OS3113 Data Analysis for HSI and MOVES

Introduction to common types of data collection (sampling methods, surveys, observational studies, and experiments) and the link between data collection methods and data analytic procedures. Non-calculus based introduction to conducting statistical inference for estimation of population parameters and hypothesis testing with common parametric methods (confidence intervals, z-tests, t-tests, ANOVA, regression, chi-square). Data sets and motivational examples are drawn from recent research relevant to HSI and/or MOVES. Prerequisites: None.

Lecture Hours


Lab Hours


Course Learning Outcomes

·       Advanced Probability Concepts: Understand and apply advanced probability theories, including conditional probability, Bayes' Theorem, and probabilistic models.

·       Hypothesis Testing Proficiency: Demonstrate the ability to conduct and interpret various hypothesis tests, such as t-tests, chi-square tests, ANOVA, and non-parametric tests.

·       Regression Analysis: Gain proficiency in linear and non-linear regression analysis, including understanding assumptions, interpreting coefficients, and assessing model fit.

·       Multivariate Statistical Methods: Learn and apply multivariate statistical techniques such as multiple regression, factor analysis, and cluster analysis.

·       Real-world Data Application: Apply statistical methods to real-world datasets, demonstrating the ability to handle, analyze, and draw conclusions from actual data.

·       Critical Thinking and Interpretation: Enhance critical thinking skills to critically analyze statistical results and their implications in real-world contexts.

·       Communication of Statistical Findings: Develop the ability to effectively communicate statistical findings, both in writing and orally, to a non-technical audience.

·       Advanced Data Visualization Techniques: Learn and apply advanced data visualization techniques to effectively present data and statistical findings.