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\nPython 🐍 Curve Fit with Step Test Data\n

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\nThe Scipy curve_fit function determines two unknown coefficients (dead-time and time constant) to minimize the difference between predicted and measured response values. Pandas imports the data and the dataframe header is diplayed with...\nThe Scipy curve_fit function determines two unknown coefficients (dead-time and time constant) to minimize the difference between predicted and measured response values. Pandas imports the data and the dataframe header is diplayed with x.head(). Conditional statements are used to create a step function and curve_fit from Scipy.optimize finds the optimal parameter values that minimize a sum of squared error. A Matplotlib plot shows the function with optimal parameter values. An R-squared value is 0.999, showing excellent agreement between the predictions and measurements.

Course Material

Github Repository (see HW04.ipynb)

Schedule

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Resource Details

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Media Length
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14:49
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Grade
10th - Higher Ed
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Subjects
STEM
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2021
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Language
English
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For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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values, length, data, zeros, time, coding
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Additional Tags

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matlab, data points, statement, predicted, ahead, function, python, greater, save, optimization, process control, calculate, coder, import, predicted values, equal, model, curve fit, engineer, red points, condition, optimized, starts coming, theta, good fit, programming, excel, read csv, print, line, blue line, programmers, conditional statement
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Classroom Considerations

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