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Find the following products. $$\left[\begin{array}{ll}3 & 1 \\\4 & 0\end{array}\right]\left[\begin{array}{ll}0 & 3 \\\1 & 6\end{array}\right]$$

Short Answer

Expert verified
The product is \(\begin{bmatrix} 1 & 15 \ 0 & 12 \end{bmatrix}\).

Step by step solution

01

- Set up the matrices

Identify the two matrices to be multiplied. Matrix A is \(\begin{bmatrix} 3 & 1 \ 4 & 0 \end{bmatrix}\) and Matrix B is \(\begin{bmatrix} 0 & 3 \ 1 & 6 \end{bmatrix}\).
02

- Multiply the elements

To find the element in the first row and first column of the product matrix, calculate: \( (3 * 0) + (1 * 1) = 0 + 1 = 1 \). To find the element in the first row and second column, calculate: \( (3 * 3) + (1 * 6) = 9 + 6 = 15 \).
03

- Complete the multiplication for the second row

To find the element in the second row and first column, calculate: \( (4 * 0) + (0 * 1) = 0 + 0 = 0 \). To find the element in the second row and second column, calculate: \( (4 * 3) + (0 * 6) = 12 + 0 = 12 \).
04

- Combine the results

Combine all calculated elements into the resulting matrix: \( \begin{bmatrix} 1 & 15 \ 0 & 12 \end{bmatrix} \).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

linear algebra
Linear algebra is a branch of mathematics that deals with vectors, vector spaces, and linear transformations.

Matrices play a significant role in it. Matrices are used to represent and manipulate linear equations and transformations.
  • For example, they can represent systems of linear equations, transformations in geometry, and even complex networks.
Understanding matrix multiplication, element-wise multiplication, and other operations is essential when studying linear algebra.

Here are some key points to remember:
  • Matrix product involves rows and columns.
  • Element-wise multiplication is done element by element.
  • Linear transformations often use matrices as representation tools.
Building a solid foundation in linear algebra will help in many practical applications, from computer graphics to machine learning.

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