R^n

\(\mathbb{R}^n\) represents an n-dimensional space where each point is described by n real numbers. For example, \(\mathbb{R}^2\) is the familiar 2D plane with coordinates like (x, y), and \(\mathbb{R}^3\) adds depth with (x, y, z). In general, \(\mathbb{R}^n\) extends this idea to any number of dimensions, serving as a mathematical framework for analyzing complex systems with multiple variables. Each point in \(\mathbb{R}^n\) can be thought of as a vector with n components, allowing the study of relationships, distances, and directions in multi-variable contexts.