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Related lectures (30)
Model Checking and Residuals
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Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Generalized Linear Models
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Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Jacamar Data Analysis
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Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
Linear Regression: Model Adjustment and Parameter Estimation
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Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.
Maximum Likelihood Theory & Applications
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Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.
Linear Models for Classification: Logistic Regression and SVM
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Covers linear models for classification, focusing on logistic regression and support vector machines.
General Linear Model: Model Selection
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Explores the General Linear Model, significance testing, model selection, and parameter inference.
IWLS Algorithm: Overview
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Covers the IWLS algorithm for obtaining MLEs in regression models and discusses the likelihood ratio statistic and deviance in model fitting.
Maximum Likelihood Estimation: Econometrics
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Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.
Generalized Linear Models: Theory and Applications
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Explores Generalized Linear Models theory, including logistic and Poisson regression, model evaluation, and coefficient tests.
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