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NCERT Solutions For Class 12 Maths Chapter 3 Matrices

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How Can Class 12 Maths Chapter 3 Miscellaneous Exercise Solutions Help You Prepare For Exams

Class 12 Maths NCERT Solutions for Chapter 3 Matrices includes solutions to all Miscellaneous Exercise problems. Matrices Miscellaneous Exercise Class 12 Chapter 3 Solutions are based on the concepts presented in Maths Chapter 3. This activity is crucial for both the CBSE Board examinations and competitive tests. Students can download the revised Class 12 Maths NCERT Solutions from our page which is prepared in a way so that you can understand it easily.

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The Class 12 Maths Chapter 3 Miscellaneous Exercise Solutions are aligned with the updated CBSE guidelines for Class 12, ensuring students are well-prepared for exams. Access the Class 12 Maths Syllabus here.

How Can Class 12 Maths Chapter 3 Miscellaneous Exercise Solutions Help You Prepare For Exams

Miscellaneous Exercise

1. If \[A\] and \[B\] are symmetric matrices, prove that \[AB-BA\] is a skew symmetric matrix.

Ans: \[A\] and \[B\] are symmetric matrix, therefore, we have:

\[A'=A\] and \[B'=B\]                                                    …(1)

Here, \[\left( AB-BA \right)'=\left( AB \right)'-\left( BA \right)'\]

\[\Rightarrow \left( AB-BA \right)'=B'A'-A'B'\]

From (1),

\[\Rightarrow \left( AB-BA \right)'=BA-AB\]

\[\Rightarrow \left( AB-BA \right)'=-\left( AB-BA \right)\]                           …(2)

From equation (2),

\[AB-BA\] is a skew symmetric matrix.


2. Show that the matrix \[B'AB\] is symmetric or skew symmetric accordingly as \[A\] is symmetric or skew symmetric.

Ans: Let \[A\] be a symmetric matrix, then \[A'=A\]               …(1)

\[\left( B'AB \right)'=\left\{ B'\left( AB \right) \right\}'\]

\[\Rightarrow \left( B'AB \right)'=\left( AB \right)'B'\]

\[\Rightarrow \left( B'AB \right)'=B'\left( A'B \right)\]

From (1),

\[\Rightarrow \left( B'AB \right)'=B'\left( AB \right)\]

Thus, if \[A\] is a symmetric matrix, then \[B'AB\] is a symmetric matrix.

Let \[A\] be a skew symmetric matrix, then \[A'=-A\]               …(2)

\[\left( B'AB \right)'=\left\{ B'\left( AB \right) \right\}'\]

\[\Rightarrow \left( B'AB \right)'=\left( AB \right)'B'\]

\[\Rightarrow \left( B'AB \right)'=B'\left( A'B \right)\]

From (2),

\[\Rightarrow \left( B'AB \right)'=B'\left( -AB \right)\]

\[\Rightarrow \left( B'AB \right)'=-B'AB\]

Thus, if \[A\] is a skew-symmetric matrix, then \[B'AB\] is a skew-symmetric matrix.

Therefore, the matrix \[B'AB\] is symmetric or skew symmetric accordingly as \[A\] is symmetric or skew symmetric.


3. Find the values of x, y, z if the matrix A =$\begin{pmatrix}0 & 2y& z \\x&y& -z \\ x& -y& z\\\end{pmatrix}$ satisfy the equation ${A}'A=I$

Ans: Given matrix A=$\begin{bmatrix} 0 & 2y& z \\ x & y & -z \\ x& -y& z \\ \end{bmatrix}$

Transpose of A = ${A}'=\begin{bmatrix} 0 &x &x \\ 2y& y &-y \\ z& -z &z \\ \end{bmatrix}$

Given ${A}'A=I$

= $\begin{bmatrix} 0 & 2y &z \\ x& y&-z \\ x& -y &z \\ \end{bmatrix}\begin{bmatrix} 0 &x &x \\ 2y& y &-y \\ z &-z &z \\ \end{bmatrix}=\begin{bmatrix} 1 & 0 &0 \\ 0& 1& 0 \\ 0& 0 &1 \\ \end{bmatrix}$

${A}'$=$\begin{bmatrix} 0+x^{2}+x^{2} &0+xy-xy &0-xz+xz \\ 0+xy-xy & 4y^{2}+y^{2}+y^{2} &2yz-yz-yz \\ 0-zx+zx& 2yz-yz-yz &z^{2}+z^{2}+z^{2} \\ \end{bmatrix}=\begin{bmatrix} 1 &0 &0 \\ 0& 1& 0\\ 0& 0 &1 \\ \end{bmatrix}$

=$\begin{bmatrix} 2x^{2} &0 &0 \\ 0& 6y^{2}&0 \\ 0& 0 & 3z^{2} \\ \end{bmatrix}$ =$\begin{pmatrix} 1 &0 &0 \\ 0& 1& 0\\ 0& 0 &1 \\ \end{pmatrix}$

To find the values of x, y, z we have to equate the entries with corresponding matrix

$2x^{2}=1$

$\Rightarrow x^{2}$=$\dfrac{1}{2}$

$\Rightarrow$ x= $\pm \dfrac{1}{\sqrt{2}}$


$6y^{2}=1$

$\Rightarrow y^{2}$ = $\dfrac{1}{6}$

$\Rightarrow$ y = $\pm \dfrac{1}{\sqrt{6}}$ 

And

$3z^{2} = 1$

$\Rightarrow z^{2} = \dfrac{1}{3}$

$\Rightarrow$ z= $\pm \dfrac{1}{\sqrt{3}}$


4. For what values of \[x\] , \[\left[ \begin{matrix}   1 & 2 & 1  \\ \end{matrix} \right]\left[ \begin{matrix}   1 & 2 & 0  \\   2 & 0 & 1  \\   1 & 0 & 2  \\ \end{matrix} \right]\left[ \begin{matrix}   0  \\   2  \\   x  \\ \end{matrix} \right]=O\] ?

Ans: Given \[\left[ \begin{matrix}   1 & 2 & 1  \\ \end{matrix} \right]\left[ \begin{matrix}   1 & 2 & 0  \\   2 & 0 & 1  \\   1 & 0 & 2  \\ \end{matrix} \right]\left[ \begin{matrix}   0  \\   2  \\   x  \\ \end{matrix} \right]=O\]

\[\Rightarrow \left[ \begin{matrix}   1+4+1 & 2+0+0 & 0+2+2  \\ \end{matrix} \right]\left[ \begin{matrix}   0  \\   2  \\   x  \\ \end{matrix} \right]=O\]

\[\Rightarrow \left[ \begin{matrix}   6 & 2 & 4  \\\end{matrix} \right]\left[ \begin{matrix}   0  \\   2  \\   x  \\ \end{matrix} \right]=O\]

\[\Rightarrow \left[ 6\left( 0 \right)+2\left( 2 \right)+4\left( x \right) \right]=O\]

\[\Rightarrow \left[ 4+4x \right]=\left[ 0 \right]\]

\[\Rightarrow 4+4x=0\]

\[\therefore x=-1\]

Thus, the required value of \[x\] is \[-1\] .


5. If \[A=\left[ \begin{matrix}   3 & 1  \\   -1 & 2  \\ \end{matrix} \right]\] , show that \[{{A}^{2}}-5A+7I=O\] .

Ans: Given \[A=\left[ \begin{matrix}   3 & 1  \\   -1 & 2  \\ \end{matrix} \right]\]

\[\therefore {{A}^{2}}=A\cdot A\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   3 & 1  \\   -1 & 2  \\ \end{matrix} \right]\left[ \begin{matrix}   3 & 1  \\   -1 & 2  \\ \end{matrix} \right]\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   3\left( 3 \right)+1\left( -1 \right) & 3\left( 1 \right)+1\left( 2 \right)  \\   -1\left( 3 \right)+2\left( -1 \right) & -1\left( 1 \right)+2\left( 2 \right)  \\ \end{matrix} \right]\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   9-1 & 3+2  \\   -3-2 & -1+4  \\ \end{matrix} \right]\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   8 & 5  \\   -5 & 3  \\ \end{matrix} \right]\]

LHS: \[{{A}^{2}}-5A+7I\]

\[\therefore \left[ \begin{matrix}   8 & 5  \\   -5 & 3  \\ \end{matrix} \right]-5\left[ \begin{matrix}  3 & 1  \\   -1 & 2  \\ \end{matrix} \right]+7\left[ \begin{matrix}   1 & 0  \\   0 & 1  \\ \end{matrix} \right]\]

\[\Rightarrow \left[ \begin{matrix}   8 & 5  \\   -5 & 3  \\ \end{matrix} \right]-\left[ \begin{matrix}   15 & 5  \\   -5 & 10  \\ \end{matrix} \right]+\left[ \begin{matrix}   7 & 0  \\   0 & 7  \\ \end{matrix} \right]\]

\[\Rightarrow \left[ \begin{matrix}   0 & 0  \\   0 & 0  \\ \end{matrix} \right]\] 

\[\Rightarrow O\]

RHS: \[\Rightarrow O\]

\[LHS=RHS\]

\[\therefore {{A}^{2}}-5A+7I=O\] hence proved.


6. Find \[X\] , if \[\left[ \begin{matrix}   x & -5 & -1  \\ \end{matrix} \right]\left[ \begin{matrix}   1 & 0 & 2  \\   0 & 2 & 1  \\   2 & 0 & 3  \\ \end{matrix} \right]\left[ \begin{matrix}   x  \\   4  \\   1  \\ \end{matrix} \right]=O\] .

Ans: Given \[\left[ \begin{matrix}   x & -5 & -1  \\ \end{matrix} \right]\left[ \begin{matrix}   1 & 0 & 2  \\   0 & 2 & 1  \\   2 & 0 & 3  \\ \end{matrix} \right]\left[ \begin{matrix}   x  \\   4  \\   1  \\ \end{matrix} \right]=O\]

\[\Rightarrow \left[ \begin{matrix}   x-2 & -10 & 2x-8  \\ \end{matrix} \right]\left[ \begin{matrix}   x  \\   4  \\   1  \\ \end{matrix} \right]=O\]

\[\Rightarrow \left[ x\left( x-2 \right)-40+2x-8 \right]=O\]

\[\Rightarrow \left[ {{x}^{2}}-48 \right]=\left[ 0 \right]\]

\[\Rightarrow {{x}^{2}}-48=0\]

\[\therefore x=\pm 4\sqrt{3}\]

Thus, the required value of \[x\] is \[\pm 4\sqrt{3}\] .


7. A manufacture produces three products \[X,Y,Z\] which he sells in two markets.

Annual sales are indicated below:


Market

Products

\[I\]

\[10000\]

\[2000\]

\[18000\]

\[II\]

\[6000\]

\[20000\]

\[8000\]



(a) If unit sale prices of \[X,Y,Z\] are Rs \[2.50\] , Rs \[1.50\] and Rs \[1.00\] , respectively, find the total revenue in each market with the help of matrix algebra.

Ans: Here the total revenue in market \[I\] can be represented in the form of a matrix as:

\[\left[ \begin{matrix}   10000 & 2000 & 18000  \\ \end{matrix} \right]\left[ \begin{matrix}   2.50  \\   1.50  \\   1.00  \\ \end{matrix} \right]\]

\[\Rightarrow 1000\times 2.50+2000\times 1.50+18000\times 1.00\]

\[\Rightarrow 46000\]

And, the total revenue in market \[II\] can be represented in the form of a matrix as:

\[\left[ \begin{matrix}   6000 & 20000 & 8000  \\ \end{matrix} \right]\left[ \begin{matrix}   2.50  \\   1.50  \\   1.00  \\ \end{matrix} \right]\]

\[\Rightarrow 6000\times 2.50+20000\times 1.50+8000\times 1.00\]

\[\Rightarrow 53000\]

Thus, the total revenue in market \[I\] is Rs \[46000\] and the same in market \[II\] is Rs \[53000\].


(b) If the unit costs of the above three commodities are Rs \[2.00\] , Rs \[1.00\] and \[50\] paise respectively. Find the gross profit.

Ans: Here, the total cost prices of all the products in the market \[I\] can be represented in the form of a matrix as:

\[\left[ \begin{matrix}   10000 & 2000 & 18000  \\ \end{matrix} \right]\left[ \begin{matrix}   2.00  \\   1.00  \\   0.50  \\ \end{matrix} \right]\]

\[\Rightarrow 1000\times 2.00+2000\times 1.00+18000\times 0.50\]

\[\Rightarrow 31000\]

As the total revenue in market \[I\] is Rs \[46000\] , the gross profit in this market is \[Rs46000-Rs31000=Rs15000\] .

And, the total revenue in market \[II\] can be represented in the form of a matrix as:

\[\left[ \begin{matrix}   6000 & 20000 & 8000  \\ \end{matrix} \right]\left[ \begin{matrix}   2.00  \\   1.00  \\   0.50  \\ \end{matrix} \right]\]

\[\Rightarrow 6000\times 2.00+20000\times 1.00+8000\times 0.50\]

\[\Rightarrow 36000\]

As the total revenue in market \[II\] is Rs \[53000\] , the gross profit in this market is \[Rs53000-Rs36000=Rs17000\] .


8. Find the matrix \[X\] so that \[X\left[ \begin{matrix}   1 & 2 & 3  \\   4 & 5 & 6  \\ \end{matrix} \right]=\left[ \begin{matrix}   -7 & -8 & -9  \\   2 & 4 & 6  \\ \end{matrix} \right]\].

Ans: Given \[X\left[ \begin{matrix}   1 & 2 & 3  \\   4 & 5 & 6  \\ \end{matrix} \right]=\left[ \begin{matrix}   -7 & -8 & -9  \\   2 & 4 & 6  \\ \end{matrix} \right]\]

Here, \[X\] has to be a \[2\times 2\] matrix.

Now, let \[X=\left[ \begin{matrix}   a & c  \\   b & d  \\ \end{matrix} \right]\]

Thus, we have:

\[\left[ \begin{matrix}   a & c  \\   b & d  \\ \end{matrix} \right]\left[ \begin{matrix}   1 & 2 & 3  \\   4 & 5 & 6  \\ \end{matrix} \right]=\left[ \begin{matrix}   -7 & -8 & -9 \\   2 & 4 & 6  \\ \end{matrix} \right]\]

\[\Rightarrow \left[ \begin{matrix}   a+4c & 2a+5c & 3a+6c  \\   b+4d & 2b+5d & 3b+6d  \\ \end{matrix} \right]=\left[ \begin{matrix}   -7 & -8 & -9  \\   2 & 4 & 6  \\ \end{matrix} \right]\]

Comparing the corresponding elements of two matrices, we have:

\[a+4c=-7\]

\[2a+5c=-8\]

\[3a+6c=-9\]

\[b+4d=2\]

\[2b+5d=4\]

\[3b+6d=6\]

Now, solving the above equations we get,

\[\therefore a=1,b=2,c=-2,d=0\]

Thus, the required matrix \[X\] is \[\left[ \begin{matrix}   1 & -2  \\   2 & 0  \\ \end{matrix} \right]\].


9. Choose the correct answer in the following questions:

If \[A=\left[ \begin{matrix}   \alpha  & \beta   \\   \gamma  & -\alpha   \\ \end{matrix} \right]\] is such that \[{{A}^{2}}=I\] then 

  1. \[1+{{\alpha }^{2}}+\beta \gamma =0\]

  2. \[1-{{\alpha }^{2}}+\beta \gamma =0\]

  3. \[1-{{\alpha }^{2}}-\beta \gamma =0\]

  4. \[1+{{\alpha }^{2}}-\beta \gamma =0\]

Ans: Given \[A=\left[ \begin{matrix}   \alpha  & \beta   \\   \gamma  & -\alpha  \\ \end{matrix} \right]\] 

\[\therefore {{A}^{2}}=A\cdot A\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   \alpha  & \beta   \\   \gamma  & -\alpha   \\ \end{matrix} \right]\left[ \begin{matrix}   \alpha  & \beta   \\   \gamma  & -\alpha   \\ \end{matrix} \right]\]

\[\Rightarrow {{A}^{2}}=\left[ \begin{matrix}   {{\alpha }^{2}}+\beta \gamma  & \alpha \beta -\alpha \beta   \\   \alpha \gamma -\alpha \gamma  & \beta \gamma +{{\alpha }^{2}}  \\ \end{matrix} \right]\]

Now, \[{{A}^{2}}=I\]

\[\Rightarrow \left[ \begin{matrix}   {{\alpha }^{2}}+\gamma  & 0  \\   0 & \beta \gamma +{{\alpha }^{2}}  \\ \end{matrix} \right]=\left[ \begin{matrix}   1 & 0  \\   0 & 1  \\ \end{matrix} \right]\]

Equating the corresponding elements, we get:

\[\beta \gamma +{{\alpha }^{2}}=1\]

\[\Rightarrow {{\alpha }^{2}}+\beta \gamma -1=0\]

\[\Rightarrow 1-{{\alpha }^{2}}-\beta \gamma =0\]


10. If the matrix \[A\] is both symmetric and skew symmetric, then

  1. \[A\] is a diagonal matrix

  2. \[A\] is a zero matrix

  3. \[A\] is a square matrix

  4. None of these

Ans: If a matrix \[A\] is both symmetric and skew symmetric, then

\[A'=A\] and \[A'=-A\]

\[\Rightarrow A=-A\]

\[\Rightarrow A+A=O\]

\[\Rightarrow A=O\]

Thus, option (B) is correct.


11. If \[A\] is a square matrix such that \[{{A}^{2}}=A\] , then \[{{\left( I+A \right)}^{3}}-7A\] is equal to

  1. \[A\]

  2. \[I-A\]

  3. \[I\]

  4. \[3A\]

Ans: \[{{\left( I+A \right)}^{3}}-7A={{I}^{3}}+{{A}^{3}}+3A+3{{A}^{2}}-7A\]

\[\Rightarrow {{\left( I+A \right)}^{3}}-7A=I+{{A}^{3}}+3A+3{{A}^{2}}-7A\]

Given that \[{{A}^{2}}=A\] ,

\[\Rightarrow {{\left( I+A \right)}^{3}}-7A=I+{{A}^{2}}\cdot A+3A+3{{A}^{2}}-7A\]

\[\Rightarrow {{\left( I+A \right)}^{3}}-7A=I+A\cdot A-A\]

\[\Rightarrow {{\left( I+A \right)}^{3}}-7A=I\]

Thus, option (C) is correct.


Conclusion

Matrices Class 12 Miscellaneous Exercise Solutions are important for understanding various concepts thoroughly. Class 12 Maths Chapter 3 Miscellaneous Exercise Solutions covers diverse problems that require the application of multiple formulas and techniques. It's important to focus on understanding the underlying principles behind each question rather than just memorizing solutions. Remember to understand the theory behind each concept, practice regularly, and refer to solved examples to master this exercise effectively.


Class 12 Maths Chapter 3: Exercises Breakdown

Exercise

Number of Questions

Exercise 3.1

10 Questions & Solutions

Exercise 3.2

22 Questions & Solutions

Exercise 3.3

12 Questions & Solutions

Exercise 3.4

1 Questions & Solutions



CBSE Class 12 Maths Chapter 3 Other Study Materials



Chapter-Specific NCERT Solutions for Class 12 Maths

Given below are the chapter-wise NCERT Solutions for Class 12 Maths. Go through these chapter-wise solutions to be thoroughly familiar with the concepts.


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FAQs on NCERT Solutions For Class 12 Maths Chapter 3 Matrices

1. How do I solve questions involving basic matrix operations in the NCERT Class 12 Maths Chapter 3 exercises?

To solve problems involving matrix operations, follow the specific rules for each operation as per the NCERT guidelines for the 2025-26 session:

  • Addition/Subtraction: Ensure the matrices have the same order (same number of rows and columns). Then, add or subtract the corresponding elements.
  • Scalar Multiplication: To multiply a matrix by a scalar (a constant number), multiply every element of the matrix by that scalar.
  • Matrix Multiplication: To multiply two matrices A and B (product AB), the number of columns in A must be equal to the number of rows in B. The resulting matrix will have the same number of rows as A and columns as B.

Following these foundational rules step-by-step is key to arriving at the correct solution for NCERT problems.

2. What is the correct step-by-step method to solve a system of linear equations using the matrix inverse method from Chapter 3?

The matrix inverse method is a crucial technique for solving systems of linear equations. The correct procedure as per NCERT solutions is as follows:

  1. Represent the system of equations in the form AX = B, where A is the coefficient matrix, X is the variable matrix, and B is the constant matrix.
  2. Calculate the inverse of the coefficient matrix, A⁻¹. This step is only possible if A is an invertible matrix.
  3. The solution is then found by pre-multiplying both sides by A⁻¹, which gives the formula X = A⁻¹B.
  4. Perform the matrix multiplication of A⁻¹ and B to find the values of the variables in matrix X.

3. How many exercises are in NCERT Class 12 Maths Chapter 3, and what is the focus of each?

Chapter 3, Matrices, in the NCERT textbook for the 2025-26 syllabus, contains four main exercises and a Miscellaneous Exercise. The solutions for each are designed to build your skills progressively:

  • Exercise 3.1: Introduces the definition of a matrix, its order, types of matrices, and equality of matrices.
  • Exercise 3.2: Focuses on the fundamental operations on matrices, including addition, subtraction, scalar multiplication, and matrix multiplication.
  • Exercise 3.3: Covers the transpose of a matrix and its properties, including symmetric and skew-symmetric matrices.
  • Exercise 3.4: Deals with finding the inverse of a matrix using elementary operations (transformations).
  • Miscellaneous Exercise: Contains a mix of higher-order thinking skill (HOTS) questions that test your comprehensive understanding of all concepts in the chapter.

4. Why is checking the order of matrices before multiplication essential for getting the correct NCERT solution?

Checking the order of matrices is not just a procedural step; it is fundamental to the definition of matrix multiplication. For a product AB to be defined, the number of columns in matrix A must equal the number of rows in matrix B. This is because the calculation for each element in the resulting matrix involves a dot product of a row from A with a column from B. If this condition is not met, the operation is undefined, and attempting to multiply them will lead to an incorrect solution. This is a common pitfall in board exams.

5. What is the correct procedure for finding the inverse of a square matrix using elementary row operations as per the NCERT solutions?

Finding the inverse of a matrix A using elementary row operations is a systematic process. The correct method is:

  1. Start with the equation A = IA, where I is the identity matrix of the same order as A.
  2. Apply a sequence of elementary row operations to the matrix A on the left-hand side (LHS) with the goal of converting it into the identity matrix, I.
  3. Simultaneously, apply the exact same sequence of operations to the identity matrix I on the right-hand side (RHS).
  4. When the LHS becomes I, the matrix on the RHS will be the inverse, A⁻¹. The equation will be transformed into I = (A⁻¹)A.

It is crucial to apply operations consistently and accurately to avoid errors.

6. How are the concepts of symmetric and skew-symmetric matrices used to solve specific types of questions in Chapter 3?

The concepts of symmetric (where A' = A) and skew-symmetric (where A' = -A) matrices are primarily used to solve problems based on a key theorem: any square matrix can be expressed as the sum of a symmetric and a skew-symmetric matrix. NCERT exercises include questions where you must find these two component matrices for a given square matrix A using the formulas:

  • Symmetric part: P = ½ (A + A')
  • Skew-symmetric part: Q = ½ (A - A')

Solving these requires you to first find the transpose (A'), then calculate P and Q, and finally verify that P is symmetric, Q is skew-symmetric, and their sum equals A.

7. Why is matrix multiplication not commutative (AB ≠ BA), and how does this affect solving NCERT problems?

Matrix multiplication is not commutative because the calculation process is order-dependent. The element in the i-th row and j-th column of the product is found by multiplying the i-th row of the first matrix with the j-th column of the second. When you reverse the order to BA, you are multiplying different rows and columns, which generally produces a completely different result matrix or may not even be defined. In solving NCERT problems, especially proofs and algebraic manipulations involving matrices, you cannot assume AB equals BA. This is a fundamental difference from scalar algebra and must be respected to avoid incorrect steps and solutions.

8. How do the NCERT solutions for Chapter 3 prepare students for the next chapter on Determinants?

Mastering the NCERT solutions for Matrices (Chapter 3) is a critical prerequisite for Determinants (Chapter 4). The key connections are:

  • Invertible Matrices: The concept of an invertible matrix, explored in Chapter 3, is directly linked to its determinant. You will learn in Chapter 4 that a matrix is invertible if and only if its determinant is non-zero.
  • Solving Systems of Equations: The matrix inverse method (AX=B) learned in this chapter is a foundational application that is further explored and solved using determinants (Cramer's rule) in the next chapter.
  • Square Matrices: Determinants are calculated only for square matrices, a concept thoroughly established in Chapter 3.

A strong grasp of matrix operations and properties from Chapter 3 makes the computational and theoretical aspects of Chapter 4 much easier to understand.