IGNOU MST 18 SOLVED ASSIGNMENT

High Demand Verified Solution

★★★★★ 4.9/5 (3889 Students)

₹80

₹30

Language

Type

Session

MST 18: Multivariate Analysis

Title Name	IGNOU MST 18 SOLVED ASSIGNMENT
Type	Soft Copy (E-Assignment) .pdf
University	IGNOU
Degree	MASTER DEGREE PROGRAMMES
Course Code	MSCAST
Course Name	M.Sc. (Applied Statistics)
Subject Code	MST 18
Subject Name	Multivariate Analysis
Year	2025
Session	-
Language	English Medium
Assignment Code	MST 18/Assignment-1/2025
Product Description	Assignment of MSCAST (M.Sc. (Applied Statistics)) 2025. Latest MST 018 2026 Solved Assignment Solutions
Last Date of IGNOU Assignment Submission	Last Date of Submission of IGNOU BEGC-131 (BAG) 2025-26 Assignment is for January 2026 Session: 30th September, 2026 (for December 2025 Term End Exam). Semester Wise January 2025 Session: 30th March, 2026 (for June 2026 Term End Exam). July 2025 Session: 30th September, 2025 (for December 2025 Term End Exam).
Format	Ready-to-Print PDF (.soft copy)

📅 Important Submission Dates

January 2025 Session: 31st October, 2025
July 2025 Session: 30th April, 2025

Why Choose Our Solved Assignments?

• Accuracy: Solved by IGNOU subject experts.
• Guidelines: Strictly follows 2025-26 official word limits.
• Scoring: Designed to help students achieve 90+ marks.

📋 Assignment Content Preview

Included:

MST 018 (January 2025 - July 2025) - ENGLISH

TUTOR MARKED ASSIGNMENT

MST-018: Multivariate Analysis

Course Code: MST-018

Assignment Code: MST-018/TMA/2025

Maximum Marks: 100

Note: All questions are compulsory. Answer in your own words.

1. State whether the following statements are true or false and also give the reason in support of your answer:

(a) The covariance matrix of random vectors $underline{X} ext{and}underline{Y}$ is symmetric.

(b) $ifunderline{x}$ is a p-variate normal random vector, then every linear combination $underline{c{}'}$ $underline{x}$ is a scalar vector, is also p-variate normal vector

(d) If a matrix is positive definite then its inverse is also positive definite.

(e) $ext{If}underline{X}sim N_2left(egin{pmatrix}2\1end{pmatrix},egin{pmatrix}1&0\0&1end{pmatrix} ight) ext{and}underline{Y}sim N_2left(egin{pmatrix}-1\3end{pmatrix},egin{pmatrix}1&0\0&1end{pmatrix} ight), ext{then}$

$underline{X}+underline{Y}sim N_2left(egin{pmatrix}1\4end{pmatrix},egin{pmatrix}2&0\0&2end{pmatrix} ight).$

2(a) $ext{Let}underline{X}=egin{pmatrix}X_1\X_2end{pmatrix}$ has the following joint density function

$f(x_1,x_2)=egin{cases}4x_1x_2&,0<x_1<1,0<x_2<1\0&, ext{otherwise}.end{cases}$

Find the marginal distributions, mean vector and variance-covariance matrix. Also, comment on the independence of X₁and X₂

(b) $underline{X}=egin{pmatrix}underline{X}^{(1)}\underline{X}^{(2)}end{pmatrix}sim N_4(underline{mu},underline{Sigma})$ whare $underline{mu}=egin{pmatrix}-4\1\4\0end{pmatrix},quadunderline{Sigma}=egin{pmatrix}2&2&|&3&0\2&1&|&2&0\--&--&--&--&--\3&2&|&2&1\0&0&|&1&1end{pmatrix}$ Find the

$E(underline{X}^{(2)}|underline{X}^{(1)}=underline{x}^{(1)})quad ext{and}quad Cov(underline{X}^{(2)}|underline{X}^{(1)}=underline{x}^{(1)})$

3 (a) Let X  be a 3-dimensional random vector with dispersion matrix

$underline{Sigma}=egin{pmatrix}4&-2&0\-2&4&0\0&0&2end{pmatrix}$

Determine the first principal component and the proportion of the total variability that it explains.

(b) $underline{X}sim N_4(underline{mu},underline{Sigma}),quad ext{where}quadunderline{mu}=egin{pmatrix}3\-2\1\-2end{pmatrix}$ and $underline{Sigma}=egin{pmatrix}4&0&0&0\0&3&0&0\0&0&2&-2\0&0&-2&5end{pmatrix}$ Check the

independence $ext{of the(i)}X_2 ext{and}X_1quad ext{(ii)}(X_2,X_4) ext{and}(X_1,X_3)quad ext{(iii)}(X_1,X_2) ext{and}(X_3,X_4)$

4 (a) Consider the following data of 11 samples on 8 variables by Anscombe, Francis J. (1973):

x₁	x₂	x₃	x₄	y₁	y₂	y₃	y₄
10	10	10	8	8.04	9.14	7.46	6.58
8	8	8	8	6.95	8.14	6.77	5.76
13	13	13	8	7.58	8.74	12.74	7.71
9	9	9	8	8.81	8.77	7.11	8.84
11	11	11	8	8.33	9.26	7.81	8.47
14	14	14	8	9.96	8.10	8.84	7.04
6	6	6	8	7.24	6.13	6.08	5.25
4	4	4	19	4.26	3.10	5.39	12.50
12	12	12	8	10.84	9.13	8.15	5.56
7	7	7	8	4.82	7.26	6.42	5.56
5	7	5	8	5.68	4.74	5.73	5.56

If the vector $underline{x}=egin{pmatrix}x_1\x_2\x_3\x_4end{pmatrix}quad ext{and}quadunderline{y}=egin{pmatrix}y_1\y_2\y_3\y_4end{pmatrix}$ then obtain the sample covariance matrix between $underline{x} ext{and}underline{y}$

Source: Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17– 21. doi: 10.2307/2682899.

(b) Obtain the maximum likelihood estimator of the mean vector and variance-covariance matrix of the multivariate normal distribution.

5 (a) Define the following:

(i) Covariance Matrix

(ii) Mahalanobis D2

(iii) Hotelling’s T2

(iv) Clustering

(v) Relationship between (ii) and (iii).

(b) $ext{If}underline{X}sim N_3(underline{mu},underline{Sigma}) ext{with}underline{mu}=egin{pmatrix}2\1\2end{pmatrix} ext{and}underline{Sigma}=egin{pmatrix}5&3&0\3&3&-2\0&-2&5end{pmatrix}.$ Then find the joint distribution of $X_1+2X_2,quad 2X_1-X_2quad ext{and}quad X_3.$

❓ Frequently Asked Questions (FAQs)

Q: How will I receive the PDF?
A: Immediately after payment, the download link will appear and be sent to your email.

Q: Is this hand-written or typed?
A: This is a professional typed computer PDF. You can use it as a reference for your handwritten submission.

Get the full solved PDF for just Rs. 15