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$A 2\times 2$ transformation matrix maps the standard basis vectors to its own columns. Specifically, the first column of the matrix is the image of the vector $[1, 0]^T$, and the second column is the image of the vector $[0, 1]^T$.

Conditions: The matrix is $2\times 2$.; Working in standard Cartesian coordinates.

Multiplying a 3x3 matrix by a vector $(x, y, z)$ scales each column of the matrix (which represents a transformed basis vector) by the corresponding input coordinate and sums the results. This works because linear transformations preserve addition and scalar multiplication.

Conditions: Matrix is 3x3 representing a linear transformation; Input vector has coordinates $(x, y, z)$; Transformation is linear

To compute the matrix for the composition $A \circ B$, apply the transformation $A$ to each column of the matrix $B$. The resulting vectors form the columns of the matrix $A \circ B$.

Conditions: A and B are both 3x3 matrices.; The operation is performed over the real numbers.; These are real linear maps with compatible input and output spaces, represented using standard column-vector coordinates.

Matrix-vector multiplication in 3D works by scaling the columns of the matrix by the corresponding coordinates of the input vector and summing the results. The coordinates $(x, y, z)$ act as scalar multipliers for the transformed basis vectors (the columns), leveraging the linearity property that preserves addition and scalar multiplication.

Conditions: The matrix is a 3x3 transformation matrix.; The input vector has coordinates $(x, y, z)$.; The transformation is linear.

Multiplying two 3x3 matrices represents applying their respective spatial transformations sequentially. The rightmost matrix acts first on the initial space, and the leftmost matrix acts on the already transformed result.

Conditions: Both matrices are 3x3 transformation matrices.; The operation is standard matrix multiplication.

To construct the 3x3 matrix, observe where the three standard basis vectors ($\hat{i}$, $\hat{j}$, and $\hat{k}$) land after the transformation. Record the final coordinates of these transformed vectors as column vectors.

Conditions: Working in three-dimensional Cartesian space.; The transformation is linear.

Matrix-vector multiplication is reframed as scaling the transformed basis vectors (which are the columns of the matrix) by the input vector's components and summing them. This constructs the final position within the skewed coordinate system defined by the matrix, rather than just following a tedious arithmetic recipe.

Conditions: Input and output coordinates use the fixed standard basis.; The matrix represents an active linear map moving vectors.

We create a $2 \times 2$ matrix where the first column records the coordinates of where $\hat{i}$ lands, and the second column records the coordinates of where $\hat{j}$ lands. This matrix fully represents the linear transformation.

Conditions: Working in 2D space.; Knowing the images of $\hat{i}$ and $\hat{j}$.

The j-th column of the composed matrix $A \circ B$ is the image under A of the j-th column of B. To find a missing column, identify the corresponding column in B, treat it as an input vector, and apply the transformation A to it.

Conditions: A is a linear transformation represented by a matrix.; Columns are read as vectors in the domain of A.

To find the area of the transformed region, multiply the original area by the absolute value of the determinant of the transformation matrix. The formula is: $\text{New Area} = \text{Old Area} \cdot |\det(A)|$.

Conditions: The transformation is linear and represented by $a 2\times 2$ matrix.; The original area is known.; Use ordinary Euclidean area in standard orthonormal coordinates.

A 3D linear transformation is fully determined by tracking where the standard basis vectors ($\hat{i}$, $\hat{j}$, and $\hat{k}$) land. The coordinates of these three transformed vectors are recorded as column vectors to form a 3x3 matrix.

Conditions: Working in three-dimensional Cartesian space; Using the standard basis vectors aligned with x, y, and z axes; The transformation is linear (preserves grid lines parallel/evenly spaced and fixes origin)