A matrix with the same number of rows as columns) that has full rank implies that you can invert the matrix, and vice-versa (i.e. If you can invert a square matrix, this implies that it has full rank). In other words, the set of columns of an invertible square matrix are linearly independent. Why is full rank important in linear models?