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The 30 pages that link to Linear regression, each with the reason it gives.

• Normal distributionRelated: Normal error assumptions support familiar inference for regression coefficients.

• Linear independenceRelated: Dependent predictor columns make coefficients non-unique, a problem called multicollinearity.

• SlopeRelated: Its fitted line’s slope estimates how one variable changes with another.

• Gradient descentRelated: Its squared-error parameters can be fitted by minimizing an objective with gradient descent.

• Linear combinationRelated: Its predictions combine predictor contributions using fitted coefficients.

• StandardizationRelated: Standardized coefficients express predictor effects in standard-deviation units.

• Calibration curveRelated: It estimates the line used to convert response into concentration.

• System of linear equationsRelated: Least-squares estimates satisfy the normal equations, a linear system.

• Invertible matrixRelated: The normal-equation solution uses an inverse when the design matrix has full column rank.

• Degrees of freedomRelated: Residual degrees of freedom depend on observations and independent fitted coefficients.

• Gaussian distributionRelated: Gaussian error assumptions yield familiar estimation and uncertainty formulas.

• Scatter plotRelated: A fitted line summarizes a roughly linear pattern among plotted points.

• Student's t-distributionRelated: Under standard assumptions, regression coefficient estimates are tested using t-distributed statistics.

• Linear functionRelated: It estimates a linear trend when real measurements do not lie exactly on a line.

• Arbitrage pricing theoryRelated: Empirical APT applications estimate factor exposures using regressions of returns.

• Gauss–Markov theoremRelated: The theorem emerged as a foundational result about regression coefficient estimation.

• Y-interceptRelated: The fitted line’s y-intercept estimates the response when the predictor is zero.

• Cochran's theoremRelated: Regression residual and fitted sums of squares follow Cochran-style decompositions under Gaussian errors.