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2014/15 Undergraduate Module Catalogue

MATH1725 Introduction to Statistics

10 creditsClass Size: 301

Module manager: Dr A Baczkowski

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2014/15

Pre-requisite qualifications

MATH1715 or appropriate A-Level in Mathematics and Statistics, or equivalent.

This module is mutually exclusive with

LUBS1240Maths&Stats For Bus&Ec 1

This module is approved as a discovery module

Module summary

This module builds on the ideas introduced in MATH1715 by focusing on relationships between variables, including techniques for handling 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 course also includes techniques relevant to the analysis of count data and tests concerning proportions.


On completion of this module, students should be able to:
(a) carry out appropriate hypothesis tests on the means of one or two populations
(b) understand and carry out simple least squares linear regression
(c) carry out inference on proportions
(d) carry out chi-squared tests
(e) have knowledge of descriptive statistics
(f) understand simple inference.


1. Populations and samples. Frequency distributions. Histograms.
2. Measures of location. Measures of spread. Interpreting the standard deviation.
3. Statistical Inference. Iid random variables. Point estimation. Sampling distribution of the sample mean. Central limit theorem. Interval estimation. Confidence intervals for mean (variance known and unknown).
4. Hypothesis testing for means. p-values. Tests concerning means. z-test. t-test.
5. Inference for two populations. Two independent samples. Paired samples.
6. Several random variables. Sample covariance and correlation. Continuous bivariate distributions. Properties of expectations, population covariance, correlation. Linear combinations of random variables.
7. Regression. Least squares regression. Inference concerning slope. Computer package for regression.
8. Attribute data. Hypothesis tests for a population proportion. Large sample confidence interval for a population proportion. Comparing two proportions.
9. Chi-squared tests. Single sample classified into two or more groups. Fitting distributions, for example binomial, Poisson, normal. Goodness of fit tests. Contingency tables.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours73.00
Total Contact hours27.00
Total hours (100hr per 10 credits)100.00

Private study

Studying and revising of course material.
Completing of assignments and assessments.

Opportunities for Formative Feedback

Regular problem solving assignments

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

Methods of assessment

Assessment typeNotes% of formal assessment
In-course Assessment.20.00
Total percentage (Assessment Coursework)20.00

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

Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 00 mins80.00
Total percentage (Assessment Exams)80.00

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: 02/02/2015


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