Numerical method (curve fitting)
***TOPICS ARE****
Linear Regression
Multiple Linear Regression
Polynomial Regression
Example of Newton’s Interpolation Polynomial And example
Example of Newton’s Interpolation Polynomial And example
This presentation is a part of Computer Oriented Numerical Method . Newton-Cotes formulas are an extremely useful and straightforward family of numerical integration techniques.
This presentation is a part of Computer Oriented Numerical Method . Newton-Cotes formulas are an extremely useful and straightforward family of numerical integration techniques.
A short presentation on the topic Numerical Integration for Civil Engineering students.
This presentation consist of small introduction about Simpson's Rule, Trapezoidal Rule, Gaussian Quadrature and some basic Civil Engineering problems based of above methods of Numerical Integration.
Here we focuses on Fixed-Point Iterative Technique for solving nonlinear Equations in Numerical Analysis. It is one of the opened-iterative techniques for finding roots called Fixed-Point of Non-linear Equations.
There are three main sources of errors in numerical computation: rounding, data uncertainty, and truncation. Rounding errors, also called arithmetic errors, are an unavoidable consequence of working in finite precision arithmetic.
These slides contain information about Euler method,Improved Euler and Runge-kutta's method.How these methods are helpful and applied to our questions are detailed discussed in the slides.
A short presentation on the topic Numerical Integration for Civil Engineering students.
This presentation consist of small introduction about Simpson's Rule, Trapezoidal Rule, Gaussian Quadrature and some basic Civil Engineering problems based of above methods of Numerical Integration.
Here we focuses on Fixed-Point Iterative Technique for solving nonlinear Equations in Numerical Analysis. It is one of the opened-iterative techniques for finding roots called Fixed-Point of Non-linear Equations.
There are three main sources of errors in numerical computation: rounding, data uncertainty, and truncation. Rounding errors, also called arithmetic errors, are an unavoidable consequence of working in finite precision arithmetic.
These slides contain information about Euler method,Improved Euler and Runge-kutta's method.How these methods are helpful and applied to our questions are detailed discussed in the slides.
Applied Numerical Methods Curve Fitting: Least Squares Regression, InterpolationBrian Erandio
Correction with the misspelled langrange.
and credits to the owners of the pictures (Fantasmagoria01, eugene-kukulka, vooga, and etc.) . I do not own all of the pictures used as background sorry to those who aren't tagged.
The presentation contains topics from Applied Numerical Methods with MATHLAB for Engineers and Scientist 6th and International Edition.
On elements of deterministic chaos and cross links in non- linear dynamical s...iosrjce
In this paper we examine the existing definitions of deterministic chaos and the characterisation of
its various ingredients. We then make use of some classical examples to provide cross links between the
different chaotic behaviour of some simple but interesting maps which are then explained in a precise manner.
A polynomial interpolation algorithm is developed using the Newton's divided-difference interpolating polynomials. The definition of monotony of a function is then used to define the least degree of the polynomial to make efficient and consistent the interpolation in the discrete given function. The relation between the order of monotony of a particular function and the degree of the interpolating polynomial is justified, analyzing the relation between the derivatives of such function and the truncation error expression. In this algorithm there is not matter about the number and the arrangement of the data points, neither if the points are regularly spaced or not. The algorithm thus defined can be used to make interpolations in functions of one and several dependent variables. The algoritm automatically select the data points nearest to the point where an interpolation is desired, following the criterion of symmetry. Indirectly, the algorithm also select the number of data points, which is a unity higher than the order of the used polynomial, following the criterion of monotony. Finally, the complete algoritm is presented and subroutines in fortran code is exposed as an addendum. Notice that there is not the degree of the interpolating polynomial within the arguments of such subroutines.
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Fixed Point Theorm In Probabilistic Analysisiosrjce
Probabilistic operator theory is the branch of probabilistic analysis which is concerned with the study of
operator-valued random variables and their properties. The development of a theory of random operators is of
interest in its own right as a probabilistic generalization of (deterministic) operator theory and just as operator
theory is of fundamental importance in the study of operator equations, the development of probabilistic operator
theory is required for the study of various classes of random equations
Basics of probability in statistical simulation and stochastic programmingSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 2.
More info at http://summerschool.ssa.org.ua
project for web based game
project for web based game
project for web based game
project for web based game
project for web based game
project for web based game
project for web based game
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
1. Name:Sujit Kumar Saha
Lecturer at Varendra University
Rajshahi
Name: Istiaque Ahmed Shuvo
Id: 141311057
5th batch, 7th Semester
Sec-B
Dept. Of Cse
Varendra University, Rajshahi
Submitted By: Submitted To
11-Apr-16 1
2.
3. TOPICS ARE
Linear Regression
Multiple Linear Regression
Polynomial Regression
Example of Newton’s Interpolation
Polynomial And example
11-Apr-16 3
4. Fitting a straight line to a set of paired
observations: (x1, y1), (x2, y2),…,(xn, yn).
y = a0+ a1 x + e
a1 - slope
a0 - intercept
e - error, or residual, between the model and
the observations
Linear Regression
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5.
6.
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xaay
xxaay
a
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xaay
a
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iiii
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xaxaxy
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naa
2 equations with 2
unknowns, can be solved
simultaneously
Linear Regression:
Determination of ao and a1
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7.
221
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yxyxn
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xaya 10
Linear Regression:
Determination of ao and a1
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8. • Another useful extension of linear
regression is the case where y is a
linear function of two or more
independent variables:
• Again, the best fit is obtained by
minimizing the sum of the squares
of the estimate residuals:
Multiple Linear Regression
9. • The least-squares procedure
from Chapter 13 can be
readily extended to fit data
to a higher-order
polynomial. Again, the idea
is to minimize the sum of the
squares of the estimate
residuals.
• The figure shows the same
data fit with:
a) A first order polynomial
b) A second order polynomial
Polynomial Regression
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10. Many times, data is given only at discrete points such as (x0, y0), (x1, y1), ......, (xn−1, yn−1),
(xn, yn). So, how then does one find the value of y at any other value of x ? Well, a
continuous function f (x) may be used to represent the n +1 data values with f (x)
passing through the n +1 points (Figure 1). Then one can find the value of y at any
other value of x . This is called interpolation.
Of course, if x falls outside the range of x for which the data is given, it is no
longer interpolation but instead is called extrapolation.
So what kind of function f (x) should one choose? A polynomial is a common
choice for an interpolating function because polynomials are easy to
(A) evaluate,
(B) differentiate, and
(C) integrate,
relative to other choices such as a trigonometric and exponential series.
Polynomial interpolation involves finding a polynomial of order n that passes
through the n +1 points. One of the methods of interpolation is called Newton’s divided
difference polynomial method. Other methods include the direct method and the
Lagrangian interpolation method. We will discuss Newton’s divided difference
polynomial method in this
What is interpolation?
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