I’ve expanded the problem sets section of this site to include coursework from my Master’s in Mathematics at Southern Illinois University Edwardsville (SIUe). These five courses represent the core of my graduate statistics training from 2021-2022.
Available Courses
STAT 478 - Time Series Analysis
Spring 2021 - Dr. Beidi
Covers ARIMA models, forecasting, spectral analysis, and state space methods. The problem sets include both theoretical derivations and practical R implementations for analyzing temporal data.
STAT 482 - Regression Analysis
Fall 2022 - Dr. Andrew Neath
A deep dive into linear models, diagnostics, variable selection, and model comparison. Dr. Neath’s approach emphasized understanding the theoretical foundations alongside practical application.
STAT 575 - Computational Statistics
Summer 2021 - Dr. Qiang Beidi
This was perhaps the most hands-on course. Topics included:
- Newton-Raphson and numerical optimization
- Monte Carlo simulation
- Sampling methods (inverse transform, acceptance-rejection)
- Hand-coded MLE for Poisson regression
Implementing these algorithms from scratch, rather than relying on library functions, provided a much deeper understanding of how statistical methods actually work.
STAT 579 - Discrete Multivariate Analysis
Spring 2021 - Dr. Andrew Neath
Analysis of categorical data, log-linear models, and contingency tables. The coursework covers both the mathematical theory and R implementations for analyzing discrete multivariate data.
STAT 581 - Statistical Methods
Fall 2021 - Dr. Neath
A comprehensive methods course covering experimental design, ANOVA, and general linear models with practical applications.
Why Share This?
A few reasons:
Learning resource: Graduate-level statistics coursework with worked solutions can be hard to find. Maybe someone studying similar material will find these useful.
Personal archive: I completed much of this work during cancer treatment. Having it organized and accessible feels like preserving something meaningful from that period.
Reference material: I occasionally refer back to these derivations and implementations in my own research work.
Format
Each course section includes:
- Problem set PDFs (original assignments)
- My solutions with full derivations
- R code implementations
- Exam solutions where available
The solutions show complete working, not just final answers. When I implemented numerical methods, I included verification against built-in R functions to confirm correctness.
Acknowledgments
I’m grateful to the faculty in SIUe’s Department of Mathematics and Statistics, particularly Dr. Andrew Neath and Dr. Qiang Beidi, for their excellent instruction during my time in the program.
Browse the complete collection at /probsets.
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