational intelligence for social transformation, machine learning for healthcare informatics, and machine learning for agriculture and environmental sustainability. |
| multiple linear regression 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 linear regression matlab: A First Course in Systems Biology Eberhard O. Voit, 2012-03-28 A First Course in Systems Biology is a textbook designed for advanced undergraduate and graduate students. Its main focus is the development of computational models and their applications to diverse biological systems. Because the biological sciences have become so complex that no individual can acquire complete knowledge in any given area of specialization, the education of future systems biologists must instead develop a student's ability to retrieve, reformat, merge, and interpret complex biological information. This book provides the reader with the background and mastery of methods to execute standard systems biology tasks, understand the modern literature, and launch into specialized courses or projects that address biological questions using theoretical and computational means. The format is a combination of instructional text and references to primary literature, complemented by sets of small-scale exercises that enable hands-on experience, and larger-scale, often open-ended questions for further reflection. |
| multiple linear regression matlab: Manufacturing and Engineering Technology (ICMET 2014) Ai Sheng, Yizhong Wang, 2014-11-24 Manufacturing and Engineering Technology brings together around 200 peer-reviewed papers presented at the 2014 International Conference on Manufacturing and Engineering Technology, held in San-ya, China, October 17-19, 2014. The main objective of these proceedings is to take the Manufacturing and Engineering Technology discussion a step further. Contributions cover Manufacture, Mechanical, Materials Science, Industrial Engineering, Control, Information and Computer Engineering. Furthermore, these proceedings provide a platform for researchers, engineers, academics as well as industrial professionals from all over the world to present their research results and development activities in Manufacturing Science and Engineering Technology. |
| multiple linear regression matlab: Large-Scale Machine Learning in the Earth Sciences Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, 2017-08-01 From the Foreword: While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book. |
| multiple linear regression matlab: Data Analysis in the Earth Sciences Using Matlab® Gerard V. Middleton, 2000 Exploring the application of MATLAB to the various earth sciences, this text presents an integrated, step-by-step introduction to data analysis and the use of MATLAB. |
| multiple linear regression matlab: Progress in Manufacturing Automation Technology and Application Guang Lin Wang, Hui Feng Wang, Xiang Zhang, Yue Feng Li, 2013-09-10 Special topic volume on Manufacturing Automation Technology and Application |
| multiple linear regression matlab: Fuzzy Engineering and Operations Research Bing-Yuan Cao, Xiang-Jun Xie, 2012-06-30 “Fuzzy Engineering and Operations Research” is the edited outcome of the 5th International Conference on Fuzzy Information and Engineering (ICFIE2011) held during Oct. 15-17, 2011 in Chengdu, China and by the 1st academic conference in establishment of Guangdong Province Operations Research Society (GDORSC) held on Oct. 20, 2011 in Guangzhou, China. The 5th ICFIE2011, built on the success of previous conferences, and the GDORC, first held, are major Symposiums, respectively, for scientists, engineers practitioners and Operation Research (OR) researchers presenting their updated results, developments and applications in all areas of fuzzy information and engineering and OR. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in Fuzziology and OR fields. The book contains 62 papers and is divided into five main parts: “Fuzzy Optimization, Logic and Information”, “The mathematical Theory of Fuzzy Systems”, “Fuzzy Engineering Applications and Soft Computing Methods”, “OR and Fuzziology” and “Guess and Review”. |
| multiple linear regression matlab: Software Process Dynamics and Agility Qing Wang, 2007-05-02 This book constitutes the refereed proceedings of the First International Conference on Software Process, held in Minneapolis, MN, USA, in May 2007. The 28 revised full papers presented together with the abstracts of two keynote addresses cover process content, process tools and metrics, process management, process representation, analysis and modeling, experience report, and simulation modeling. |
| multiple linear regression matlab: Methodologies for Service Life Prediction of Buildings Ana Silva, Jorge de Brito, Pedro Lima Gaspar, 2016-04-28 Presenting an analysis of different approaches for predicting the service life of buildings, this monograph discusses various statistical tools and mathematical models, some of which have rarely been applied to the field. It explores methods including deterministic, factorial, stochastic and computational models and applies these to façade claddings. The models allow (i) identification of patterns of degradation, (ii) estimation of service life, (iii) analysis of loss of performance using probability functions, and (iv) estimation of service life using a probability distribution. The final chapter discusses the differences between the different methodologies and their advantages and limitations. The authors also argue that a better understanding of the service life of buildings results in more efficient building maintenance and reduced environmental costs. It not only provides an invaluable resource to students, researchers and industry professionals interested in service life prediction and sustainable construction, but is also of interest to environmental and materials scientists. |
| multiple linear regression matlab: Computational Methods in Engineering S.P. Venkateshan, Prasanna Swaminathan, 2013-12-09 Computational Methods in Engineering brings to light the numerous uses of numerical methods in engineering. It clearly explains the application of these methods mathematically and practically, emphasizing programming aspects when appropriate. By approaching the cross-disciplinary topic of numerical methods with a flexible approach, Computational Methods in Engineering encourages a well-rounded understanding of the subject. This book's teaching goes beyond the text—detailed exercises (with solutions), real examples of numerical methods in real engineering practices, flowcharts, and MATLAB codes all help you learn the methods directly in the medium that suits you best. - Balanced discussion of mathematical principles and engineering applications - Detailed step-by-step exercises and practical engineering examples to help engineering students and other readers fully grasp the concepts - Concepts are explained through flowcharts and simple MATLAB codes to help you develop additional programming skills |
| multiple linear regression 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. |
| multiple linear regression matlab: Machine Learning in Medicine Ton J. Cleophas, Aeilko H. Zwinderman, 2013-05-30 Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York. |
| multiple linear regression matlab: Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling Jahan B. Ghasemi, 2022-10-20 Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introductory approaches with the latest advanced techniques. Beginning with an introduction of univariate and multivariate statistical analysis, the book then explores multivariate calibration and validation methods. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical chemistry are then discussed, providing useful examples of the techniques in chemistry applications. Drawing on the knowledge of a global team of researchers, this book will be a helpful guide for chemists interested in developing their skills in multivariate data and error analysis. - Provides an introductory overview of statistical methods for the analysis and interpretation of chemical data - Discusses the use of machine learning for recognizing patterns in multidimensional chemical data - Identifies common sources of multivariate errors |
| multiple linear regression matlab: Practical Guide To Chemometrics Paul Gemperline, 2006-04-16 The limited coverage of data analysis and statistics offered in most undergraduate and graduate analytical chemistry courses is usually focused on practical aspects of univariate methods. Drawing in real-world examples, Practical Guide to Chemometrics, Second Edition offers an accessible introduction to application-oriented multivariate meth |
| multiple linear regression matlab: Comprehensive Chemometrics , 2009-03-09 Designed to serve as the first point of reference on the subject, Comprehensive Chemometrics presents an integrated summary of the present state of chemical and biochemical data analysis and manipulation. The work covers all major areas ranging from statistics to data acquisition, analysis, and applications. This major reference work provides broad-ranging, validated summaries of the major topics in chemometrics—with chapter introductions and advanced reviews for each area. The level of material is appropriate for graduate students as well as active researchers seeking a ready reference on obtaining and analyzing scientific data. Features the contributions of leading experts from 21 countries, under the guidance of the Editors-in-Chief and a team of specialist Section Editors: L. Buydens; D. Coomans; P. Van Espen; A. De Juan; J.H. Kalivas; B.K. Lavine; R. Leardi; R. Phan-Tan-Luu; L.A. Sarabia; and J. Trygg Examines the merits and limitations of each technique through practical examples and extensive visuals: 368 tables and more than 1,300 illustrations (750 in full color) Integrates coverage of chemical and biological methods, allowing readers to consider and test a range of techniques Consists of 2,200 pages and more than 90 review articles, making it the most comprehensive work of its kind Offers print and online purchase options, the latter of which delivers flexibility, accessibility, and usability through the search tools and other productivity-enhancing features of ScienceDirect |
| multiple linear regression matlab: Applied Biomechatronics Using Mathematical Models Jorge Garza Ulloa, 2018-06-16 Applied Biomechatronics Using Mathematical Models provides an appropriate methodology to detect and measure diseases and injuries relating to human kinematics and kinetics. It features mathematical models that, when applied to engineering principles and techniques in the medical field, can be used in assistive devices that work with bodily signals. The use of data in the kinematics and kinetics analysis of the human body, including musculoskeletal kinetics and joints and their relationship to the central nervous system (CNS) is covered, helping users understand how the complex network of symbiotic systems in the skeletal and muscular system work together to allow movement controlled by the CNS. With the use of appropriate electronic sensors at specific areas connected to bio-instruments, we can obtain enough information to create a mathematical model for assistive devices by analyzing the kinematics and kinetics of the human body. The mathematical models developed in this book can provide more effective devices for use in aiding and improving the function of the body in relation to a variety of injuries and diseases. - Focuses on the mathematical modeling of human kinematics and kinetics - Teaches users how to obtain faster results with these mathematical models - Includes a companion website with additional content that presents MATLAB examples |
| multiple linear regression matlab: Mathematics for Social Justice Catherine A. Buell, Bonnie Shulman, 2021-11-18 Mathematics instructors are always looking for ways to engage students in meaningful and authentic tasks that utilize mathematics. At the same time, it is crucial for a democratic society to have a citizenry who can critically discriminate between “fake” and reliable news reports involving numeracy and apply numerical literacy to local and global issues. This book contains examples of topics linking math and social justice and addresses both goals. There is a broad range of mathematics used, including statistical methods, modeling, calculus, and basic algebra. The range of social issues is also diverse, including racial injustice, mass incarceration, income inequality, and environmental justice. There are lesson plans appropriate in many contexts: service-learning courses, quantitative literacy/reasoning courses, introductory courses, and classes for math majors. What makes this book unique and timely is that the most previous curricula linking math and social justice have been treated from a humanist perspective. This book is written by mathematicians, for mathematics students. Admittedly, it can be intimidating for instructors trained in quantitative methods to venture into the arena of social dilemmas. This volume provides encouragement, support, and a treasure trove of ideas to get you started. The chapters in this book were originally published as a special issue of the journal, PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies. |
| multiple linear regression matlab: Chemometrics Richard G. Brereton, 2003-07-25 This book is aimed at the large number of people who need to use chemometrics but do not wish to understand complex mathematics, therefore it offers a comprehensive examination of the field of chemometrics without overwhelming the reader with complex mathematics. * Includes five chapters that cover the basic principles of chemometrics analysis. * Provides two chapters on the use of Excel and MATLAB for chemometrics analysis. * Contains 70 worked problems so that readers can gain a practical understanding of the use of chemometrics. |
| multiple linear regression matlab: Design of Video Quality Metrics with Multi-Way Data Analysis Christian Keimel, 2015-12-29 This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling. |
| multiple linear regression matlab: Chemometrics in Spectroscopy Howard Mark, Jerry Workman Jr., 2021-09-30 Chemometrics in Spectroscopy, Revised Second Edition provides the reader with the methodology crucial to apply chemometrics to real world data. The book allows scientists using spectroscopic instruments to find explanations and solutions to their problems when they are confronted with unexpected and unexplained results. Unlike other books on these topics, it explains the root causes of the phenomena that lead to these results. While books on NIR spectroscopy sometimes cover basic chemometrics, they do not mention many of the advanced topics this book discusses. This revised second edition has been expanded with 50% more content on advances in the field that have occurred in the last 10 years, including calibration transfer, units of measure in spectroscopy, principal components, clinical data reporting, classical least squares, regression models, spectral transfer, and more. - Written in the column format of the authors' online magazine - Presents topical and important chapters for those involved in analysis work, both research and routine - Focuses on practical issues in the implementation of chemometrics for NIR Spectroscopy - Includes a companion website with 350 additional color figures that illustrate CLS concepts |
| multiple linear regression 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 linear regression matlab: Engineering Design and Mathematical Modelling Nnamdi Nwulu, Mammo Muchie, 2020-12-17 Engineering Design and Mathematical Modelling: Concepts and Applications consists of chapters that span the Engineering design and mathematical modelling domains. Engineering design and mathematical modelling are key tools/techniques in the Science, Technology and Innovation spheres. Whilst engineering design is concerned with the creation of functional innovative products and processes, mathematical modelling seeks to utilize mathematical principles and concepts to describe and control real world phenomena. Both of these can be useful tools for spurring and hastening progress in developing countries. They are also areas where Africa needs to ‘skill-up’ in order to build a technological base. The chapters in this book cover the relevant research trends in the fields of both engineering design and mathematical modelling. This book was originally published as a special issue of the African Journal of Science, Technology, Innovation and Development. |
| multiple linear regression matlab: Machine Learning GUNRATHI BHARATHKUMAR GOUD, 2024-01-13 Machine learning concepts are going to play important role in providing solutions for applications with usage artificial intelligence. The book provides complete basics and various algorithms involved in solving the problems. |
| multiple linear regression matlab: Computational Intelligence and Biomedical Signal Processing Mitul Kumar Ahirwal, Anil Kumar, Girish Kumar Singh, 2021-05-25 This book presents an interdisciplinary paradigms of computational intelligence techniques and biomedical signal processing. The computational intelligence techniques outlined in the book will help to develop various ways to enhance and utilize signal processing algorithms in the field of biomedical signal processing. In this book, authors have discussed research, discoveries and innovations in computational intelligence, signal processing, and biomedical engineering that will be beneficial to engineers working in the field of health care systems. The book provides fundamental and initial level theory and implementation tools, so that readers can quickly start their research in these interdisciplinary domains. |
| multiple linear regression matlab: Mathematical Modelling and Optimization of Engineering Problems J. A. Tenreiro Machado, Necati Özdemir, Dumitru Baleanu, 2020-02-12 This book presents recent developments in modelling and optimization of engineering systems and the use of advanced mathematical methods for solving complex real-world problems. It provides recent theoretical developments and new techniques based on control, optimization theory, mathematical modeling and fractional calculus that can be used to model and understand complex behavior in natural phenomena including latest technologies such as additive manufacturing. Specific topics covered in detail include combinatorial optimization, flow and heat transfer, mathematical modelling, energy storage and management policy, artificial intelligence, optimal control, modelling and optimization of manufacturing systems. |
| multiple linear regression matlab: Machine Learning Fundamentals Dr.Sunil Tekale Mrs. Nandagiri Kiranmai Ms.Nazima Begum Ms. RACHAKONDA M D H LAVANYA Mr. M. Pramod Mr. B.PREM KUMAR, 2025-03-03 Machine Learning Fundamentals offers an accessible and structured approach to understanding the core concepts, algorithms, and techniques that power the field of machine learning. This book is designed for beginners, students, or professionals transitioning into ML, providing both theoretical foundations and practical examples to help readers build a strong understanding of how machine learning works. |
| multiple linear regression matlab: Linear Regression Analysis Xin Yan, Xiaogang Su, 2009 This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area. --Book Jacket. |
| multiple linear regression matlab: Near Real Time Wind Energy Forecasting Incorporating Wind Tunnel Modeling William David Lubitz, 2005 |
| multiple linear regression matlab: Data Analysis Using Stata Ulrich Kohler (Dr. phil.), Frauke Kreuter, 2005-06-15 This book provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Using data from a longitudinal study of private households in Germany, the book presents many examples from the social sciences to bring beginners up to speed on the use of Stata. -- BACK COVER. |
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
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
