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STU33009
TRINITY COLLEGE DUBLIN
School of Computer Science and Statistics
Mid-Term Assignment 2020-21 STU33009: Statistical Methods for Computer Science
Submitting Your Report
• Reports must be typed (no handwritten answers please) and submitted
on Blackboard.
• As a guideline, reports should be about 5 pages in length including all
plots (please don’t go a lot over this).
• You will need to use matlab to calculate values, or alternatively write a
short program in python to do this. In either case give the code used as
an appendix to the report (it doesn’t count towards the page limit), but
please keep the code short.
• In order to obtain full credit it is essential that you explain/justify how you
obtained your results and, where appropriate, that you critically reflect
upon them. Simply giving raw numbers as answers will receive few marks
as will saying“see code for details”and the like, even if the code contains
explanatory comments.
• It is mandatory to complete the declaration that the work is entirely your
own and you have not collaborated with anyone – the declaration form is
available on Blackboard.
Downloading Data
In this assignment you will analyse the data on shopping behaviour. Start by downloading
the following dataset:
• https://www.scss.tcd.ie/doug…. Important: You
must fetch your own copy of the dataset, do not use the dataset downloaded by someone
else. Keep the dataset that you download as I might request it to validate your
results.
• The data file consists of rows of data. Each row i corresponds to one supermarket
shopping basket and each column j corresponds to one item for sale. The value Zi,j
in row i, column j gives how many of the j’th item are in the i’th shopping basket.
Assignment
- (a) Plot a histogram showing the PMF of the number of items in a basket. Hint:
Summing the values in a row gives the number of items in that shopping basket.
[5 marks]
(b) Estimate the probability P(Zi,1 = 1) that the first column in the dataset takes
value 1 i.e. that a shopping basket contains an item 1. Briefly explain/discuss
your calculation. Hint: Observe that the first column in the dataset only takes
values 0 or 1 and recall that for an indicator RV X we have P rob(X = 1) = E[X].
[5 marks]
(c) Derive a confidence interval for your estimate P(Zi,1 = 1) using the CLT and
Chebyshev Inequality. Explain/discuss your calculation. [5 marks]
(d) Suppose we require to estimate the value of P(Zi,1 = 1) to an accuracy of ±1%
with 95% confidence. How many shopping baskets would we need to collect data
from? [5 marks] - Your task is to explore whether the presence of item 1 in a shopping basket can be
predicted from the presence of other items in the basket. We start with whether item - in the basket is predictive of item 1 being in the basket. Since the first column
in the dataset only takes values 0 or 1, its conditional expectation E[Zi,1|Zi,2 =
z] = P(Zi,1 = 1|Zi,2 = z). the sum is taken over the baskets with second column equal to z, and N = |{i :
Zi,2 = z}| is the size of this set. This sample mean concentrates on E[Zi,1|Zi,2 = z]
as the number of shopping baskets observed grows.
(a) Calculate the sample mean of Zi,1 conditioned on the second column Zi,2 = z
for z = 0, 1, . . . being each of the different values that the second column takes.
Report the values in a table. Briefly explain/discuss your calculation. [5 marks]
(b) Derive confidence intervals for your estimate E[Zi,1|Zi,2 = z] using the CLT and
Chebyshev Inequality. Explain your working and extend your table from (a) to
include these intervals. [5 marks]
(c) Using the matlab errorbar() function, or python equivalent, plot your estimates
of E[Zi,1|Zi,2 = z] vs z together with their confidence intervals i.e. a plot with
z on the x-axis and the estimate of E[Zi,1|Zi,2 = z] on the y-axis, together with
error bars indicating the confidence interval around this estimate. Discuss. [5
marks]
(d) Compare your estimate of E[Zi,1|Zi,2 = z] with your estimate of E(Zi,1) from
part 1(b)-(c), bearing in mind their confidence intervals. Critically discuss
whether the presence of item 2 in the basket is predictive of item 1 being in
the basket. [5 marks] - (a) Repeat your analysis in 2(d) but now using only the first 100 rows from the
dataset (its enough to plot the data, no need to include a table of values). What
is the impact on the confidence intervals of using less data, and why? How does
that impact what conclusions you can draw from the data? [5 marks]
(b) Now repeat 2(d) but for E[Zi,1|Zi,3 = z] i.e. conditioned on the third column
Zi,3 = z. Compare and contrast the behaviour with that observed when conditioning
on the second column, again bearing in mind the confidence intervals.
[5 marks]