Lectures related to Coefficient of determination

Assessing Significance and Fit

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Multicollinearity and Model Fit Analysis

Explores the dangers of 'big' models, multicollinearity issues, and model fit analysis in statistics for data science.

Multilinear Regression: Least Square Fit

Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.

Regression Analysis: Model Selection and Diagnostic Tools

Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.

Model Selection Methods in Biostatistics

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Linear Regression: Simple

Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Equivalent Degrees of Freedom in Spline Smoothing

Explores equivalent degrees of freedom in spline smoothing and the bias-variance tradeoff in fitting data.

Regression Analysis: Interpretation and Applications

Explores multiple regression, ANOVA, interpretation of coefficients, and regression estimation.

Functional Linear Regression: Sparse Estimation and Adaptive Methods

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Linear Regression: Estimation and Inference

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

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Multilinear Regression: Standardized Variables and Effects

Explores standardizing variables and effects in multilinear regression analysis.

Bias-Variance Tradeoff in Ridge Estimation

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Statistical Inference: Linear Models Log in to Mediaspace to watch this video

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Least-Squares Approximation Log in to Mediaspace to watch this video

Covers polynomial interpolation and least-squares approximations to minimize errors.