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2019/20 Undergraduate Module Catalogue

MATH1712 Probability and Statistics II

10 creditsClass Size: 455

Module manager: Dr Jochen Voss
Email: J.Voss@leeds.ac.uk

Taught: Semester 2 View Timetable

Year running 2019/20

Pre-requisite qualifications

MATH1710 or equivalent.

This module is mutually exclusive with

LUBS1240Maths&Stats For Bus&Ec 1

Module replaces

MATH1725

This module is approved as a discovery module

Module summary

This module extends the ideas introduced in MATH1710, using approximating distributions to estimate quantities of interest in the population using a frequentist approach. We model relationships between variables, including data arising from both related and independent samples. Inferential methods are used to compare the means of two populations, eg to compare the average wages of males and females doing similar occupations. Where two variables are related, the nature and strength of the relationship can be examined by regression procedures. The module also includes techniques relevant to the analysis of count data and tests concerning proportions.

Objectives

On completion of this module, students should be able to:
(a) describe properties of key distributions and understand simple inference
(b) with the aid of a statistical package, carry out appropriate hypothesis tests on the means of one or two populations
(c) understand and carry out simple least squares linear regression
(d) carry out inference on proportions
(e) carry out chi-squared tests

Syllabus

1. Sampling distributions and central limit theorem.
2. Frequentist statistical inference. Iid random variables. Point estimation and interval estimation. Confidence intervals for mean (variance known and unknown).
3. Hypothesis testing for means. p-values. Tests concerning means. z-test. T-test.
4. Inference for two populations. Two independent samples. Paired samples.
5. Several random variables. Sample covariance and correlation. Continuous bivariate distributions. Properties of expectations, population covariance, correlation. Linear combinations of random variables.
6. Regression. Least squares regression. Inference concerning slope.
7. Binary data. Hypothesis tests for a population proportion. Large sample confidence interval for a population proportion. Comparing two proportions.
8. Chi-squared tests. Single sample classified into two or more groups. Fitting distributions, for example binomial, geometric, Poisson, normal. Goodness of fit tests. Contingency tables and test of independence.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture221.0022.00
Practical21.002.00
Tutorial51.005.00
Independent online learning hours10.00
Private study hours61.00
Total Contact hours29.00
Total hours (100hr per 10 credits)100.00

Private study

This will include tutorial exercise sheets, lecture preparation, studying course material, using R for data analysis, revision for exams, preparing for tutorials.

Opportunities for Formative Feedback

Fortnightly tutorials and homework sheets.


!!! In order to pass the module, students must pass the examination. !!!

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
In-course Assessment.30.00
Total percentage (Assessment Coursework)30.00

There is no resit available for the coursework component of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 70.00
Total percentage (Assessment Exams)70.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 13/08/2018

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