MATH3030-E1
The University of Nottingham
SCHOOL OF MATHEMATICAL SCIENCES
A LEVEL 3 MODULE, SPRING SEMESTER 2019-2020
MULTIVARIATE ANALYSIS
Suggested time to complete: TWO Hours THIRTY Minutes
Paper set: 21/05/2020 - 10:00
Paper due: 28/05/2020 - 10:00
Answer ALL questions
Your solutions should be written on white paper using dark ink (not pencil), on a tablet, or
typeset. Do not write close to the margins. Your solutions should include complete
explanations and all intermediate derivations. Your solutions should be based on the material
covered in the module and its prerequisites only. Any notation used should be consistent with
that in the Lecture Notes.
Guidance on the Alternative Assessment Arrangements can be found on the Faculty of Science
Moodle page: https://moodle.nottingham.ac.uk/course/view.php?id=99154#section-2
Submit your answers as a single PDF with each page in the correct orientation, to the
appropriate dropbox on the module’s Moodle page. Use the standard naming
convention for your document: [StudentID]_[ModuleCode].pdf. Please check the
box indicated on Moodle to confirm that you have read and understood the statement
on academic integrity: https://moodle.nottingham.ac.uk/pluginfile.php/6288943/mod_
tabbedcontent/tabcontent/8496/FoS%20Statement%20on%20Academic%20Integrity.pdf
A scan of handwritten notes is completely acceptable. Make sure your PDF is easily readable
and does not require magnification. Text which is not in focus or is not legible for any other
reason will be ignored. If your scan is larger than 20Mb, please see if it can easily be reduced
in size (e.g. scan in black & white, use a lower dpi — but not so low that readability is
compromised).
Staff are not permitted to answer assessment or teaching queries during the assessment
period. If you spot what you think may be an error on the exam paper, note this in your
submission but answer the question as written. Where necessary, minor clarifications or
general guidance may be posted on Moodle for all students to access.
Students with approved accommodations are permitted an extension of 3 days.
The standard University of Nottingham penalty of 5% deduction per working day will
apply to any late submission.
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MATH3030-E1
Academic Integrity in Alternative Assessments
The alternative assessment tasks for summer 2020 are to replace exams that would have
assessed your individual performance. You will work remotely on your alternative assessment
tasks and they will all be undertaken in “open book” conditions. Work submitted for
assessment should be entirely your own work. You must not collude with others or employ the
services of others to work on your assessment. As with all assessments, you also need to avoid
plagiarism. Plagiarism, collusion and false authorship are all examples of academic misconduct.
They are defined in the University Academic Misconduct Policy at: https://www.nottingham.ac.
uk/academicservices/qualitymanual/assessmentandawards/academic-misconduct.aspx
Plagiarism: representing another person’s work or ideas as your own. You could do this by
failing to correctly acknowledge others’ ideas and work as sources of information in an
assignment or neglecting to use quotation marks. This also applies to the use of graphical
material, calculations etc. in that plagiarism is not limited to text-based sources. There is
further guidance about avoiding plagiarism on the University of Nottingham website.
False Authorship: where you are not the author of the work you submit. This may include
submitting the work of another student or submitting work that has been produced (in whole
or in part) by a third party such as through an essay mill website. As it is the authorship of an
assignment that is contested, there is no requirement to prove that the assignment has been
purchased for this to be classed as false authorship.
Collusion: cooperation in order to gain an unpermitted advantage. This may occur where you
have consciously collaborated on a piece of work, in part or whole, and passed it off as your
own individual effort or where you authorise another student to use your work, in part or
whole, and to submit it as their own. Note that working with one or more other students to
plan your assignment would be classed as collusion, even if you go on to complete your
assignment independently after this preparatory work. Allowing someone else to copy your
work and submit it as their own is also a form of collusion.
Statement of Academic Integrity
By submitting a piece of work for assessment you are agreeing to the following statements:
1. I confirm that I have read and understood the definitions of plagiarism, false authorship
and collusion.
2. I confirm that this assessment is my own work and is not copied from any other person’s
work (published or unpublished).
3. I confirm that I have not worked with others to complete this work.
4. I understand that plagiarism, false authorship, and collusion are academic offences and I
may be referred to the Academic Misconduct Committee if plagiarism, false authorship or
collusion is suspected.
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1. (a) i) Briefly describe the method of principal components analysis and explain its main
uses.
ii) Describe the situations when it is most suitable to use principal components analysis
of the sample correlation matrix rather than the sample covariance matrix , and
when it is preferable to use rather than .
[10 marks]
(b) The profits (in £) of five banks ,, ,, from the United Kingdom were
recorded as vectors of length 5 over 40 quarter year periods. A principal components
analysis was performed on the sample covariance matrix with eigenvectors given by
PC1 PC2 PC3 PC4 PC5
0.421 -0.526 0.541 -0.176 0.472
0.457 0.509 0.178 0.676 0.206
0.421 -0.435 0.385 -0.382
0.470 0.260 0.335 -0.400 -0.662
0.464 0.240 -0.612 -0.451 0.387
with corresponding eigenvalues 2.502, 1.273, 0.329, 0.201, 0.136.
i) Calculate the value of (to two decimal places).
ii) Draw a scree plot and suggest the number of components that are needed to
describe the data adequately.
iii) Provide an interpretation of these components.
iv) Calculate the total percentage of variability explained by these components.
[10 marks]
(c) Data are available for another 7 banks from the United States over the same period.
State what method could be used to investigate the linear combinations of the bank
profits that are most highly correlated in the two datasets of UK and US banks, and
give brief details of the technique.
[10 marks]
(d) In the table below, Euclidean distances are given in a matrix between four pension
funds based on measurements of 12 financial variables.
Fund A Fund B Fund C Fund D
Fund A 0 2.1 2.0 2.4
Fund B 2.1 0 1.8 0.2
Fund C 2.0 1.8 0 2.5
Fund D 2.4 0.2 2.5 0
i) Apply the single linkagemethod to the matrix. Summarise your results graphically
using a dendrogram.
ii) Apply the complete linkage method to the matrix . Summarise your results
graphically using a dendrogram.
iii) Suppose exactly two clusters are required. What would be your clusters based on
(I) single linkage and (II) complete linkage?
iv) If two clusters are required then state which of these two linkage methods you
prefer, and briefly give your reasons.
[10 marks]
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2. (a) Let 1,… , be independent identically distributed(,) random variables. Denote
the sample mean by
̄ =
1


∑
=1

and the sample covariance matrix by
=
1


∑
=1
( − ̄)( − ̄)
⊤.
i) Using the result
(̄ − )−1(̄ − ) ∼ 2 .
describe how to obtain a confidence region for when is known.
ii) State, without proof, the distribution of
2 = ( − 1)(̄ − )−1(̄ − ).
iii) Explain how the result in ii) can be used to test 0 ∶ = 0 versus 1 ∶ ≠ 0.
In practice which null distribution is used for carrying out the test?
[10 marks]
(b) The length and width measurements in for a particular species of fish with sample
size = 30 have mean vector ̄1 = (81.50, 68.90)
and covariance matrix
1 = [
30.0 10.0
10.0 20.0]
.
It has been conjectured that the mean of the length and width of this species of fish
should equal 80 and 69 respectively, i.e. 0 = (80, 69)
⊤.
Examine this claim by carrying out a suitable test, and carefully state your assumptions.
[10 marks]
(c) The length and width measurements for a second species of fish with sample size = 35
have mean vector ̄2 = (84. ∗ ∗, 70.60)
⊤ and covariance matrix
2 = [
28.6 9.9
9.9 21.1]
,
where ∗∗ are the final two digits of your Student ID number. For example if your
Student ID is 53256178 then ∗∗ is 78 and the sample mean length of the second species
of fish is 84.78.
Carry out a two sample test using test statistic
2 =
( + − − 1)
( + − 2)

+
(̄1 − ̄2)
⊤−1 (̄1 − ̄2),
to investigate whether the two population means are the same or not, carefully stating
your assumptions. Note that is the pooled unbiased covariance matrix estimator.
[10 marks]
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(d) i) Comment on whether or not the assumptions for the test in (c) are reasonable.
ii) Briefly discuss an alternative procedure for testing the hypothesis in (c) which is
based on the multivariate linear model
= + ,
where the terms in the model should be specified. There is no need to carry out
this alternative test.
[10 marks]
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3. (a) Consider the following road distances between some cities in Europe (in km):
Athens Barcelona Brussels Calais Cherbourg Cologne
Athens 0 3313 2963 3175 3339 2762
Barcelona 3313 0 1318 1326 1294 1498
Brussels 2963 1318 0 204 583 206
Calais 3175 1326 204 0 460 409
Cherbourg 3339 1294 583 460 0 785
Cologne 2762 1498 206 409 785 0
i) Briefly describe how to obtain the principal co-ordinates using the method of classical
multidimensional scaling, making reference to how the centering matrix is used
in the calculation.
ii) The eigenvalues calculated from using classical multidimensional scaling for these
data are (×106):
(8.015, 1.423, 0.157, 0, −0.002, −0.019)
State whether or not the resulting estimated two dimensional map frommultidimensional
scaling is a good approximation to the spatial arrangement of the cities, giving your
reasons.
iii) Is the distance matrix between the cities a Euclidean distance matrix? Give your
reasoning.
[15 marks]
(b) For = 1, ..., let denote a population described by a probability density function
(|). Provide a brief explanation of the sample ML discriminant rule in this situation.
[5 marks]
(c) i) Measurements of cranial length (1) and cranial breadth (2), both measured in
millimetres, on samples of 40male and 40 female frogs led to the following statistics
for the sample mean vector (̄) and sample covariance matrix (), with = 1
for male frogs and = 2 for female frogs.
̄1 = (
24.1
24.8)
̄2 = (
22.8
23.5)
1 = (
6.5 3
3 6.5)
2 = (
5.5 3
3 5.5)
.
Assuming the data are multivariate normal and stating any additional assumptions
that youmake, derive a sample ML discriminant rule for allocating a new observation
to 1 or 2 for this example.
ii) Provide a suitable diagram and plot the straight line which separates the two
allocation regions and label each region.
iii) Would you classify a new observation = (23, 24) as male or female? Give your
reasoning.
iv) If the multivariate normal assumption looks suspect, e.g. the distributions have
thicker tails than the multivariate normal distribution, briefly describe a possible
strategy for obtaining an appropriate classification rule.
[20 marks]
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