Courses Catalogue

Biometry

COURSE CODE: ENV9203
COURSE CREDIT UNIT: 4
ACADEMIC PROGRAMME: Environmental Science, PhD
COLLEGE/SCHOOL/FACULTY: School of Engineering and Applied Sciences
STATUS: Core
PROGRAMME TYPE: Postgraduate

Course Description

This course introduces commonly used statistical methods. The intent of this course is to provide an understanding of statistical techniques and guidance on the appropriate use of methodologies. The course covers the mathematical foundations of common methods as an aid toward understanding both the types of applications that are appropriate and the limits of the methods. MATLAB and statistical software are used so students can apply statistical methodology to practical problems in the workplace. Topics include the basic laws of probability and descriptive statistics, conditional probability, random variables, expectation and variance, discrete and continuous probability models, bivariate distributions and covariance, sampling distributions, hypothesis testing, method of moments and maximum likelihood point (MLE) estimation, confidence intervals, contingency tables, analysis of variance (ANOVA), and linear regression modeling. All these would enable students to carry out data analysis using various computer packages to solve problems in science and engineering.


COURSE JUSTIFICATION/RATIONALE
This subject aims to give students an appreciation of the decisions to be made in designing field studies or experiments. The focus is on the knowledge, understanding and sound practical skills required for the application of analysis techniques of general use in ecology and environmental science. Practical exercises are built around the statistical package SAS, which is widely used in commerce, industry and educational institutions.

LEARNING OBJECTIVES
By the end of this course, the student should be able to:

  • Solve basic probability problems including finding properties of distribution functions
  • analyze and Interpret data using statistical computer packages e.g. STATA, SAS, SPSS, MINITAB and EXCEL.
  • draw and interpret graphs
  • transform raw data into meaningful and useful information
  • test for ‘cause and effect’ relationship between two variables
  • test for differences and relationships between variables.
  • Solve and interpret simple regression models

LEARNING OUTCOMES

A doctoral student completing the course is expected to demonstrate

  • knowledge of statistics and its application
  • knowledge of the decisions needed to design, execute, analyse and report on an ecological study
  • the ability to interpret results of analyses
  • professional attitudes to study design and analysis
  • understanding of sampling designs (e.g., simple random sampling, stratified
  • sampling) and experimental designs (e.g., completely randomised, randomised block)
  • statistical analyses using ANOVA and other techniques
  • understanding of interactions in two-way ANOVA writing lab reports in which data are analysed and computer output is interpreted