mana.azizsoltani@unlv.edu
UNLV syllabus
HOA 732: Advanced Statistics in R for Hospitality and Business
Fall 2026
Web-based
HOA 730 or equivalent preparation in applied statistics and R
Description
Course description
HOA 732 builds on HOA 730 by introducing advanced statistical learning and machine-learning methods for hospitality and business problems. Students use R and RStudio for classification, regression, ensemble, dimension-reduction, and unsupervised learning methods while evaluating model performance and communicating findings to research and professional audiences.
Objectives
What students should be able to do
- Distinguish between statistical learning for inference and machine learning for prediction, and translate hospitality and business questions into appropriate analytical problems.
- Prepare data for modeling and create reproducible analysis workflows in R and RStudio.
- Fit and interpret logistic regression and other generalized linear models.
- Build and compare decision trees, random forests, support vector machines, boosting models, and neural networks.
- Apply dimension-reduction, classification, and clustering methods, including principal components analysis and linear discriminant analysis.
- Evaluate model performance, explain limitations and uncertainty, and communicate results clearly to technical and nontechnical audiences.
Evaluation
Assignments and grading
TaskWeightDue
Professionalism and participation10%Throughout the course
Homework and in-class assignments20%Across the eight modules
Midterm project30%Week 3
Final project40%Week 8
Schedule
Weekly teaching schedule
WeekTopic
1 · Oct. 19-25R setup and review of visualization, inference, multiple regression, and multicollinearity
2 · Oct. 26-Nov. 1Binary logistic regression, decision-tree classifiers, and classification metrics
3 · Nov. 2-8Random forests, support vector machines, resampling, and the midterm project
4 · Nov. 9-15Regression trees, random-forest regression, and predictive performance
5 · Nov. 16-22Support-vector regression, neural networks, model tuning, and evaluation
6 · Nov. 23-29Boosting for classification and regression
7 · Nov. 30-Dec. 6Principal components, dimension reduction, and time series as time permits
8 · Dec. 7-12Clustering, linear discriminant analysis, synthesis, and the final project
Assessment design
What students were asked to do
The Fall 2026 course balances regular practice with two larger applied projects and expects students to explain—not merely produce—model output.
- Prepare data and build reproducible modeling workflows in R.
- Frame and compare classification and regression approaches.
- Evaluate out-of-sample performance using appropriate metrics.
- Justify model choices, interpret uncertainty, and explain limitations.
- Communicate findings clearly to research and professional audiences.