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2013•Unpublished venueRequires access

Models with Interpolation and Curve Fitting

Jose M. Garrido

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Abstract

If the mathematical model in the form a polynomial function is needed to represent the raw data, then curve fitting, also known as regression, techniques are used. In the general case, the coefficients of the polynomial can be computed (estimated) for a polynomial function of any degree. This chapter discusses two general numerical techniques that help estimate data values: interpolation and curve fitting. Solutions implemented with the GSL and the C programming language are presented and discussed.

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What this paper is about

If the mathematical model in the form a polynomial function is needed to represent the raw data, then curve fitting, also known as regression, techniques are used. In the general case, the coefficients of the polynomial can be computed (estimated) for a polynomial function of any degree. This chapter discusses two general numerical techniques that help estimate data values: interpolation and curve fitting. Solutions implemented with the GSL and the C programming language are presented and discussed.

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Available abstract

If the mathematical model in the form a polynomial function is needed to represent the raw data, then curve fitting, also known as regression, techniques are used. In the general case, the coefficients of the polynomial can be computed (estimated) for a polynomial function of any degree. This chapter discusses two general numerical techniques that help estimate data values: interpolation and curve fitting. Solutions implemented with the GSL and the C programming language are presented and discussed.

Key concepts: Interpolation (computer graphics), Curve fitting, Mathematics, Statistics, Computer science, Computer graphics (images), Animation

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