This is a collection of well curated online resources (Web, and ChatGPT Discussions) to help you learn Statistics, Data Science, Machine Learning, and their corresponding fundamentals. Use the community to discuss and clear your doubts. Enjoy learning.
Linear and Logistic Regression
Syllabus: Simple Linear Regression, Multiple Linear Regression. Simple Logistic Regression, Multiple Logistic Regression, and Multinomial Logistic Regression.
- Simple Linear Regression (SLR) from Wikipedia (Summary of SLR)
- Almost everything about Simple Linear Regression (SLR) by University of Colorado Boulder (Intuitive to Probability Explanation)
- Applied Regression Analysis Online Notes by Penn State (Stat 462) (Good for SLR, MLR, and a Smooth Introduction to Logistic Regression, Poisson Regression, Generalized Linear Models (GLM), Non Linear Regression)
- Multiple Linear Regression (MLR) by University of Colorado Boulder (A Good Overview, A Good Transition from SLR version)
- MLR Diagnostics and Inference by Cosma Shalizi, Carnegie Mellon University (A Nice Review)
- SLR + MLR Overview + Statistical Inference by John Fox, McMaster University (Good for Moderately Quantitative Person)
- Linear Regression (ISLR Section)
- Simple Logistic Regression
- Multiple Logistic Regression
Your goal is to learn from multiple resources and make your own notes for understanding, and apply them to datasets. Note: Make sure you use your own set of notations. Different resources have different notations. So, please be careful. Use the community to learn together.
Learn. Code. Apply.
Statistics. Machine Learning.