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You are given a set of data to analyze. The commands to load the data are in the first line of the script. Separate the first column and second columns into the variable X and Y respectively. Perform a polynomial least square regression of an order 3: 1. Calculate the coefficients to fit the data and save them in a vector C. 2. Use these coefficient to calcutate fitted y values YFit. 3. Calculate the rsquare value and save it in the variable Rsq. Plot X and Y values. Overlay YFit values in the same plot. Add axis labels and a legend. I

You are given a set of data to analyze. The commands to load the data are in the first line of the script. Separate the first column and second columns into the variable X and Y respectively. Perform a polynomial least square regression of an order 3: 1. Calculate the coefficients to fit the data and save them in a vector C. 2. Use these coefficient to calcutate fitted y values YFit. 3. Calculate the rsquare value and save it in the variable Rsq. Plot X and Y values. Overlay YFit values in the same plot. Add axis labels and a legend. I

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter9: Systems Of Equations And Inequalities
Section9.6: The Algebra Of Matrices
Problem 43E
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You are given a set of data to analyze. The commands to load the data are in the first line of the script. Separate the first column and second columns into the variable X and Y respectively. Perform a polynomial least square regression of an order 3: 1. Calculate the coefficients to fit the data and save them in a vector C. 2. Use these coefficient to calcutate fitted y values YFit. 3. Calculate the rsquare value and save it in the variable Rsq. Plot X and Y values. Overlay YFit values in the same plot. Add axis labels and a legend. I
Transcribed Image Text:You are given a set of data to analyze. The commands to load the data are in the first line of the script. Separate the first column and second columns into the variable X and Y respectively. Perform a polynomial least square regression of an order 3: 1. Calculate the coefficients to fit the data and save them in a vector C. 2. Use these coefficient to calcutate fitted y values YFit. 3. Calculate the rsquare value and save it in the variable Rsq. Plot X and Y values. Overlay YFit values in the same plot. Add axis labels and a legend. I
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