Theo Pepler

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# 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.