SENATOROVAI/Normal-equations-scalar-form-solver-simple-linear-regression-course

The normal equations for simple linear regression are a system of two linear equations used to find the optimal intercept and slope that minimize the sum of squared residuals. They are derived from the ordinary least squares (OLS) method and can be expressed in scalar or matrix form.Solver

49
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Emerging

This tool helps researchers and data scientists quickly find the best-fit line for their data in simple linear regression. You provide a set of data points (x and y values), and it calculates the optimal intercept and slope for that line. This is ideal for anyone who needs to understand the direct relationship between two variables.

Use this if you need a clear, closed-form solution for simple linear regression coefficients for research or educational purposes.

Not ideal if you are working with multiple input variables, very large datasets, or require more robust numerical stability for complex problems.

data-analysis statistical-modeling research quantitative-analysis predictive-modeling
No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 15 / 25
Community 18 / 25

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Stars

18

Forks

14

Language

Jupyter Notebook

License

MIT

Last pushed

Mar 01, 2026

Commits (30d)

0

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