Least Squares Method Part 2: residuals, setting up your model, examples
In this sequel, we dive deeper into the application of the least squares method for polynomial fitting. We focus on calculating the residual vector and the R-squared value to evaluate the fit of our models. Key examples include fitting quadratic polynomials to data sets and specific case studies like analyzing Raleigh's population growth and predicting emergency braking distances for vehicles.
We emphasize the importance of understanding model limitations and avoiding overfitting, highlighting the practicality of the least squares method in real-world scenarios. We focus on how to set up A to reflect having certain terms (omitting others).
Part 1 here: • Least Squares Method Part 1: backgrou...
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