How does matrix-vector multiplication in 3D correspond to scaling and adding transformed basis vectors?

In this context, $A \circ B$ denotes the transformation obtained by applying B first and then applying A to the result. The notation follows the convention where the rightmost transformation acts first on the input vector.

Conditions: A and B are treated as transformations on the same space, here three-dimensional space.; The output of B must be an input acceptable to A.

The missing middle column is $\begin{bmatrix} 0 \\ -16 \\ 9 \end{bmatrix}$. This is obtained by applying the transformation A to the middle column of B, which is $\begin{bmatrix} 0 \\ 2 \\ 3 \end{bmatrix}$.

Conditions: A and B are the specific 3x3 matrices shown in the video.; The method of column-wise composition is used.

During a 90-degree rotation around the y-axis, $\hat{i}$ moves to the negative z-axis at $(0, 0, -1)$, $\hat{j}$ remains stationary on the y-axis at $(0, 1, 0)$, and $\hat{k}$ swings to the positive x-axis at $(1, 0, 0)$.

Conditions: Rotation angle is exactly 90 degrees; Axis of rotation is the y-axis; Right-handed coordinate system convention assumed for sign determination

$A 2\times 2$ 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.

The determinant scales the area of any measurable planar figure, not just squares. The absolute value of the determinant acts as a uniform area scaling factor for all regions under the linear transformation.

Conditions: The transformation is linear.; The figure is measurable and has finite area.; Use ordinary Euclidean area in standard orthonormal coordinates.