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Statistics 1 (Math 11400) Home page

This page provides links to a series of information pages that supplement the unit description page for the Level 1 Statistics 1 unit (MATH 11400).


  • Lectures 17, 18 and 19 will be in the usual time slots on 10, 12 and 17 May. Lecture 19 (on 17 May) is the last of the unit. I will also come to LT3 at the usual time on Thursday 19 May, when to help with revision I will present a very short overview of the course (with no new material) and then answer questions - to make the best of this, please come prepared. The exam is on Friday 27 May.
  • You will have received an email to take part in the National Student Survey which is an annual survey of undergraduates across the UK. It's quick to complete and you'll be helping prospective students make the right choices of where and what to study. Bristol will receive the anonymised data to help us identify areas where we are doing well, as well as areas for improvement so we can improve the student learning experience for future generations of students. Please complete the survey and consider your responses carefully to ensure that the data is representative of your experience.
  • My office hour for this unit (mainly for 2nd/3rd/4th years) will be 2-3pm each Tuesday (starting 15 February), room 4.11, School of Mathematics

Reading List

The recommended textbook for the unit is:

  • Mathematical statistics and data analysis by JA Rice

The book by Rice covers both the Probability and the Statistics material and will probably cover some of the second year Statistics unit as well. It is particularly good at combining modern ideas of data analysis, using graphical and computational techniques, with more traditional approaches to mathematical statistics.

The statistical package R will frequently be used to illustrate ideas in lectures and you will be expected to use it for set work. Although the examination will not require writing R code, you may be expected to understand and interpret examples of R output, similar to those seen in the unit. The notes and handouts should provide sufficient information, but a good introductory text for further reading is:

  • Introductory Statistics with R by Peter Dalgaard

It will be particularly useful for students who intend to continue studying statistics in their second, third (and fourth) year.

Please note that the use of statistical tables will no longer be taught in the unit, and they will not be needed or provided in the examination.

There is also a linked list of books in the Library which you may find useful as alternatives to the formal recommended text books.

Statistical Computing using R

In addition to the book by Dalgaard above, there is also a handout to help you start using R in the Mathematics computing lab and a self-learning tutorial in R if you want to learn at your own pace.

Using R in the Mathematics computing lab
R: A Self-learn tutorial

If you would like to use R on your own computer, you can download an appropriate version of the package from here.

If you want to access the data sets for the unit on your own computer, a copy can be downloaded from here: stats1.RData. Put this file on your desktop or in a suitable folder, and double-click on it to start up R and load the data sets. If you are unsure, unclear or confused about the format of individual data sets in the stats1.RData workspace, please consult this note.

For Windows users, an older version of the Using R handout is still available. Note that the section on accessing the Statistics 1 data sets in this older document no longer applies.

Section Web Pages

Each section page will contain some or all of the following:
  • The aims and objectives for the section
  • A brief guide to relevant material in the recommended text book
  • (In due course) links to printable copies of the Handout, Problem Sheet and Solution Sheet for the section.
  • A list of the questions set for handing in that week.
  • Links to web pages which contain interesting material, either related to the section or related to the subject in general.
Section 1 Exploratory Data Analysis & Computing in R Lectures 1&2 (week 1)
Section 2 Parametric models, Method of moments estimation & Assessment of fit Lectures 3-5 (weeks 2&3)
Section 3 Maximum likelihood estimation Lectures 6&7 (week 4)
Section 4 Linear regression & Least squares estimation Lectures 8&9 (week 5)
Section 5 Sampling variation: (a) Simulation based methods, (b) Central Limit Theorem Lectures 10&11 (week 6)
Section 6 Exact sampling distributions related to the Normal distribution Lectures 12&13 (weeks 7&9)
Section 7 Confidence intervals Lectures 14&15 (weeks 9&10)
Section 8 Hypothesis testing Lectures 16&17 (weeks 10&11)
Section 9 Comparisons and regression Lectures 17-19 (weeks 11&12)

Copyright notice

© University of Bristol 2011

All material in these pages is copyright of the University unless explicitly stated otherwise. It is provided exclusively for educational purposes at the University and is to be downloaded or copied for your private study only, and not for distribution to anyone else.

Please also note that material from previous years' delivery of this unit is not necessarily a reliable indicator of what will be covered or examined this year.

Professor Peter Green, School of Mathematics, University of Bristol, Bristol, BS8 1TW, UK.
Email link Telephone: +44 (0)117 928 7967; Fax: +44 (0)117 928 7999
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