Roel F. Ceballos, PhD
Associate Professor | Research Consultant | Biostatistician | Data Scientist
Roel F. Ceballos, PhD
Associate Professor | Research Consultant | Biostatistician | Data Scientist
Email: roel.ceballos@usep.edu.ph
Email: roel.ceballos@usep.edu.ph
This course focuses on the effective communication and practical application of statistical methods in consulting and collaborative research settings. It develops students’ skills in translating real-world problems into statistical frameworks, interacting with clients or stakeholders, and communicating results to both technical and non-technical audiences. Students will engage in case-based learning and consulting simulations, culminating in a client-oriented statistical report and presentation. The course prepares students for professional statistical practice in academic, industry, government, and policy environments.
This course introduces Bayesian statistical methods for data analysis and inference. It emphasizes probability as a measure of uncertainty, prior specification, likelihood-based modeling, and posterior inference. Topics include Bayes’ theorem, conjugate priors, Bayesian estimation and hypothesis assessment, Bayesian regression, and model comparison. Computational methods such as simulation and Markov chain Monte Carlo (MCMC) are introduced to support practical Bayesian analysis. Students will apply Bayesian methods to real data, interpret results in applied contexts, and critically evaluate Bayesian analyses in statistical literature. The course prepares students for advanced statistical modeling, research applications, and data-driven decision-making in academic, industry, and policy settings.
This course introduces statistical methods and modeling techniques used to assess and evaluate the impact of programs, policies, and interventions, with emphasis on causal inference and evidence-based decision-making. It covers experimental and quasi-experimental designs to address issues such as selection bias and confounding. Students gain applied experience in analyzing real-world data, interpreting results, and communicating findings to policymakers and stakeholders, enabling them to critically assess impact studies and support data-driven program and policy improvement.
This course serves as the culminating academic requirement for undergraduate students in the BS Statistics program. It provides an opportunity for students to independently design and execute a research study that demonstrates their ability to apply statistical theories, methods, and tools in solving real-world problems.
This course introduces the fundamental concepts, principles, and practices of conducting statistical research. It covers the entire research process—from formulating research questions and hypotheses, designing studies, collecting and managing data, to analyzing and interpreting results. Emphasis is placed on ethical research practices, survey and experimental design, proposal writing, and critical appraisal of statistical literature. Students will develop a research proposal using appropriate statistical tools. The course prepares students for independent or collaborative research in academic, industry, or policy settings.
This course introduces students to the fundamental principles and techniques of descriptive statistics, providing a strong foundation for data-driven inquiry. Topics include basic statistical concepts, methods of data collection, and sampling techniques. Emphasis is placed on the processing, organization, and presentation of data using textual, tabular, and graphical methods. Students will compute and interpret various statistical measures including central tendency, location (e.g., percentiles and deciles), dispersion, skewness, and kurtosis. The course also covers exploratory data analysis (EDA), measures of association and correlation, and the use of rates, ratios, proportions, percentages, index numbers, and official statistical indicators. Through practical exercises and the use of statistical software, students will gain competence in summarizing and describing data for informed decision-making.
This course introduces statistical methods and modeling techniques used to assess and manage environmental risks. Emphasis is placed on identifying environmental hazards, analyzing vulnerabilities and exposures, quantifying risks using probabilistic approaches, and evaluating policy responses. Real-world case studies and hands-on data analysis will be central to this course.
This course explores the nature of mathematics as an art and as a tool for understanding the world. It highlights the practical, intellectual, and aesthetic dimensions of mathematics and its role in modern society. Topics include the language and logic of mathematics, problem-solving and reasoning strategies, patterns and numbers, financial mathematics, linear and exponential models, basic statistics and probability, and the mathematics of voting and social choice. The course emphasizes critical thinking, quantitative literacy, and real-life applications, equipping students with the mathematical tools necessary to make informed decisions as responsible citizens in a data-driven world.
This course serves as the culminating academic requirement for undergraduate students in the BS Statistics program. It provides an opportunity for students to independently design and execute a research study that demonstrates their ability to apply statistical theories, methods, and tools in solving real-world problems.