- (a) In this question, A,B, and C are random variables with means µA,µB, and µC respectively, and a common variance σ
2
. An independent random sample of size 10 is
taken from each of these random variables, with the following results:
a¯ = 49.617 ∑
10
i=1
a
2
i = 24635.30 ∑
10
i=1
(ai −a¯)
2 = 16.832
b¯ = 48.526 ∑
10
i=1
b
2
i = 23583.16 ∑
10
i=1
(bi −b¯)
2 = 35.438
c¯ = 51.031 ∑
10
i=1
c
2
i = 26100.33 ∑
10
i=1
(ci −c¯)
2 = 58.704
i) Explain what assumptions on A,B, andC are required in order to perform a oneway ANOVA test of the null hypothesis µA = µB = µC against the alternative that
this is false. [2 marks]
ii) Given that those assumptions hold, perform the ANOVA test at the 0.05 significance level. [5 marks]
iii) Let SSw denote the total sum of squares random variables in this ANOVA set-up.
What is the distribution of SSw/σ
2? [3 marks]
iv) Derive a 95% confidence interval for σ
2
. [4 marks]
Version 1 Page 2 of 7
(b) You have performed a simple linear regression with independent variable x, resulting
the following plot of the residual at xi against xi
:
Does this cast any doubt on the assumptions of the simple linear regression? Explain
your answer. [2 marks]
MA2261-MA7023
All candidates
(c) You are given the following paired data
i xi yi
1 1 0.93
2 2 −0.70
3 3 −2.94
4 4 −5.00
5 5 −6.90
6 6 −9.16
7 7 −10.95
8 8 −12.87
9 9 −15.33
10 10 −16.96
for which
x¯ = 5.5
y¯ = −7.988,
10
∑
i=1
(yi −y¯)
2 = 336.62,
10
∑
i=1
(xi −x¯)
2 = 82.5,
10
∑
i=1
(xi −x¯)(yi −y¯) = −166.59.
Assume that the data satisfy the usual simple linear regression model, so that the yi
are observations of a random variable Y whose conditional distribution given X = x
has the form N(a+bx,σ
2
).
i) Calculate the predicted mean response if x = 11. [4 marks]
ii) Find a 95% confidence interval for the slope parameter b. [5 marks]
Total: 25 marks
Version 1 Page 4 of 7
MA2261-MA7023
All candidates - i) State the central limit theorem for a sequence of independent and identically distributed random variables X1,X2,… with finite mean µ and variance σ
2
. [3 marks]
ii) Let X be a random variable with the Bernoulli(p) distribution, so that
P(X = 1) = p, P(X = 0) = 1− p.
a) Derive the expected value of X. [3 marks]
b) Derive the variance of X. [3 marks]
iii) Let B be a random variable with the Binomial(n, p) distribution. Using the central
limit theorem and the fact that B has the same distribution as
X1 +···+Xn,
where X1,…,Xn are independent and each has the Bernoulli(p) distribution, show
that
B−np
p
np(1− p)
has approximately a standard normal distribution for large n. [6 marks]
iv) Someone claims to be able to predict the outcome of a coin flip. In n = 100 flips they
are correct 55 times. Let B be a random variable counting the number of successful
predictions in 100 flips and p be the probability they predict a single flip correctly.
Test the hypothesis H0 : p = 1/2 against the alternative H1 : p > 1/2 at significance
level α = 0.05 using the test statistic
T =
B−100p
p
100p(1− p)
,
which you can assume to have approximately the N(0,1) distribution. [6 marks]
v) What is the minimum number of successful predictions out of 100 flips which would
cause you to reject the null hypothesis in the test of part iv)? [4 marks]
Total: 25 marks
Version 1 Page 5 of 7
MA2261-MA7023
All candidates - (a) Hooke’s law states that the elongation L of a spring subjected to a weight force W
is given by W = kL, where W is measured in kg, L is measured in mm, k is called
the elastic constant of the spring and is expressed in kg/mm. A group of Physics
students wants to measure the elastic constant of a spring. Not having very precise
instruments they perform an experiment by applying to the spring weights of increasing intensity, from 10 to 50 kg, for five times. The measurements are influenced by
the approximation of the reading of the lengths and by the fact that the spring does
not behave like a perfect spring and the same weight applied several times does not
give the same elongation. The measurements of elongation, in millimeters, for each
test done are shown below.
Weight (kg) Measured elongation L (mm)
W L1 L2 L3 L4 L5
10 48.6 47.6 48.8 51.5 49.8
15 78.4 77.5 71.6 77.5 73.6
20 95.7 98.6 100.4 102.4 97.3
25 123.5 131.1 118.9 130.6 128.3
30 150.6 154.5 148.3 146.0 153.3
35 175.5 176.3 173.2 181.8 181.8
40 209.4 199.8 197.8 195.9 203.5
45 230.9 233.2 230.5 218.7 222.6
50 245.2 249.2 257.0 256.7 244.3
i. Write the equation of a simple linear regression model with repeated observations for this dataset. [3 marks]
ii. Perform a statistical test to establish if a linear regression model with repeated
observations is a good fit for these data. [6 marks]
iii. Perform a statistical test to establish if Hookes law can be assumed to be valid
for this dataset with the 5% significance level. [5 marks]
(b) In this question, X is a continuous random variable with probability density function
f(x) = (
α
−1
x
(α
−1
)−1 0 < x < 1 0 otherwise. Here α is a positive parameter which we will estimate using an independent random sample from X. i) Show that if t < α −1 then E(X −t ) = α −1 α−1−t . [3 marks] Version 1 Page 6 of 7 MA2261-MA7023 All candidates ii) Show that the log-likelihood function l(a) based on independent observations x1,…, xn of X is l(a) = −nloga+ (a −1 −1) n ∑ i=1 logxi for a > 0. [2 marks]
iii) Show that the maximum likelihood estimator of α is
1
n
n
∑
i=1
(−logXi).
You may assume any critical point of l is a maximum. [2 marks]
iv) Show that the maximum likelihood estimator of α is unbiased, and compute its
mean squared error. You can use without proof the fact that if Z ∼ Exponential(λ)
then E(Z) = λ
−1 and V(Z) = λ
−2
. [4 marks]
Total: 25 marks
Version 1 Page 7 of 7
MA2261-MA7023
All candidates - (a) In this question, X is a continuous random variable with probability density function
fX(x) = (
θx
θ−1 0 < x < 1 0 otherwise, where θ is a positive parameter. i) Derive E(X) and V(X). [6 marks] ii) Let y be a real number. Show that P(X ≥ y) = 1 y ≤ 0 1−y θ 0 < y < 1 0 y ≥ 1 [4 marks] iii) Use the previous part to show that the density function fX−1 of X −1 is fX−1 (r) = θr −θ−1 if r > 1 and fX−1 (r) = 0 otherwise. [5 marks]
(b) In this question, X and Y are jointly distributed discrete random variables. The probability P(X = i,Y = j)for discrete random variable (X,Y) is given by the entry in
column i, row j of the following table:
X
0 1 2
Y
0 pq q− pq 0
1 p− pq
p
2 −q+ pq 1−
3p
2
i) Show that P(X = 0) = p, P(X = 1) = p/2 and P(X = 2) = 1−3p/2.
[3 marks]
ii) Are the events Y = 0 and X = 0 independent? Justify your answer.
[2 marks]
iii) For which values of p and q are the events X = 2 and Y = 1 independent?
[1 mark]
iv) Give a formal definition of what it means for two random variables to be independent. [1 mark]
v) For which values of p and q are the random variables X and Y independent?
[3 marks]
Total: 25 marks
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