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Linear Regression: Basics and Estimation
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Related lectures (51)
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.
Linear Regression: Multicollinearity, Outliers, Model Specification
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Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.
Untitled
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Machine Learning Fundamentals: Regularization and Cross-validation
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Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Multi-linear regression
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Covers the concept of multi-linear regression and the least squares method for model fitting.
Modern Regression: Statistical Models and Data Analysis
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Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.
Introduction to OLS
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Covers the basics of Ordinary Least Squares (OLS) in econometrics, including variable relationships, coefficient determination, and model interpretation.
Spin Glasses and Bayesian Estimation
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Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.
Gaussian Mixture Regression: Examples and Analysis
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Explores Gaussian Mixture Regression in 2D datasets, analyzing priors, components, and regression outcomes.
Elements of Statistics: Probability, Distributions, and Estimation
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Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Statistics: Exploratory Data Analysis
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Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
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