Topic hub
Regression & Correlation
How variables relate to each other: correlation coefficients, lines of best fit, and linear regression for measuring an association's strength.
Correlation Coefficient (r): Formula & How to Interpret r Values
Learn what a correlation coefficient is, how to use the formula, and how to interpret r values — from the strongest to the weakest correlation.
Correlation Definition: Positive, Negative & Types Explained
Learn the correlation definition: a numerical measure of how strongly two variables are related, plus positive, negative, and zero correlation explained.
Correlation vs Causation: Why Correlation Doesn't Imply Causation
Learn correlation vs causation: why correlation doesn't imply causation, what spurious correlation means, and how scientists establish true cause-and-effect.
Linear Regression Assumptions: What They Are & How to Check
Learn the five linear regression assumptions — linearity, independence, homoscedasticity, normality, no multicollinearity — plus how to test each one.
Linear Regression: Equation, Formula & How It Works
Understand linear regression: the regression equation, slope and intercept formula, and how to fit a least-squares line with a fully worked numeric example.
Pearson Correlation Coefficient: Formula, Covariance & Spearman
Learn the Pearson correlation coefficient formula, how it relates to covariance, how to interpret r values, and when to use Spearman rank correlation instead.
Regression to the Mean: Definition & Real-World Examples
Learn what regression to the mean is, how Galton discovered it, the key formula, and real-world examples from sports, medicine, and education.