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Multi-linear regression
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Related lectures (31)
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Linear Regression: Foundations and Applications
Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.
Linear Regression
Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Regression: Linear Models
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Linear Regression: Basics and Geometric Interpretation
Explores Gaussian linear regression, design matrix, least squares estimation, and geometric interpretation in linear regression analysis.
Understanding Data Attributes
Covers the analysis of various data attributes and linear regression models.
Linear Regression: Estimation and Testing
Explores linear regression estimation, hypothesis testing, and practical applications in statistics.
Regression Analysis: Interpretation and Applications
Explores multiple regression, ANOVA, interpretation of coefficients, and regression estimation.
Applied Biostatistics: Bivariate Data and Regression Analysis
Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.
Linear Regression
Covers linear regression for estimating train speed using least squares and regularization.
Geometry and Least Squares
Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
