MATH3714, Section 1: Simple Linear Regression

(17:15)

MATH3714, Section 2.5: Normal Equations in R

(10:50)

MATH3714, Section 11.1: Improving the Model Fit

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MATH3714, Section 12.2: Standardising the Variables

(13:3)

MATH3714, Section 15.1: Factors

(12:27)

MATH3714, Section 11.3: Power Transforms

(11:54)

MATH3714, Section 2.2: Least Squares Estimates

(23:44)

MATH3714, Section 12.1: Ridge Regression

(21:24)

MATH3714, Section 10.1: The Effect of Multicollinearity

(8:55)

Generalized Linear Model Iteratively Reweighted Least Square Method using R

(1:14)

CS480/680 Lecture 7: Mixture of Gaussians

(1:59)

Why π is in the normal distribution (beyond integral tricks)

(24:46)

011. M-Estimation: A Practicing Statistician's Best Friend (Conceptual, Theory, and Application)

(31:35)

CS480/680 Lecture 5: Statistical Linear Regression

(1:13:25)

CE 413 Lecture 06: Transformed Sections (2018.01.24)

(48:45)

Image understanding: supervised learning: regression: total least-squares: line fitting, ax + by = 0

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LINEAR MODELS : LEC-11 | QUADRATIC FORMS | #COCHRANS THEOREMS | #MULTIPLE REGRESSION MODEL

(1:26:19)

LINEAR MODELS | LEC-12 | Mastering Cochran's Theorems \u0026 Quadratic Forms in Linear Models

(1:11:7)

Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares

(15:49)

MATH3714, Section 9.1: Influence of Observations

(18:56)

MATH3714, Section 4.1: The Regression Coefficients

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MATH3714, Section 8.2: The R squared Value

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MATH3714, Section 15.2: Interactions

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MATH3714, Section 14.1: Exhaustive Search

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MATH3714, Section 10.2: Detecting Multicollinearity

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MATH3714, Section 8.1: Diagnostic Plots

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MATH3714, Section 18.2: Iterative Methods

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MATH3714, Section 16.1: Interactions Example

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