Biometry

212 (8) Introductory Biometry (2L, 1T / 1P)

Role of statistics in research; methods of tabulation and graphical representation of data; descriptive measures of locality, variation and association; the elementary principles of estimation, sampling, randomization, unbiasedness and distributions; simple linear and non-linear regression; calcualtion of standard errors; introduction to hypothesis testing; contingency tables and chi-square tests; tests for normality; F-test for homogeneity of variance. All data will be anlaysed using applicable software.

Flexible assessment
P Mathematics (Bio) 124 or Mathematics 114

Module co-ordinator: Mr S van der Westhuizen

Lecturers: Mr S van der Westhuizen and Ms Samantha Joao

242 (8) Applications in Biometry (2L, 1T / 1P)

Treatment and experimental design; efficiency of estimation; analysis of variance; hypothesis tests for means and differences between means: F-test, t-test, Student’s LSD; confidence intervals; non-parametric tests; multiple linear regression. All data will be analysed using applicable software.

Flexible assessment
P Biometry 212

Module co-ordinator: Mr S van der Westhuizen

Lecturers: Mr S van der Westhuizen and Ms Samantha Joao

311 (8) Advanced regression and ANOVA (1L, 1P, 1T)

Matrix algebra; generalized linear models; power analysis; simple- and multiple linear regression; polynomial regression; logistical regression; diagnostic tests for influential observations; analysis of covariance; testing model assumptions.

Flexible assessment
P Biometry 242

Module Co-ordinator: Mr S van der Westhuizen

Lecturer: Mr S van der Westhuizen

711 (8) and 811 (8) Biometrical applications and data analysis in SAS

Data processing and graphical procedures with SAS Enterprise Guide. Simple descriptive statistics; t-tests for single populations, independent samples t-tests and paired t-tests for two populations; analysis of variance: completely random design, random-blocks design, Latin-square design, cross-classification designs; repeated-measures analysis of variance; multiple comparison procedures. Power analysis. Non-parametric tests: Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman; linear regression and correlation; polynomial regression, multiple regression; selection of independent variables with stepwise regression and all-subset regression; covariance analysis; categorical data analyses (Chi-squared tests); logistic regression. This module is presented in two blocks of five half days each in the first semester.

Flexible assessment.
P Biometry 212 and 242, 211. Students with different undergraduate Statistics modules must obtain at least 50% for an admission examination.

Module co-ordinator and lecturer: Ms Samantha Joao

721 (8) and 821 (8) Biometrical applications and data analysis in R

Data processing and graphical procedures with R. Simple descriptive statistics; t-tests for single populations, independent samples t-tests and paired t-tests for two populations; analysis of variance: completely random design, random-blocks design, Latin-square design, cross-classification designs; repeated-measures analysis of variance; multiple comparison procedures. Power analysis. Non-parametric tests: Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman; linear regression and correlation; polynomial regression, multiple regression; selection of independent variables with stepwise regression and all-subset regression; covariance analysis; categorical data analyses (Chi-squared tests); logistic regression. This module is presented in two blocks of five half days each in the first semester.