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Elementary Statistics (MATH 10510)

Academic Year:

Contents of this document:

Administrative Information

  1. Unit number and title: MATH 10510 Elementary Statistics
  2. Level: C/4 (Open)
  3. Credit point value: 10 credit points
  4. Year: 12/13
  5. First Given in this form: 1993/94 (but the same material has been given for several years
  6. Unit Organiser: Li Chen
  7. Lecturer: Li Chen
  8. Teaching block: 2
  9. Prerequisites: GCSE grade C or better.

Unit aims

To introduce some basic ideas of statistics as useful tools for science students.

General Description of the Unit

The unit provides a short introduction to the aspects of statistics of most interest and importance to scientists. It will cover the basics of probability, statistical distributions, hypothesis testing, regression etc. No previous statistical knowledge will be assumed.

Relation to Other Units

This is a stand-alone 10cp unit on statistics. The same material is available as part of the 40 credit-point units Mathematics 1AS and Mathematics 1ES and of the 20 credit-point unit Mathematics 1FS.

Teaching Methods

3 lectures per week plus a computer laboratory practical session using Excel and the R programming language. Marked work is returned to the students and difficulties explained in the tutorials. Attendance at the practicals is compulsory. See the section Award of Credit Points below.

Learning Objectives

At the end of the unit students should:

  • have an insight into the value, use and interest of statistical methods in scientific work and thought
  • be able to apply simple statistical methods in their own scientific work, and to understand what they are doing
  • be able to understand the statistical jargon used in scientific papers.

Assessment Methods

To pass the unit your final assessment mark must be 40 or over. This assessment mark will be made up from four practical Statistics assignments:

Assignment 1 gives 20% of the mark.
Assignment 2 gives 25% of the mark.
Assignment 3 gives 25% of the mark.
Assignment 4 gives 30% of the mark.

Each week's assignment is to be handed in the following week that the assignment is set. Assignments handed in late will receive reduced or no marks.

There may be good reasons, such as illness, for handing in work late or not attending the required practical classes: you must provide evidence, such as a doctor's note, in order for marks to be awarded in such cases.

September Examinations

If you fail Elementary Statistics in June, you may (depending on which Faculty you are in and how you have done in your other units) be allowed to resit it in September by taking a practical assessment.

Award of Credit Points

To be awarded the credit points for this unit you must normally pass the unit, i.e. you must achieve an assessment mark of 40 or more.

The assessment mark is calculated as described in the Assessment section above. Details of the university's common criteria for the award of credit points are set out in the Regulations and Code of Practice for Taught Programmes at http://www.bristol.ac.uk/esu/assessment/codeonline.html

For this unit:

  • Attendance at the practical sessions is compulsory. Students who do not attend without providing acceptable reasons will fail the course, without credit points.

Note: we will make allowances for illness and other such good reasons, PROVIDED that you follow the School of Mathematics procedures: you must inform the Undergraduate Student Administrators in Mathematics and submit a completed Extenuating Circumstances form (available from the School) together with supporting written documentation (e.g. a doctor's certificate, specifying the date(s) you were unable to undertake academic work).

Transferable Skills

  • Increased skills in handling data (numeracy skills).
  • IT skills developed through use of R programming language

Texts

Recommended but not required: Gerald Keller, Applied Statistics with Microsoft Excel, published by Duxbury.
You might also find this useful: Bruce E. Trumbo, Learning Statistics with Real Data, Duxbury

Syllabus

Probability:

The use of probability in everyday life and in scientific modelling.
Exploratory methods: plotting data, structure exposed by suitable plots, log-log plots, outliers.

Probability models:

Use of probability to model observed phenomena.
Discrete variables: The Binomial distribution, the Poisson distribution
Continuous variables: The Normal distribution: its uses and misuses.

Inference:

Hypothesis testing and confidence intervals:
What is a p-value? One- and two-sided tests. Standard errors.
One and two sample t-tests, One-way Analysis of Variance.

Regression:

Dependence and independence. Linear regression and correlation. Percentage of variability explained.