Guides ยท Math
Linear Regression Basics
Fit a line to data
Linear regression estimates relationships between variables by fitting a line that minimizes squared errors, using coefficients, intercept, and evaluating fit.
- slope
- intercept
- least squares
- residuals
- r-squared
Model
Predict y from x with y = b0 + b1x; extend to multiple x.
Fit
Use least squares to minimize residuals; check assumptions.
Evaluate
Check residuals, R-squared, and avoid extrapolation.