Linear Algebra › Section F · Orthogonality and least squares › Topic F.6
Least squares for linear models
The regression line of statistics is a least-squares problem in disguise. Writing it with a design matrix shows exactly where its formulas come from, and the same set-up fits curves, several predictors and any model whose unknown coefficients appear linearly.
In this topic
- Regression as a matrix problem
- Design matrix, parameters and observations
- The normal equations for a line
- Worked example: fitting a line
- Why the residuals add to zero
- The mean-deviation form
- The general linear model
- Worked example: fitting a parabola
- Several explanatory variables
- Common mistakes to avoid
- Key takeaways
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