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| linear fit matlab: Curve Fitting with MATLAB. Linear and Non Linear Regression. Interpolation Braselton J., 2016-06-21 Curve Fitting Toolbox(tm) provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. |
| linear fit 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. |
| linear fit matlab: Curve Fitting With Matlab J. Braselton, 2014-09-10 MATLAB Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Linear and Nonlinear Regression Parametric Fitting Parametric Fitting with Library Models Selecting a Model Type Interactively Selecting Model Type Programmatically Using Normalize or Center and Scale Specifying Fit Options and Optimized Starting Points List of Library Models for Curve and Surface Fitting Use Library Models to Fit Data Library Model Types Model Names and Equations Polynomial Models About Polynomial Models Selecting a Polynomial Fit Interactively Selecting a Polynomial Fit at the Command Line Defining Polynomial Terms for Polynomial Surface Fits Exponential Models About Exponential Models Selecting an Exponential Fit Interactively Selecting an Exponential Fit at the Command Line Fourier Series About Fourier Series Models Selecting a Fourier Fit Interactively Selecting a Fourier Fit at the Command Line Gaussian Models About Gaussian Models Selecting a Gaussian Fit Interactively Selecting a Gaussian Fit at the Command Line Power Series About Power Series Models Selecting a Power Fit Interactively Selecting a Power Fit at the Command Line Rational Polynomials About Rational Models Selecting a Rational Fit Interactively Selecting a Rational Fit at the Command Line Sum of Sines Models About Sum of Sines Models Selecting a Sum of Sine Fit Interactively Selecting a Sum of Sine Fit at the Command Line Weibull Distributions About Weibull Distribution Models Selecting a Weibull Fit Interactively Selecting a Weibull Fit at the Command Line Least-Squares Fitting Introduction Error Distributions Linear Least Squares Weighted Least Squares Robust Least Squares Nonlinear Least Squares Custom Linear and Nonlinear Regression Interpolation and Smoothing Nonparametric Fitting Interpolants Interpolation Methods Selecting an Interpolant Fit Interactively Selecting an Interpolant Fit at the Command Line Smoothing Splines About Smoothing Splines Selecting a Smoothing Spline Fit Interactively Selecting a Smoothing Spline Fit at the Command Line Lowess Smoothing About Lowess Smoothing Selecting a Lowess Fit Interactively Selecting a Lowess Fit at the Command Line Fitting Automotive Fuel Efficiency Surfaces at the Command Line Filtering and Smoothing Data About Data Smoothing and Filtering Moving Average Filtering Savitzky-Golay Filtering Local Regression Smoothing Fit Postprocessing Exploring and Customizing Plots Displaying Fit and Residual Plots Viewing Surface Plots and Contour Plots Using Zoom, Pan, Data Cursor, and Outlier Exclusion Customizing the Fit Display Print to MATLAB Figures Removing Outliers Selecting Validation Data Generating Code and Exporting Fits to the Workspace Generating Code from the Curve Fitting Tool Exporting a Fit to the Workspace Evaluating Goodness of Fit How to Evaluate Goodness of Fit Goodness-of-Fit Statistics Residual Analysis Plotting and Analysing Residuals Confidence and Prediction Bounds About Confidence and Prediction Bounds Confidence Bounds on Coefficients Prediction Bounds on Fits Differentiating and Integrating a Fit Surface Fitting Objects and Methods |
| linear fit matlab: CURVE and SURFACE FITTING with MATLAB. LINEAR and NONLINEAR REGRESSION A Ramirez, 2019-07-22 You can fit curves and surfaces to data and view plots with the Curve Fitting app in MATLAB. Is possible: .Create, plot, and compare multiple fits.Use linear or nonlinear regression, interpolation, smoothing, and custom equations..View goodness-of-fit statistics, display confidence intervals and residuals, remove outliers and assess fit with validation data..Automatically generate code to fit and plot curves and surfaces, or export fits to the workspace for further analysis.Curve Fitting app makes it easy to plot and analyze fit at the command line. You can export individual fit to the workspace for further analysis, or you can generate MATLAB code to recreate all fit and plots in your session. By generating code, you can use your interactive curve fitting session to quickly assemble code for curve and surface fit and plots into useful programs.The Curve Fitting app allows convenient, interactive use of Curve Fitting Toolbox functions, without programming. You can, however, access Curve Fitting Toolbox functions directly, and write programs that combine curve fitting functions with MATLAB functions and functions from other toolboxes. This allows you to create a curve fitting environment that is precisely suited to your needs. Models and fit in the Curve Fitting app are managed internally as curve fitting objects. Objects are manipulated through a variety of functions called methods. You can create curve fitting objects, and apply curve fitting methods, outside of the Curve Fitting app |
| linear fit 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®. |
| linear fit matlab: Linear Feedback Control Dingyu Xue, YangQuan Chen, Derek P. Atherton, 2007-01-01 Less mathematics and more working examples make this textbook suitable for almost any type of user. |
| linear fit matlab: Curve and Surface Fitting Peter Lancaster, Kȩstutis Šalkauskas, 1986 The purpose of this book is to reveal the foundations and major features of several basic methods for curve and surface fitting that are currently in use. |
| linear fit matlab: An Introduction to Numerical Methods Abdelwahab Kharab, Ronald Guenther, 2018-09-05 Previous editions of this popular textbook offered an accessible and practical introduction to numerical analysis. An Introduction to Numerical Methods: A MATLAB® Approach, Fourth Edition continues to present a wide range of useful and important algorithms for scientific and engineering applications. The authors use MATLAB to illustrate each numerical method, providing full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes a new chapter on Dynamical Systems and Chaos. Features Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Presents numerous examples and exercises, with selected answers at the back of the book |
| linear fit matlab: Fundamentals of Numerical Computation Tobin A. Driscoll, Richard J. Braun, 2017-12-21 Fundamentals of Numerical Computation?is an advanced undergraduate-level introduction to the mathematics and use of algorithms for the fundamental problems of numerical computation: linear algebra, finding roots, approximating data and functions, and solving differential equations. The book is organized with simpler methods in the first half and more advanced methods in the second half, allowing use for either a single course or a sequence of two courses. The authors take readers from basic to advanced methods, illustrating them with over 200 self-contained MATLAB functions and examples designed for those with no prior MATLAB experience. Although the text provides many examples, exercises, and illustrations, the aim of the authors is not to provide a cookbook per se, but rather an exploration of the principles of cooking. The authors have developed an online resource that includes well-tested materials related to every chapter. Among these materials are lecture-related slides and videos, ideas for student projects, laboratory exercises, computational examples and scripts, and all the functions presented in the book. The book is intended for advanced undergraduates in math, applied math, engineering, or science disciplines, as well as for researchers and professionals looking for an introduction to a subject they missed or overlooked in their education.? |
| linear fit matlab: Accelerating MATLAB Performance Yair M. Altman, 2014-12-11 The MATLAB programming environment is often perceived as a platform suitable for prototyping and modeling but not for serious applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with tho |
| linear fit matlab: Data-Driven Modeling & Scientific Computation J. Nathan Kutz, 2013-08-08 The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing. |
| linear fit matlab: Linear and Nonlinear Regression With Matlab. Fitting Curves and Surfaces to Data Perez C., 2017-08-17 MATLAB allows to work with linear and nonlinear regression models efficiently. It has tools that contemplate the phases of estimation, diagnosis and prediction.MATLAB Curve Fitting Toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting,interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methodsThis book develops the following topics:* Curve Fitting * Surface Fitting * Spline Fitting * Parametric Fitting with Library Models * Polynomial Models * Exponential Models * Fourier Series Models* Gaussian Models* Power Series Models* Rational Models* Sum of Sines Models* Weibull Distribution Models* Least-Squares Fitting* Linear Least Squares * Weighted Least Squares * Robust Least Squares * Nonlinear Least Squares * Robust Fitting* Custom Linear and Nonlinear Regression * Nonparametric Fitting* Interpolation and Smoothing * Smoothing Splines* Filtering and Smoothing Data* Fit Postprocessing * Explore and Customize Plots * Remove Outliers * Select Validation Data * Evaluate a Curve Fit * Evaluate a Surface Fit* Compare Fits Programmatically * Evaluating Goodness of Fit* Residual Analysis * Confidence and Prediction Bounds |
| linear fit matlab: Sensitivity Analysis Emanuele Borgonovo, 2017-04-19 This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques for performing their sensitivity analysis. A variety of sensitivity methods have been developed over the years. This monograph helps the analyst in her/his first exploration of this world. The main goal is to foster the recognition of the crucial role of sensitivity analysis methods as the techniques that allow us to gain insights from quantitative models. Also, exercising rigor in performing sensitivity analysis becomes increasingly relevant both to decision makers and modelers. The book helps the analyst in structuring her/his sensitivity analysis quest properly, so as to obtain the correct answer to the corresponding managerial question. The first part of the book covers Deterministic Methods, including Tornado Diagrams; One-Way Sensitivity Analysis; Differentiation-Based Methods and Local Sensitivity Analysis with Constraints. The second part looks at Probabilistic Methods, including Regression-Based methods, Variance-Based Methods, and Distribution-Based methods. The final section looks at Applications, including capital budgeting, sensitivity analysis in climate change modelling and in the risk assessment of a lunar space mission. |
| linear fit matlab: Exploring Linear Algebra Crista Arangala, 2019-03-07 Exploring Linear Algebra: Labs and Projects with MATLAB® is a hands-on lab manual that can be used by students and instructors in classrooms every day to guide the exploration of the theory and applications of linear algebra. For the most part, labs discussed in the book can be used individually or in a sequence. Each lab consists of an explanation of material with integrated exercises. Some labs are split into multiple subsections and thus exercises are separated by those subsections. The exercise sections integrate problems using Mathematica demonstrations (an online tool that can be used with a browser with Java capabilities) and MATLAB® coding. This allows students to discover the theory and applications of linear algebra in a meaningful and memorable way. Features: The book’s inquiry-based approach promotes student interaction Each chapter contains a project set which consists of application-driven projects emphasizing the chapter’s materials Adds a project component to any Linear Algebra course Explores many applications to a variety of fields that can promote research projects Employs MATLAB® to calculate and explore concepts and theories of linear algebra |
| linear fit matlab: Computational Surface and Roundness Metrology Balasubramanian Muralikrishnan, Jayaraman Raja, 2008-09-11 “Computational Surface and Roundness Metrology” provides an extraordinarily practical and hands-on approach towards understanding the diverse array of mathematical methods used in surface texture and roundness analysis. The book, in combination with a mathematical package or programming language interface, provides an invaluable tool for experimenting, learning, and discovering the many flavors of mathematics that are so routinely taken for granted in metrology. Whether the objective is to understand the origin of that ubiquitous transmission characteristics curve of a filter we see so often yet do not quite comprehend, or to delve into the intricate depths of a deceptively simple problem of fitting a line or a plane to a set of points, this book describes it all (in exhaustive detail). From the graduate student of metrology to the practicing engineer on the shop floor, this book is a must-have reference for all involved in metrology, instrumentation/optics, manufacturing, and electronics. |
| linear fit matlab: A Gentle Introduction to Scientific Computing Dan Stanescu, Long Lee, 2022-05-01 Scientific Computation has established itself as a stand-alone area of knowledge at the borderline between computer science and applied mathematics. Nonetheless, its interdisciplinary character cannot be denied: its methodologies are increasingly used in a wide variety of branches of science and engineering. A Gentle Introduction to Scientific Computing intends to serve a very broad audience of college students across a variety of disciplines. It aims to expose its readers to some of the basic tools and techniques used in computational science, with a view to helping them understand what happens behind the scenes when simple tools such as solving equations, plotting and interpolation are used. To make the book as practical as possible, the authors explore their subject both from a theoretical, mathematical perspective and from an implementation-driven, programming perspective. Features Middle-ground approach between theory and implementation. Suitable reading for a broad range of students in STEM disciplines. Could be used as the primary text for a first course in scientific computing. Introduces mathematics majors, without any prior computer science exposure, to numerical methods. All mathematical knowledge needed beyond Calculus (together with the most widely used Calculus notation and concepts) is introduced in the text to make it self-contained. The erratum document for A Gentle Introduction to Scientific Computing can be accessed here. |
| linear fit matlab: Numerical Methods Anne Greenbaum, Tim P. Chartier, 2012-04-01 A rigorous and comprehensive introduction to numerical analysis Numerical Methods provides a clear and concise exploration of standard numerical analysis topics, as well as nontraditional ones, including mathematical modeling, Monte Carlo methods, Markov chains, and fractals. Filled with appealing examples that will motivate students, the textbook considers modern application areas, such as information retrieval and animation, and classical topics from physics and engineering. Exercises use MATLAB and promote understanding of computational results. The book gives instructors the flexibility to emphasize different aspects—design, analysis, or computer implementation—of numerical algorithms, depending on the background and interests of students. Designed for upper-division undergraduates in mathematics or computer science classes, the textbook assumes that students have prior knowledge of linear algebra and calculus, although these topics are reviewed in the text. Short discussions of the history of numerical methods are interspersed throughout the chapters. The book also includes polynomial interpolation at Chebyshev points, use of the MATLAB package Chebfun, and a section on the fast Fourier transform. Supplementary materials are available online. Clear and concise exposition of standard numerical analysis topics Explores nontraditional topics, such as mathematical modeling and Monte Carlo methods Covers modern applications, including information retrieval and animation, and classical applications from physics and engineering Promotes understanding of computational results through MATLAB exercises Provides flexibility so instructors can emphasize mathematical or applied/computational aspects of numerical methods or a combination Includes recent results on polynomial interpolation at Chebyshev points and use of the MATLAB package Chebfun Short discussions of the history of numerical methods interspersed throughout Supplementary materials available online |
| linear fit matlab: Applied Computational Modelling with MATLAB (Custom Edition) W & WILLIAM YUCHANG (G.), 2018-06-25 This custom edition is published for Central Queensland University. |
| linear fit matlab: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan, 2019-10-10 The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging. |
| linear fit matlab: Uncertainty Analysis for Engineers and Scientists Faith A. Morrison, 2021-01-07 Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Understand how to handle a complete project and how uncertainty enters into various steps. Provides a systematic, worksheet-based process to determine error limits on measured quantities, and all likely sources of uncertainty are explored, measured or estimated. Features instructions on how to carry out error analysis using Excel and MATLAB®, making previously tedious calculations easy. Whether you are new to the sciences or an experienced engineer, this useful resource provides a practical approach to performing error analysis. Suitable as a text for a junior or senior level laboratory course in aerospace, chemical and mechanical engineering, and for professionals. |
| linear fit matlab: Mathematics for the Life Sciences Erin N. Bodine, Suzanne Lenhart, Louis J. Gross, 2014-08-17 An accessible undergraduate textbook on the essential math concepts used in the life sciences The life sciences deal with a vast array of problems at different spatial, temporal, and organizational scales. The mathematics necessary to describe, model, and analyze these problems is similarly diverse, incorporating quantitative techniques that are rarely taught in standard undergraduate courses. This textbook provides an accessible introduction to these critical mathematical concepts, linking them to biological observation and theory while also presenting the computational tools needed to address problems not readily investigated using mathematics alone. Proven in the classroom and requiring only a background in high school math, Mathematics for the Life Sciences doesn't just focus on calculus as do most other textbooks on the subject. It covers deterministic methods and those that incorporate uncertainty, problems in discrete and continuous time, probability, graphing and data analysis, matrix modeling, difference equations, differential equations, and much more. The book uses MATLAB throughout, explaining how to use it, write code, and connect models to data in examples chosen from across the life sciences. Provides undergraduate life science students with a succinct overview of major mathematical concepts that are essential for modern biology Covers all the major quantitative concepts that national reports have identified as the ideal components of an entry-level course for life science students Provides good background for the MCAT, which now includes data-based and statistical reasoning Explicitly links data and math modeling Includes end-of-chapter homework problems, end-of-unit student projects, and select answers to homework problems Uses MATLAB throughout, and MATLAB m-files with an R supplement are available online Prepares students to read with comprehension the growing quantitative literature across the life sciences A solutions manual for professors and an illustration package is available |
| linear fit matlab: Sensors and Actuators Clarence W. de Silva, 2015-07-30 This introductory textbook on engineering system instrumentation emphasizes sensors, transducers, actuators, and devices for component interconnection. The book deals with instrumenting an engineering system through the incorporation of suitable sensors, actuators, and associated interface hardware including filters, amplifiers and other signal modifiers. In view of the practical considerations, design issues, and industrial techniques that are presented throughout the book, and in view of the simplified and snap-shot style presentation of more advanced theory and concepts, it also serves as a useful reference for engineers, technicians, project managers, and other practicing professionals in industry and in research laboratories. |
| linear fit matlab: Measurement, Data Analysis, and Sensor Fundamentals for Engineering and Science Patrick F. Dunn, 2019-02-20 A combination of two texts authored by Patrick Dunn, this set covers sensor technology as well as basic measurement and data analysis subjects, a combination not covered together in other references. Written for junior-level mechanical and aerospace engineering students, the topic coverage allows for flexible approaches to using the combination book in courses. MATLAB® applications are included in all sections of the combination, and concise, applied coverage of sensor technology is offered. Numerous chapter examples and problems are included, with complete solutions available. |
| linear fit matlab: Nonparametric Statistics with Applications to Science and Engineering Paul H. Kvam, Brani Vidakovic, 2007-08-24 A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods. |
| linear fit matlab: Mathematical Modeling Antonio Palacios, 2022-09-19 This book provides qualitative and quantitative methods to analyze and better understand phenomena that change in space and time. An innovative approach is to incorporate ideas and methods from dynamical systems and equivariant bifurcation theory to model, analyze and predict the behavior of mathematical models. In addition, real-life data is incorporated in the derivation of certain models. For instance, the model for a fluxgate magnetometer includes experiments in support of the model. The book is intended for interdisciplinary scientists in STEM fields, who might be interested in learning the skills to derive a mathematical representation for explaining the evolution of a real system. Overall, the book could be adapted in undergraduate- and postgraduate-level courses, with students from various STEM fields, including: mathematics, physics, engineering and biology. |
| linear fit matlab: Biomedical Fluid Dynamics Troy Shinbrot, 2019-04-11 This is a readable and attractively presented textbook on fluid flow in biological systems that includes flow through blood vessels, pulsatile flow, and pattern formation. It bridges the divide among biomedical engineering students between those with an engineering and those with a bio-scientific background, by offering guidance in both physiological and mathematical aspects of the subject. Every chapter includes surprising, amusing, and stimulating effects that the reader may want to experiment on their own. Brief historical vignettes are also included throughout this book. We in the 21st century can so easily turn to the computer to provide a solution, that we forget the extraordinary sparks of insight that scientists in centuries past had to rely on to provide us with the foundational understanding and analytical tools that we now depend on. This book is an attempt to maintain our roots in past investigations, while giving us wings to explore future ones. |
| linear fit matlab: Practical Data Analysis in Chemistry Marcel Maeder, Yorck-Michael Neuhold, 2007-08-10 The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses.* Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered |
| linear fit matlab: Sensor Systems Clarence W. de Silva, 2016-12-19 This book covers sensors and multiple sensor systems, including sensor networks and multi-sensor data fusion. It presents the physics and principles of operation and discusses sensor selection, ratings and performance specifications, necessary hardware and software for integration into an engineering system and signal processing and data analysis. Additionally, it discusses parameter estimation, decision making and practical applications. Even though the book has all the features of a course textbook, it also contains a wealth of practical information on the subject. |
| linear fit matlab: Elementary Linear Algebra Stanley I. Grossman, 1994 Intended for the first course in linear algebra, this widely used text balances mathematical techniques and mathematical proofs. It presents theory in small steps and provides more examples and exercises involving computations than competing texts. |
| linear fit matlab: Differential Equations and Linear Algebra Gilbert Strang, 2015-02-12 Differential equations and linear algebra are two central topics in the undergraduate mathematics curriculum. This innovative textbook allows the two subjects to be developed either separately or together, illuminating the connections between two fundamental topics, and giving increased flexibility to instructors. It can be used either as a semester-long course in differential equations, or as a one-year course in differential equations, linear algebra, and applications. Beginning with the basics of differential equations, it covers first and second order equations, graphical and numerical methods, and matrix equations. The book goes on to present the fundamentals of vector spaces, followed by eigenvalues and eigenvectors, positive definiteness, integral transform methods and applications to PDEs. The exposition illuminates the natural correspondence between solution methods for systems of equations in discrete and continuous settings. The topics draw on the physical sciences, engineering and economics, reflecting the author's distinguished career as an applied mathematician and expositor. |
| linear fit matlab: Trends In Welding Research Stan A. David, 2006-01-01 |
| linear fit matlab: Hydroinformatics Praveen Kumar, Mike Folk, Momcilo Markus, Jay C. Alameda, 2005-11-02 Modern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling introduces the tools, approache |
| linear fit matlab: Optical Trapping (Laser Tweezers) and Nanosurgery (Laser Scissors) Michael W. Berns, Daryl Preece, Halina Rubinsztein-Dunlop, Monika Ritsch-Marte, 2022-02-03 |
| linear fit matlab: Bridging the Gap Before and After Birth: Methods and Technologies to Explore the Functional Neural Development in Humans Marika Berchicci, Silvia Comani, 2015-12-18 Infant brain damage is a serious condition that affects millions of babies each year. The period from late gestation to the first year of life is the most critical one for the development of central and autonomous nervous systems. Medical conditions such as preterm birth may compromise brain function and the end result usually is that the baby may experience long-term neurological problems related to a wide range of psychological, physical and functional complications, with consequent life-long burdens for the individuals and their families, and a high socio-economic impact for the health care system and the whole of society. During the last years, several techniques have been employed to monitor the brain functional development in utero and after birth. As well, various analytical methods have been used to understand the functional maturation of the brain and the autonomous nervous system. However, in spite of the rapid improvement of diagnostic methods and procedures, there is still a widely recognized, severe shortage of clinically viable means for the high quality monitoring of the brain function in early life with a direct relevance to acute neurological illness and future neurocognitive outcomes. The studies collected in this e-book document the most recent advancements in monitoring systems, analytical methods and clinical diagnostic procedures that contribute to increase our knowledge of the functional development of the human brain and autonomous nervous system during pregnancy and after birth, with the ultimate goal of reducing fetal impairment and improving healthcare in the neonatal and infant period. |
| linear fit matlab: Heat Transfer Principles and Applications Charles H. Forsberg, 2020-03-20 Heat Transfer Principles and Applications is a welcome change from more encyclopedic volumes exploring heat transfer. This shorter text fully explains the fundamentals of heat transfer, including heat conduction, convection, radiation and heat exchangers. The fundamentals are then applied to a variety of engineering examples, including topics of special and current interest like solar collectors, cooling of electronic equipment, and energy conservation in buildings. The text covers both analytical and numerical solutions to heat transfer problems and makes considerable use of Excel and MATLAB® in the solutions. Each chapter has several example problems and a large, but not overwhelming, number of end-of-chapter problems. - A medium-sized text providing a thorough treatment of heat transfer fundamentals - Includes both analytical and numerical solutions of heat transfer problems - Extensive use of Excel and Matlab - Includes a chapter on mass transfer - Includes a unique chapter of multimode problems to enhance the students problem-solving skills. Minimal information is given in the problem statements. Students must determine the relevant modes of heat transfer (conduction, convection, radiation) and, using the earlier chapters, must determine the appropriate solution technique. For example, they must decide whether the problem is steady-state or transient. They must determine the applicable convection coefficients and material properties. They must decide which solution approach (e. g., analytical or numerical) is appropriate |
| linear fit matlab: An Introduction to Matlab and Mathcad Troy Siemers, 2014 This textbook provides an introduction to programming and problem solving using both Matlab and Mathcad. We provide a balanced selection of introductory exercises and real-world problems (i.e. no “contrived” problems). We include many examples and screenshots to guide the reader. We assume no prior knowledge of Matlab or Mathcad.--Publisher's description. |
| linear fit matlab: Pattern Recognition Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López, 2017-05-31 This book constitutes the refereed proceedings of the 9th Mexican Conference on Pattern Recognition, MCPR 2017, held in Huatulco, Mexico, in June 2017. The 29 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on pattern recognition and artificial intelligence techniques, image processing and analysis, robotics and remote sensing, natural language processing and recognition, applications of pattern recognition. |
| linear fit matlab: Digital Signal Processing Kaluri V. Rangarao, Ranjan K. Mallik, 2006-02-22 Digital signal processing is essential for improving the accuracy and reliability of a range of engineering systems, including communications, networking, and audio and video applications. Using a combination of programming and mathematical techniques, it clarifies, or standardizes the levels or states of a signal, in order to meet the demands of designing high performance digital hardware. Written by authors with a wealth of practical experience working with digital signal processing, this text is an excellent step-by-step guide for practitioners and researchers needing to understand and quickly implement the technology. Split into six, self-contained chapters, Digital Signal Processing: A Practitioner’s Approach covers: basic principles of signal processing such as linearity, stability, convolution, time and frequency domains, and noise; descriptions of digital filters and their realization, including fixed point implementation, pipelining, and field programmable gate array (FGPA) implementation; Fourier transforms, especially discrete (DFT), and fast Fourier transforms (FFT); case studies demonstrating difference equations, direction of arrival (DoA), and electronic rotating elements, and MATLAB programs to accompany each chapter. A valuable reference for engineers developing digital signal processing applications, this book is also a useful resource for electrical and computer engineering graduates taking courses in signal processing. |
| linear fit matlab: A Practical Guide to Laboratory Optics Andri M. Gretarsson, 2021-06-03 Learn the essential skills of laboratory optics and its underlying theoretical framework with seven key experiments. |
| linear fit matlab: Remote Sensing of Volcanic Processes and Risk Francesca Cigna, Deodato Tapete, Zhong Lu, Susanna K. Ebmeier, 2021-03-17 Remote sensing data and methods are increasingly being implemented in assessments of volcanic processes and risk. This happens thanks to their capability to provide a spectrum of observation and measurement opportunities to accurately sense the dynamics, magnitude, frequency, and impacts of volcanic activity. This book includes research papers on the use of satellite, aerial, and ground-based remote sensing to detect thermal features and anomalies, investigate lava and pyroclastic flows, predict the flow path of lahars, measure gas emissions and plumes, and estimate ground deformation. The multi-disciplinary character of the approaches employed for volcano monitoring and the combination of a variety of sensor types, platforms, and methods that come out from the papers testify to the current scientific and technology trends toward multi-data and multi-sensor monitoring solutions. The added value of the papers lies in the demonstration of how remote sensing can improve our knowledge of volcanoes that pose a threat to local communities; back-analysis and critical revision of recent volcanic eruptions and unrest periods; and improvement of modeling and prediction methods. Therefore, the selected case studies also demonstrate the societal impact that this scientific discipline can potentially have on volcanic hazard and risk management. |
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Linear – Plan and build products
Linear is a purpose-built tool for modern product development. Streamline issues, projects, and product roadmaps.
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Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
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Linear MCP server – Changelog
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Today, thousands of teams around the globe — from early-stage startups to public companies — use Linear to build their products. Linear helps them to focus on what they do best: Crafting …
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The quality of a product is driven by both the talent of its creators and how they feel while they’re crafting it. To bring back the right focus, these are the foundational and evolving ideas Linear is …
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With its meticulous design, breathtaking speed and purpose-built workflows, Linear unlocks your team’s full potential. It is the tool of choice for high-performance teams to build products better.
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Linear Projects give Mercury a source-of-truth across all their work. Read story. Watershed switched to Linear to accelerate their team. Read story. Linear keeps Retool's teams customer …
Building our way - Linear Blog
Jun 10, 2025 · Linear is unique because it actually manages all these workflows in a structured way and in a single product, bringing teams together. We see this end-to-end workflow as the …
