Nonparametric Statistics: Bayesian Approach

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Page 3 of 4

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Linear Regression: Beyond the Basics

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

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: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Applied Biostatistics: Bivariate Data Analysis

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.

Linear Regression: Estimation and Prediction

Covers the basics of linear regression, focusing on estimation and prediction.

Linear Regression

Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.