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Related lectures (31)

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Linear Regression: Basics and Interpretation

Introduces the basics of linear regression, interpreting coefficients, assumptions, transformations, and 'Difference in Differences' for causal analysis.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Linear Regression Essentials

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

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