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Linear Regression Testing

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Linear Regression: Beyond the Basics Log in to Mediaspace to watch this video

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Heteroskedasticity: Ch. 4a Log in to Mediaspace to watch this video

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.

Least Squares Solutions Log in to Mediaspace to watch this video

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Linear Regression: Basics and Applications Log in to Mediaspace to watch this video

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Linear Regression: Foundations and Applications Log in to Mediaspace to watch this video

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Linear Regression: Least Squares Method I Log in to Mediaspace to watch this video

Covers linear regression and the least squares method to find the best-fit line for given data points.

Linear Regression: Pearson Correlation Log in to Mediaspace to watch this video

Covers the Pearson correlation, relationship direction, form, strength, and regression model assessment.

Orthogonal Bases and Projection Log in to Mediaspace to watch this video

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Instrumental Variables: Intuition and Estimation Log in to Mediaspace to watch this video

Covers instrumental variables, addressing endogeneity issues in regression analysis through estimation techniques and practical examples.

Modern Regression: Applications and Examples Log in to Mediaspace to watch this video

Explores the EM algorithm, extreme-value distribution fitting, and seasonal decomposition in regression models.

Regularization in Machine Learning Log in to Mediaspace to watch this video

Introduces regularization techniques to prevent overfitting in machine learning models.

Chimpanzee Data Analysis Log in to Mediaspace to watch this video

Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Stochastic Endowment Consumption Model Log in to Mediaspace to watch this video

Covers the stochastic endowment consumption model, log-linear transformation, impulse response solution, and Matlab codes.

Group Algebra: Maschke's Theorem Log in to Mediaspace to watch this video

Explores Wedderburn's theorem, group algebras, and Maschke's theorem in the context of finite dimensional simple algebras and their endomorphisms.

Equivariant Structural Representations Log in to Mediaspace to watch this video

Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.

Logistic Regression and Polynomial Regression Log in to Mediaspace to watch this video

Introduces logistic regression for binary classification and polynomial regression on polynomial variables.

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