Step 1: Key observation:
When $M$ multiplies the standard basis vectors, we get:
Therefore, ${Me_1, Me_2, Me_3}$ is the set of columns of $M$.
Step 2: Analysis:
The rank of $M$ equals the maximum number of linearly independent columns of $M$.
Step 3: Evaluating each option:
(A) If $\text{rank}(M) = 1$, then ${Me_1, Me_2}$ is linearly independent
If rank is 1, there is only 1 linearly independent column. Any set of 2 columns must be linearly dependent. FALSE
(B) If $\text{rank}(M) = 2$, then ${Me_1, Me_2}$ is linearly independent
If rank is 2, there are exactly 2 linearly independent columns. However, this doesn't guarantee that the first two columns are the independent ones. The independent columns could be columns 1 and 3, or columns 2 and 3. NOT NECESSARILY TRUE
(C) If $\text{rank}(M) = 2$, then ${Me_1, Me_3}$ is linearly independent
By the same reasoning as (B), this is not guaranteed. NOT NECESSARILY TRUE
(D) If $\text{rank}(M) = 3$, then ${Me_1, Me_3}$ is linearly independent
If rank is 3, all three columns are linearly independent. This means any subset of the three columns is also linearly independent. In particular, ${Me_1, Me_3}$ (columns 1 and 3) must be linearly independent. TRUE
Answer: (D) If $\text{rank}(M) = 3$, then ${Me_1, Me_3}$ is a linearly independent set