Statistics

This post explores the foundations of probability, including joint, marginal, and conditional probabilities using real-world examples like the Titanic dataset. We break down Bayes' Theorem and explain the intuition behind conditional probability, making complex ideas easy to grasp.

Dive into the world of probability with Part 1 of this blog series, where we lay the foundation for understanding uncertainty in everyday events. From basic definitions to real-life examples, we break down core concepts like sample space, events, and types of probability in the simplest terms. Ideal for beginners and revision before exams!

Bias-variance decomposition explains model error. Bias (underfitting) means a model is too simple, failing to capture data patterns. Variance (overfitting) means a model is too complex, sensitive to training data, and generalizes poorly. The goal is to balance this trade-off to minimize total prediction error for optimal model performance. Reducing bias may increase variance, and vice-versa, requiring strategic adjustments like complexity changes or regularization.

Statistics is the science of collecting, analyzing, and interpreting data to make informed decisions. It helps us understand large populations by studying smaller samples and is widely used across fields like business, science, economics, and technology.