Practical Handbook of Curve Fitting

Practical Handbook of Curve Fitting

Author: Sandra Arlinghaus

Publisher: CRC Press

Published: 1994-05-09

Total Pages: 282

ISBN-13: 9780849301438

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Practical Handbook of Curve Fitting is a reference work assembled by Arlinghaus and a set of editors with well over a century of combined experience in various disciplines and activities related to curve fitting. The book demonstrates how to analyze World data bases and graph and map the results. Default settings in software packages can produce attractive graphs of data imported into the software. Often, however, the default graph has no equation associated with it and cannot therefore be used as a tool for further analysis or projection of the data. The same software can often be used to generate curves from equations. The reader is shown directly, and in a series of steps, how to fit curves to data using Lotus 1-2-3. There are traditional unbounded curve fitting techniques-lines of least squares, exponentials, logistic curves, and Gompertz curves. There is the bounded curve fitting technique of cubic spline interpolation. Beyond these, there is a detailed application of Feigenbaum's graphical analysis from chaos theory, and there is a hint as to how fractal geometry might come into play. Curve fitting algorithms take on new life when they are actually used on real-world data. They are used in numerous worked examples drawn from electronic data bases of public domain information from the Stars data base of The World Bank and from the WRD data base of the World Resources Institute. The applications are current and reflect a state-of-the-art interest in the human dimensions of global change.


Best Fit Lines & Curves

Best Fit Lines & Curves

Author: Alan R. Jones

Publisher: Routledge

Published: 2018-10-09

Total Pages: 498

ISBN-13: 1351661442

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Best Fit Lines and Curves, and Some Mathe-Magical Transformations (Volume III of the Working Guides to Estimating & Forecasting series) concentrates on techniques for finding the Best Fit Line or Curve to some historical data allowing us to interpolate or extrapolate the implied relationship that will underpin our prediction. A range of simple ‘Moving Measures’ are suggested to smooth the underlying trend and quantify the degree of noise or scatter around that trend. The advantages and disadvantages are discussed and a simple way to offset the latent disadvantage of most Moving Measure Techniques is provided. Simple Linear Regression Analysis, a more formal numerical technique that calculates the line of best fit subject to defined ‘goodness of fit’ criteria. Microsoft Excel is used to demonstrate how to decide whether the line of best fit is a good fit, or just a solution in search of some data. These principles are then extended to cover multiple cost drivers, and how we can use them to quantify 3-Point Estimates. With a deft sleight of hand, certain commonly occurring families of non-linear relationships can be transformed mathe-magically into linear formats, allowing us to exploit the powers of Regression Analysis to find the Best Fit Curves. The concludes with an exploration of the ups and downs of seasonal data (Time Series Analysis). Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.


Fitting Models to Biological Data Using Linear and Nonlinear Regression

Fitting Models to Biological Data Using Linear and Nonlinear Regression

Author: Harvey Motulsky

Publisher: Oxford University Press

Published: 2004-05-27

Total Pages: 352

ISBN-13: 9780198038344

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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.


Numerical Methods of Curve Fitting

Numerical Methods of Curve Fitting

Author: P. G. Guest

Publisher: Cambridge University Press

Published: 2012-12-13

Total Pages: 439

ISBN-13: 1107646952

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This 1961 book provides information on the methods of treating series of observations; the field covered embraces portions of both statistics and numerical analysis.


Matrix, Numerical, and Optimization Methods in Science and Engineering

Matrix, Numerical, and Optimization Methods in Science and Engineering

Author: Kevin W. Cassel

Publisher: Cambridge University Press

Published: 2021-03-04

Total Pages: 728

ISBN-13: 1108787622

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Address vector and matrix methods necessary in numerical methods and optimization of linear systems in engineering with this unified text. Treats the mathematical models that describe and predict the evolution of our processes and systems, and the numerical methods required to obtain approximate solutions. Explores the dynamical systems theory used to describe and characterize system behaviour, alongside the techniques used to optimize their performance. Integrates and unifies matrix and eigenfunction methods with their applications in numerical and optimization methods. Consolidating, generalizing, and unifying these topics into a single coherent subject, this practical resource is suitable for advanced undergraduate students and graduate students in engineering, physical sciences, and applied mathematics.


Fitting Models to Biological Data Using Linear and Nonlinear Regression

Fitting Models to Biological Data Using Linear and Nonlinear Regression

Author: Harvey Motulsky

Publisher: Oxford University Press

Published: 2004-05-27

Total Pages: 352

ISBN-13: 0198038348

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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.


Curve and Surface Fitting with Splines

Curve and Surface Fitting with Splines

Author: Paul Dierckx

Publisher: Oxford University Press

Published: 1995

Total Pages: 308

ISBN-13: 9780198534402

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The fitting of a curve or surface through a set of observational data is a very frequent problem in different disciplines (mathematics, engineering, medicine, ...) with many interesting applications. This book describes the algorithms and mathematical fundamentals of a widely used software package for data fitting with (tensor product) splines. As such it gives a survey of possibilities and benefits but also of the problems to cope with when approximating with this popular type of function. In particular it is demonstrated in detail how the properties of B-splines can be fully exploited for improving the computational efficiency and for incorporating different boundary or shape preserving constraints. Special attention is also paid to strategies for an automatic and adaptive knot selection with intent to obtain serious data reductions. The practical use of the smoothing software is illustrated with many examples, academic as well as taken from real life.


A Book of Curves

A Book of Curves

Author: Edward Harrington Lockwood

Publisher: Cambridge University Press

Published: 1967

Total Pages: 290

ISBN-13: 9781001224114

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Describes the drawing of plane curves, cycloidal curves, spirals, glissettes and others.


Best Fit Lines and Curves

Best Fit Lines and Curves

Author: Alan Jones

Publisher:

Published: 2018

Total Pages: 432

ISBN-13: 9781138065000

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Best Fit Lines and Curves, and Some Mathe-Magical Transformations (Volume III of the Working Guides to Estimating & Forecasting series) concentrates on techniques for finding the Best Fit Line or Curve to some historical data allowing us to interpolate or extrapolate the implied relationship that will underpin our prediction. A range of simple 'Moving Measures' are suggested to smooth the underlying trend and quantify the degree of noise or scatter around that trend. The advantages and disadvantages are discussed and a simple way to offset the latent disadvantage of most Moving Measure Techniques is provided. Simple Linear Regression Analysis, a more formal numerical technique that calculates the line of best fit subject to defined 'goodness of fit' criteria. Microsoft Excel is used to demonstrate how to decide whether the line of best fit is a good fit, or just a solution in search of some data. These principles are then extended to cover multiple cost drivers, and how we can use them to quantify 3-Point Estimates. With a deft sleight of hand, certain commonly occurring families of non-linear relationships can be transformed mathe-magically into linear formats, allowing us to exploit the powers of Regression Analysis to find the Best Fit Curves. The concludes with an exploration of the ups and downs of seasonal data (Time Series Analysis). Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.