If there are no linear dependencies among the columns of a matrix, the Full-Rank Matrices matrix is said to be of full rank, or nonsingular. If a matrix is not of full rank it is said to be singular. The number of linearly independent rows of a matrix will always equal the number of linearly independent columns. The linear dependency among the rows of A is shown by 9(row1) + 7(row2) = 6(row3). The critical matrices in regression will almost always have fewer columns than rows and, therefore, rank is more easily visualized by inspection of the columns.