Math Problem Statement
Solution
The problem involves transforming a set of random variables (with given covariance matrix ) into a new set of random variables using a transformation matrix . You are asked to find the covariance matrix of the new random variables.
Given:
- Covariance matrix : 3.0 & 1.8 & 1.1 \\ 1.8 & 3.0 & 1.8 \\ 1.1 & 1.8 & 1.1 \end{bmatrix}$$
- Transformation matrix : 4 & -1 & -2 \\ 2 & 2 & 1 \\ -3 & -1 & 3 \end{bmatrix}$$
To Find:
The covariance matrix of the new random variables , where: where is the transpose of the transformation matrix .
I'll now calculate the new covariance matrix .The covariance matrix of the new random variables is:
30.6 & 17.6 & -23.4 \\ 17.6 & 51.1 & -22.8 \\ -23.4 & -22.8 & 20.1 \end{bmatrix}$$ This matrix represents the covariances of the transformed variables. Do you want further details on how this result was derived or have any questions? Here are 5 related questions for further understanding: 1. How is a covariance matrix interpreted in the context of random variables? 2. Why do we use transformation matrices on covariance matrices? 3. How would the covariance matrix change if the transformation matrix $$T$$ were different? 4. What does a negative value in the covariance matrix imply about the relationship between two variables? 5. Can you generalize this approach to higher dimensions? **Tip**: To ensure matrix multiplication is valid, remember that the number of columns in the first matrix must equal the number of rows in the second.Ask a new question for Free
By Image
Drop file here or Click Here to upload
Math Problem Analysis
Mathematical Concepts
Linear Algebra
Covariance Matrix
Matrix Transformations
Formulas
C_Y = T * C_X * T^T
Theorems
Matrix Multiplication
Covariance Matrix Transformation
Suitable Grade Level
Undergraduate
Related Recommendation
Gaussian Random Variables Transformation with Covariance Matrix and Correlation
Transformation of Random Vectors with Covariance Matrix and Linear Transformations
Convert Matrix to Mean-Deviation Form and Calculate Sample Covariance Matrix
Proving Expectation of Quadratic Forms: E[y' A y] = tr(AV) + μ' A μ
Covariance of Transformed Random Vector Y = AX with Orthogonal Matrix A