STATISTICS

Credit Points

20

Course Coordinator

Dr I J Wilson

Pre-requisites

S or GCSE in Mathematics or TS 1001.

Overview

Basic data handling, summarisation and visualisation – graphical displays, tabulation, cleaning data, presentation. Elements of sampling strategies. A brief introduction to probability distributions. The concepts of confidence intervals and hypothesis testing with simple examples. Relationships – correlation and regression, the idea of a statistical model. Simple time series analysis.
12 week course – 3 one-hour lectures, 1 hour tutorial and 1 one-hour computer practical per week.
1 two-hour written examination (70%) and continuous assessment (30%).

Credit Points

15

Course Coordinator

Professor I T Jolliffe

Pre-requisites

MA 1002 or both MA 1004 and MA 1504.

Notes

This course requires no previous knowledge of Statistics.

Overview

Exploratory data analysis. Random variables, expectation, mean and variance. Basic probability theory. Conditional probabilities, independence and Bayes' theorem. Binomial and Poisson distributions. Continuous random variables. Normal and other distributions. The chi-squared, t and F distributions. Sums of random variables. Random functions associated with the Normal distribution, and the Central Limit Theorem. Maximum likelihood estimators. Confidence intervals for proportions, means and variances. Tests about proportions, means and variances. Goodness of fit tests and contingency tables.
12 week course - 2 one-hour lectures per week, 1 one-hour lecture per fortnight and 1 one-hour tutorial per week).
1 two-hour written examination (80%) and continuous assessment (20%).

Credit Points

15

Course Coordinator

Dr W F Scott

Pre-requisites

ST1505 or ST 2003.

Overview

The course covers common multivariate data analysis methods in conjunction with the statistical computer package SPSS.
Introduction to computing and SPSS, Correlation and regression. Regression with two or more variables and hypothesis testing. Stepwise regression. Dummy variables. Multicollinearity. Weighted regression. Designs. Interaction. Treatment comparisons. Orthogonal and linear contrasts. Latin squares. Factorial experiments. Survival analysis; Kaplan-Meier curvues; the log-rank test. Fractional factorials and incomplete blocks. Split-plot designs. Discriminant analysis, variable selection methods.
12 week course - 2 one-hour lectures per week, 1 one-hour lecture per fortnight, 1 one-hour tutorial per fortnight and 1 one-hour laboratory session per week.
1 two-hour written examination (70%) and continuous assessment (30%).