MA200-5-AU-CO:
Statistics II

The details
2023/24
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Autumn
Undergraduate: Level 5
Current
Thursday 05 October 2023
Friday 15 December 2023
15
03 January 2024

 

Requisites for this module
MA101 and MA108
(none)
(none)
(none)

 

MA216, MA304, MA317, MA318, MA319, MA322

Key module for

BSC N233 Actuarial Science (Including Placement Year),
BSC N233DT Actuarial Science (Including Placement Year),
BSC N323 Actuarial Science,
BSC N323DF Actuarial Science,
BSC N323DT Actuarial Science,
BSC N324 Actuarial Science (Including Year Abroad),
BSC N325 Actuarial Science (Including Foundation Year),
BSC L1G2 Economics and Mathematics (Including Placement Year),
BSC LG11 Economics and Mathematics,
BSC LG18 Economics and Mathematics (Including Foundation Year),
BSC LG1C Economics and Mathematics (Including Year Abroad),
BSC GN13 Finance and Mathematics,
BSC GN15 Finance and Mathematics (Including Placement Year),
BSC GN18 Finance and Mathematics (Including Foundation Year),
BSC GN1H Finance and Mathematics (Including Year Abroad),
BSC G100 Mathematics,
BSC G102 Mathematics (Including Year Abroad),
BSC G103 Mathematics (Including Placement Year),
BSC G104 Mathematics (Including Foundation Year),
MMATG198 Mathematics,
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year),
BSC G1G4 Mathematics with Computing (Including Year Abroad),
BSC G1G8 Mathematics with Computing (Including Foundation Year),
BSC G1GK Mathematics with Computing,
BSC G1IK Mathematics with Computing (Including Placement Year),
BSC G1F3 Mathematics with Physics,
BSC G1F4 Mathematics with Physics (Including Placement Year),
BSC G1F5 Mathematics with Physics (Including Foundation Year),
BSC GCF3 Mathematics with Physics (Including Year Abroad),
BSC I1G3 Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year),
MSCIN399 Actuarial Science and Data Science,
MSCIG199 Mathematics and Data Science,
BSC N333 Actuarial Studies,
BSC N333DT Actuarial Studies,
BSC N334 Actuarial Studies (Including Placement Year),
BSC N334DT Actuarial Studies (Including Placement Year),
BSC N335 Actuarial Studies (Including Year Abroad)

Module description

This module introduces distribution theory, estimation and Maximum Likelihood estimators, hypothesis testing ending by exploring basic linear regression and multiple linear regression implemented in R.


This module uses the R software environment for statistical computing and graphics.

Module aims

The aims of this module are:



  • To cover part of the CS1 IFOA syllabus.

Module learning outcomes

By the end of the module, students will be expected to be able to:



  1. Define and be familiar with the discrete distributions: binomial, Poisson and uniform and be familiar with the continuous distributions: normal, exponential, chi-square, t, F and uniform.

  2. Use the one-to-one correspondence between an mgf and a pdf for sums of RVs.

  3. Handle bivariate distributions, understanding the relations between joint, marginal, conditional distributions and independence.

  4. Understand the uses of the central limit theorem.

  5. Determine maximum likelihood and least squares estimates of unknown parameters. Be able to define the terms: bias and mean squared error. Determine efficiency w.r.t. the Cramer-Rao lower bound for unbiased estimators.

  6. Determine confidence intervals for means, variances and differences between means.

  7. Concepts of random sampling, statistical inference and sampling distribution, Hypothesis tests. Null and alternative hypotheses, type I and type II errors, test statistic, critical region, level of significance, probability-value and power of a test. Use tables of the t-, F-, and chi-squared distributions.

  8. Use R to implement the methods discussed in (1)-(7), [R Core Team (2017), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/] for the data analysis examples of the module.

Module information

The module follows the Graduate (Level 6) standards in Statistics of the Royal Statistical Society.


Indiciative Syllabus:


Distribution theory



  1. Sums of IID random variables, strong and weak law of large numbers, central limit theorem.

  2. Concept of Monte Carlo simulation.

  3. Joint, marginal and conditional distributions. Independence. Covariance and correlation.

  4. Convolution formula and change of variable formula.

  5. Moment generating functions to find moments of the PDF and distributions of sums of random variables.


Estimation



  1. Sampling distributions.

  2. Bias in estimators and mean squared error, efficiency and the Cramer-Rao lower bound for unbiased estimators.

  3. Maximum likelihood estimation and finding estimators analytically.

  4. The mean and variance of a sample mean.

  5. The distribution of the t-statistic for random samples from a normal distribution. - The F distribution for the ratio of two sample variances from independent samples taken from normal distributions.

  6. Chi Square distributions for the sum of squared standard normal variates


Hypothesis testing and Confidence intervals



  1. Confidence intervals for means, variances and differences between means.

  2. Hypothesis tests concerning means and variances.

  3. Null and alternative hypotheses, type I and type II errors, test statistic, critical region, level of significance, probability-value and power of a test.

  4. Use R to implement methods discussed above.


Linear models



  1. Linear relationships between variables using regression analysis.

  2. The correlation coefficient for bivariate data and the coefficient of determination. Response and explanatory variables and the least squares estimates of the slope and intercept parameters in a simple linear regression model.

  3. Multiple linear regression with IID normal errors, implemented in R.

  4. Use R to implement methods discussed above.

Learning and teaching methods

Teaching in the School will be delivered using a range of face-to-face lectures, classes, and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.

Bibliography

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Test  11/12/2023   
Exam  Main exam: In-Person, Open Book (Restricted), 180 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 180 minutes during January 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 180 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%

Reassessment

Coursework Exam
30% 70%
Module supervisor and teaching staff
Dr Yassir Rabhi, email: yassir.rabhi@essex.ac.uk.
Dr Yassir Rabhi
yassir.rabhi@essex.ac.uk

 

Availability
Yes
No
No
Travel costs for UK - based unpaid, approved work placements and live projects which are an integral part of a module may be covered by your department. (NB this will usually exclude field trips and site visits). Please check with your module supervisor to ensure that the activity is eligible.

External examiner

Dr Murray Pollock
Newcastle University
Director of Statistics / Senior Lecturer
Resources
Available via Moodle
Of 39 hours, 36 (92.3%) hours available to students:
1 hours not recorded due to service coverage or fault;
2 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information

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