Media Summary: Special cases of the F-test: ANOVA, One-way classification, etc. Gauss-Markov theorem Generalized Least-Squares (GLS) The ensemble view --- abstract meaning of confidence intervals (CI), p-values, hypothesis testing (HT), etc. Concrete construction ...

Stats 100c Linear Models Spring 2026 Lecture 12 - Detailed Analysis & Overview

Special cases of the F-test: ANOVA, One-way classification, etc. Gauss-Markov theorem Generalized Least-Squares (GLS) The ensemble view --- abstract meaning of confidence intervals (CI), p-values, hypothesis testing (HT), etc. Concrete construction ... Parametric confidence intervals and prediction intervals Teaser for conformal prediction. Split conformal prediction in depth Proof that it gives correct (marginal) coverage Difference between marginal and conditional ... Gram matrix, rank(X^T X) = rank(X) beta hat is independent of e Distribution of the quadratic forms from projections.

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STATS 100C: Linear Models --- Spring 2026 - Lecture 12
STATS 100C: Linear Models --- Spring 2026 - Lecture 13: Gauss-Markov and GLS
STATS 100C: Linear Models -- Spring 2026 -- Lecture 11
STATS 100C: Linear Models -- Spring 2026: Lecture 15 / Leverage, influence, and diagnostics
STATS 100C: Linear Models -- Spring 2026:  Lecture 14 / Multicollinearity, PCA, VIF and Shrinkage
STATS 100C: Linear Models -- Spring 2026 -- Lecture 8 (Afternoon)
STATS 100C: Linear Models --- Spring 2026 - Lecture 9
STATS 100C: Linear Models --- Spring 2026 - Lecture 10: Split conformal prediction
STATS 100C: Linear Models -- Lecture 7 (Afternoon)
STATS 100C: Linear Models - Lecture 1 (Morning)
STATS 100C: Linear Models - Lecture 5 (Afternoon)
STATS 100C: Linear Models -- Lecture 6 (Afternoon)
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STATS 100C: Linear Models --- Spring 2026 - Lecture 12

STATS 100C: Linear Models --- Spring 2026 - Lecture 12

Special cases of the F-test: ANOVA, One-way classification, etc.

STATS 100C: Linear Models --- Spring 2026 - Lecture 13: Gauss-Markov and GLS

STATS 100C: Linear Models --- Spring 2026 - Lecture 13: Gauss-Markov and GLS

Gauss-Markov theorem Generalized Least-Squares (GLS)

STATS 100C: Linear Models -- Spring 2026 -- Lecture 11

STATS 100C: Linear Models -- Spring 2026 -- Lecture 11

General

STATS 100C: Linear Models -- Spring 2026: Lecture 15 / Leverage, influence, and diagnostics

STATS 100C: Linear Models -- Spring 2026: Lecture 15 / Leverage, influence, and diagnostics

Okay so if I if I remove um see the the

STATS 100C: Linear Models -- Spring 2026:  Lecture 14 / Multicollinearity, PCA, VIF and Shrinkage

STATS 100C: Linear Models -- Spring 2026: Lecture 14 / Multicollinearity, PCA, VIF and Shrinkage

... so if it's best in this

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STATS 100C: Linear Models -- Spring 2026 -- Lecture 8 (Afternoon)

STATS 100C: Linear Models -- Spring 2026 -- Lecture 8 (Afternoon)

The ensemble view --- abstract meaning of confidence intervals (CI), p-values, hypothesis testing (HT), etc. Concrete construction ...

STATS 100C: Linear Models --- Spring 2026 - Lecture 9

STATS 100C: Linear Models --- Spring 2026 - Lecture 9

Parametric confidence intervals and prediction intervals Teaser for conformal prediction.

STATS 100C: Linear Models --- Spring 2026 - Lecture 10: Split conformal prediction

STATS 100C: Linear Models --- Spring 2026 - Lecture 10: Split conformal prediction

Split conformal prediction in depth Proof that it gives correct (marginal) coverage Difference between marginal and conditional ...

STATS 100C: Linear Models -- Lecture 7 (Afternoon)

STATS 100C: Linear Models -- Lecture 7 (Afternoon)

Gram matrix, rank(X^T X) = rank(X) beta hat is independent of e Distribution of the quadratic forms from projections.

STATS 100C: Linear Models - Lecture 1 (Morning)

STATS 100C: Linear Models - Lecture 1 (Morning)

Review of

STATS 100C: Linear Models - Lecture 5 (Afternoon)

STATS 100C: Linear Models - Lecture 5 (Afternoon)

Linear Model

STATS 100C: Linear Models -- Lecture 6 (Afternoon)

STATS 100C: Linear Models -- Lecture 6 (Afternoon)

Projection matrices, statistical

Statistical Rethinking 2026 Lecture B03 - Adventures in Covariance

Statistical Rethinking 2026 Lecture B03 - Adventures in Covariance

For full course description see https://github.com/rmcelreath/stat_rethinking_2026.