|| MA12410 |
|| BASIC DESCRIPTIVE STATISTICS AND PROBABILITY |
|| 2004/2005 |
|| Dr John A Lane |
|| Intended for use in future years |
|Next year offered
|| N/A |
|Next semester offered
|| N/A |
|| A or AS level Mathematics or any of MA12010, MA12510, MA12610 taken at the same time. |
|| May not be taken at the same time as, or after, any of MA10020 to MA11410. |
| Course delivery
|| Lecture || 22 x 1 hour lectures |
|| Seminars / Tutorials || 6 x 1 hour example classes |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Exam||2 Hours ||100%|
|Supplementary Assessment||2 Hours ||100%|
On completion of this module, a student should be able to:
categorise, summarise and interpret various types of data;
explain the concept of probability;
deduce probabilities in a variety of simple symmetric situations;
solve elementary combinatoric problems;
explain the concept of random variable;
explain the concept of probability distribution and density;
describe a variety of standard distributions;
use probability tables or a calculator for finding probabilities associated with a variety of standard distributions;
deduce important `features? of some standard distributions;
model stochastic situations with standard distributions;
explain the concepts of expectation, variance and standard deviation and deduce them in familiar standard and simple unfamiliar situations;
manipulate simple expressions involving expectations;
explain the Central Limit Theorem and its significance;
determine when it is appropriate to approximate a Binomial distribution by a Normal distribution and do this using the `continuity correction?.
This module starts with the descriptive statistics used for summarising and displaying data. It then introduces probability, the mathematical language of uncertainty and discusses the analysis of data in commonly occurring situations.
To introduce students to basic ways of thinking about data. To give students the methodology for summarising and interpreting data. To introduce the basic ideas of probability, random variable, probability distributions, expectation and variance.
1. SUMMARISING DATA: Categories of data, frequency tables, descriptive statistics, histograms, stem and leaf plots, comparing data sets.
2. PROBABILITY: Axioms of probability, deduction in symmetric situations, classical sample space.
3. COMBINATORICS: Basic formulae with applications.
4. THE ALGEBRA OF SETS: Basic formulae with applications.
5. CONDITIONAL PROBABILITY: Definition, the chain rule, Bayes rule, applications.
6. PROBABILITY DISTRIBUTIONS: Discrete and continuous cases, the probability mass function, the density function, calculation of probabilities, distribution functions, standard distributions, use in modelling. Calculation of probabilities using Statistical Tables.
7. EXPECTATION: Definitions of expectation, variance and standard deviation; properties, calculation in specific cases.
8. THE CENTRAL LIMIT THEOREM: Statement, significance, applications, approximation of the Binomial distribution by the Normal distribution.
[Note: concepts and methodology are illustrated throughout by means of a wide variety of specific examples.]
** Recommended Text
N A Weiss (1993) Elementary Statistics
2nd. Addison-Wesley 0201566400
** Supplementary Text
P T Strait (1989) A first course in Probability & Statistics with applications
2nd. Harcourt Brace Jovanovich 0155275232
P G Hoel (1976) Elementary Statistics
4th. John Wiley 0471403024
** Should Be Purchased
J Murdoch & J A Barnes (1998) Statistical tables
4th. Macmillan 0333558596
M R Spiegel (1975) Schaum's Outline of Theory and Problems of Probability & Statistics.
This module is at CQFW Level 4