ST311: Introduction to Statistics – Examines relationships between two variables using graphical techniques, simple linear regression and correlation methods. Producing data using experiment design and sampling. Elementary probability and the basic notions of statistical inference including confidence interval estimation and tests of hypotheses. One and two sample t-tests, one-way analysis of variance, inference for count data and regression. 

ST312: Introduction to Statistics II – A further examination of statistics and data analysis. Inference for comparing multiple samples, experimental design, analysis of variance and post-hoc tests. Inference for correlation, simple regression, multiple regression and curvilinear regression. Analysis of contingency tables and categorical data.

ST350: Economics and Business Statistics – Introduction to statistics applied to management, accounting, and economic problems. Emphasis on statistical estimation, inference, simple and multiple regression, and analysis of variance. Use of software to apply statistical methods to problems encountered in management and economics.

ST370: Probability and Statistics for Engineers – A calculus based introduction to probability and statistics, with a focus on collection and summary of data, along with making formal inferences and practical conclusions on the basis of data. Topics include sampling, descriptive statistics, designed experiments, simple and multiple regression, basic probability, discrete and continuous distributions, sampling distributions, hypothesis testing, confidence intervals and one and two-way analysis of variance.

ST511: Statistical Methods for Researchers I – Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correction, chi-square. 

ST513: Statistics for Management and Social Sciences I – Introduction to important topics about collecting high quality data and summarizing that data appropriately both numerically and graphically. Exploration of using probability distributions to model data. Estimation of parameters and properties of estimators. Construction and interpretation of confidence intervals and hypothesis tests. Software is used throughout to conduct the desired statistical analysis.