Learn how Bayes’ Theorem works, when to apply it, and how it connects to independence and conditional probability. This post breaks down key concepts with clear examples and practical relevance in real-world applications like spam detection and medical testing.
Learn how to calculate the probability of combined events using union rules, contingency tables, and conditional logic. This post walks you through marginal, joint, and conditional probabilities with intuitive visuals, real-world examples, and quiz-based learning.
Ever flipped a coin and wondered why you got heads three times in a row? Welcome to the world of randomness and probability — where short-term surprises often give way to long-term patterns. In th...
Learn linear regression step by step — from drawing the best-fit line and calculating residuals to interpreting slope and R², all in a beginner-friendly, ML-oriented guide.
Learn how Pearson’s r measures the strength and direction of linear relationships between variables. Includes formula breakdown, Python code, and machine learning relevance.
Understand the difference between marginal and conditional proportions using examples and contingency tables. A must-know concept for analyzing categorical data and preparing ML datasets.
Learn how to analyze relationships between variables using correlation techniques like contingency tables and scatter plots, based on whether your data is categorical or quantitative.
Walk through a complete real-world statistics example using test scores: calculate mean, median, range, IQR, standard deviation, z-scores, and visualize it with dot and box plots.
Understand z-scores and standardization — how far a value is from the mean, and why it's a powerful tool in comparing values across distributions and machine learning tasks.
Learn how variance and standard deviation measure the spread of your data — with formulas, worked examples, and relevance to data science and machine learning.