Higher Education · Data Science & Statistics
Regression Analysis
What is it
Regression analysis, in a data science context, builds predictive models (from simple linear regression to logistic regression for classification) directly from real data — covering feature selection, splitting data into training and test sets, and evaluating a model with metrics like R² and RMSE rather than proving formal statistical assumptions.
Why it matters
This is the practical, applied side of regression that data science and machine learning roles actually use day to day — the emphasis shifts from proving a model satisfies textbook statistical assumptions to building a model that predicts well on genuinely unseen data, which is a distinct and equally important skill.
Exam tip
Always evaluate a regression model on a held-out test set, never on the same data it was trained on — a model that fits its training data almost perfectly but performs poorly on new data has overfit, and this is the single most common mistake in applied regression work.
Related topics
Want help mastering Regression Analysis?
Tell us about the student's goals and confidence — we'll design a personalised plan.