MATLAB Interpolation: Implementation and Experimental Data Processing

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Resource Overview

This resource covers MATLAB's interpolation capabilities and experimental data processing applications, including implementation examples with key functions like interp1, spline, and polyfit for various interpolation techniques.

Detailed Documentation

This article provides a comprehensive overview of MATLAB's applications in interpolation and experimental data processing. Interpolation is a data processing method that estimates unknown points by creating new data points between known values. MATLAB offers numerous built-in functions to easily perform various interpolation types, including linear interpolation using interp1, spline interpolation with the spline function, and cubic interpolation implemented via pchip or makima algorithms. For experimental data processing, MATLAB provides integrated tools for data import through functions like readtable and xlsread, visualization using plot and scatter commands, data fitting with polyfit and fit functions, and statistical analysis employing mean, std, and anova1 utilities. By combining these capabilities, MATLAB serves as a powerful tool that enables scientists and engineers to better understand and process experimental data through practical code implementations.

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