# Theo Pepler

Research | Teaching | Software | Links and resources | HOWTO's# # Teaching

### STUDENT SUPERVISION

**Assel Akhmetova**- PhD (Veterinary Epidemiology)

**Danielle Cowie**- MSc (Genetics)

### COURSES TAUGHT

**Scientific Methodology and Statistics (Veterinary Biosciences)**

Introduction to the R programming language; descriptive statistics; probability distributions; experimental design; hypothesis testing; correlation, linear regression and ANOVA; GLMs; nonparametric tests; categorical data analysis; Bayesian analysis.

**Biometry 212 - Introductory Biometry**

Methods of tabulation and graphical representation of data; descriptive measures of locality, variation and association; simple linear regression; the elementary principles of randomness, distributions, sampling and estimation; contingency tables and chi-square tests; calculation of standard errors;

*F*-test for heterogeneity of variance.

**Biometry 242 - Applications in Biometry**

Treatment and experimental design; efficiency of estimation; analysis of variance; hypothesis tests for means and differences between means:

*F*-test,

*t*-test, Fisher's LSD; confidence intervals; non-parametric tests; multiple linear regression.

**Biometry 771 - Postgraduate Biometry**

Data processing with SAS Enterprise Guide (or alternatively: R). Simple descriptive statistics;

*t*-tests for single populations, combined

*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; 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; analysis of covariance analysis; categorical data analyses (chi-square tests); logistic regression.

**Biology 372 (Statistics component)**

Introduction to modeling; comparison of two groups ; paired

*t*-test. Comparison of two or more groups: ANOVA; test for homoscedasticity. Two-way ANOVA; multiple comparisons. Factorial treatment design. Simple linear regression, testing of the assumptions. Non-linear data, transformations, fitting of other functions. Multiple linear regression.

**Statistics 186 - Introduction to Statistics**

*Linear programming*: Graphical techniques to solve problems with two variables; Shadow prices; Sensitivity analyses.

*Sampling techniques*: Simple random; Stratified; Systematic; Cluster; Probability proportional to size.

*Descriptive Statistics*: Various data types; Stem-and-leaf representations; Frequency distributions; Graphical representation of data (histograms, polygons, bar and pie charts); Descriptive measures of location, spread and association (mean, median, mode, percentiles, variance, standard deviation, coefficient of correlation); Box plots.

*Probability theory*: Basic probability concepts (sample spaces, events, addition and multiplication rules, conditional probabilities, probability trees, contingency tables); Bayes' theorem; Counting rules.

*Discrete random variables and probability distributions*: Expected value, variance and standard deviation of a discrete random variable; Covariance between discrete random variables; Portfolio management; Binomial and hypergeometric distributions.

*Basic calculus*: Introduction to differentiation and integration with simple applications. Continuous random variables and probability distributions: Expected value, variance and standard deviation of a continuous random variable; Normal distribution.

*Sampling distributions*: Central limit theorem; Sampling distributions of the mean, a proportion and the variance; Sampling distribution of the difference between two means.

*Inferential Statistics*: Interval estimation and hypothesis testing for the mean, a proportion, the variance and the standard deviation; Interval estimation and hypothesis testing for the difference between two means and the ratio of two variances; Applications of interval estimation in auditing.

*Regression analysis*: The simple linear regression model; The method of least squares estimation; Inference on the model parameters and coefficient of correlation; Residual analysis.

**Theory of Interest 152**

Simple and compound interest. Force of interest. Future value, present value and discount. Accumulation and discounting of amounts of money. Various types of annuities and applications.

**Data Management 111**

Introductory statistics module offered to students in the health sciences.