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Exam Preparation: Tips and Guidelines

Related lectures (32)

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

Generalised Linear Models: Regression with Exponential Family Responses

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Logistic Regression: Cost Functions & Optimization

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Modern Regression: Inference and Models

Covers iterative weighted least squares, model checking, and generalized linear models in regression analysis.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Regression Methods: Model Building and Diagnostics

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Data-Driven Modeling: Regression

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

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Inference: Poisson Regression

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.