This repository contains assignments from the Advanced Statistical Analysis and Model-Based Learning course, part of the Master’s in Machine Learning and Data Science. The coursework explores key statistical concepts and model-based learning techniques, providing hands-on experience in statistical inference, regression models, and hypothesis testing.
The assignments cover various topics including:
- The Linear Model
- Linear Least Squares and Weighted Least Squares
- Statistical Inference and Exploratory Data Analysis (EDA)
- Sinusoidal Regression
- Probability review
- Normal, Chi-Squared, t, and F distributions
- Distributional Properties of the Linear Model
- Solving LS using SVD
- Statistical Estimation
- Hypothesis Testing in one and two samples
- ANOVA
- Prediction in Simple Regression
- Bonferroni's Test
- ANOVA and multiple Comparisons
- Multiple Regression
- Variable Selection
- Selecting Order of Regression Using Cross-validation
- Violation of Assumptions
- A/B Testing