USE MATLAB: Implement the following GUI to compare the results of Matlab\'s Inte
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Question
USE MATLAB:
Implement the following GUI to compare the results of Matlab's Interpolation and Regression Capabilities .
The GUI components and their functionality is described below
Input Data 1: Name of independent data vector
Input Data 2: Name of dependent data vector
Length: The number of interpolation and regression display points
Interpolation Method: Popup Menu to select between linear, spline, and cubic
Regression Order:* Slider specifying the regression order. Min = 1, Max = length of independent data vector.
Display Results: Pushbutton that does the following:
a) Plot the input independent/dependent data pair with red '*'s
b) Create 'xVector'. A vector with 'Length' linearly spaced between the minimum and maximum value of the input independent data vector
c) Otain the interpolation method from the Interpolation Method popup Menu Use the presribed method to create 'intVector. Plot intVector with '-'s.
d) Obtain the regression order from the regression slider. Perfrom a regression analysis of that order on the input data pair to obtain 'regVector'. Plot the regVector polynomial using xVector as the input. Use blue '.'s. for the plot%
Use xVector for both the interpolation data points and the independent data vector required to evaluate the polynomial determined by the regression evaluation.
Display all plots simultaneously.
Save Results: Pushbutton that saves xVector, intVector, and regVector to Matlab's base workspace:
near Interpolation Method Display Results Save Results Final Exam System Sp17 Final Exam Analysis System Input Data 1 Edit Text Length Edit Text 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.3 0.4 0.2 Input Data 2 Edit Text Regression order 0.6 0.7 0.8 0.5 0.9Explanation / Answer
unctions 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.
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