FP1.6 - Transformations using matrices

Linear transformations of column The transformation represented by AB is the transformation vectors in two dimensions and their represented by B followed by the transformation matrix representation. represented by A.

Use fp1.6.1 - linear transformations of column to connect the rule to the data and decision in the question.

This matters because fp1.6.1 - linear transformations of column determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply fp1.6.1 - linear transformations of column to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: FP1.6.1 - Linear transformations of column is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

Applications of 2 × 2 matrices to Identification and use of the matrix representation of single represent geometrical transformations from: reflection in coordinate axes and transformations. lines y = ±x, rotation through any angle about (0, 0), stretches parallel to the x-axis and y-axis, and enlargement about centre (0, 0), with scale factor k, (k ≠ 0), where k ∈.

Use fp1.6.2 - applications of 2 × 2 matrices to connect the rule to the data and decision in the question.

This matters because fp1.6.2 - applications of 2 × 2 matrices determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply fp1.6.2 - applications of 2 × 2 matrices to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: FP1.6.2 - Applications of 2 × 2 matrices is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

Combinations of transformations.; Identification and use of the matrix representation of ℝ combined transformations.

Use fp1.6.3 - combinations of transformations to connect the rule to the data and decision in the question.

This matters because fp1.6.3 - combinations of transformations determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply fp1.6.3 - combinations of transformations to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: FP1.6.3 - Combinations of transformations is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

The inverse (when it exists) of a Idea of the determinant as an area scale factor in given transformation or transformations. combination of transformations.

Use fp1.6.4 - inverse transformations and determinant scale factor to connect the rule to the data and decision in the question.

This matters because fp1.6.4 - inverse transformations and determinant scale factor determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply fp1.6.4 - inverse transformations and determinant scale factor to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: FP1.6.4 - Inverse transformations and determinant scale factor is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.