Distinguish between statistical learning for inference and machine learning for prediction, and translate hospitality and business questions into appropriate analytical problems.
HOA 732: Advanced Statistics in R for Hospitality and Business
An eight-week graduate course using R to connect statistical learning, machine learning, model evaluation, and applied hospitality and business decisions.
Course description
Course description
An advanced statistics course for hospitality graduate students, emphasizing applied modeling, interpretation, and quantitative methods that support hospitality research. The course builds toward more independent analysis and clearer communication of technical findings.
Students move from a focused review of inference and multiple regression into logistic regression, decision trees, random forests, support vector machines, boosting, neural networks, principal components, clustering, and linear discriminant analysis. The course emphasizes honest out-of-sample evaluation, interpretation, and reproducible communication rather than prediction accuracy alone.
Learning objectives
Learning objectives
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.
Assessment
Assignments, grading, and scale
Tap a section of the chart
Choose a slice to see its weight and due date.
A93% and above
A-90% to 92.9%
B+87% to 89.9%
B83% to 86.9%
B-80% to 82.9%
C+77% to 79.9%
C73% to 76.9%
C-70% to 72.9%
D60% to 69.9%
FBelow 60%
Schedule
Semester calendar
October
202627
28
29
30
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24
Course introduction; R and RStudio setup; review of visualization, inference, multiple regression, and multicollinearity
25
26
27
28
29
30
31
Classification, binary logistic regression, decision-tree classifiers, confusion matrices, and classification metrics
November
20261
2
3
4
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6
7
Random-forest and support-vector classifiers; resampling and out-of-sample evaluation
8
9
10
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13
14
Regression trees, random-forest regression, and comparing predictive performance
15
16
17
18
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20
21
Support-vector regression, neural networks, model tuning, and evaluation
22
23
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25
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28
Boosting for classification and regression; learning rates, tree depth, and tuning
29
30
1
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3
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5
Principal components, dimension reduction, and time-series methods as time permits
December
202629
30
1
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7
8
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10
11
12
Clustering, linear discriminant analysis, course synthesis, and model comparison
13
14
15
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20
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26
27
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31
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Materials
Lectures, assignments, and code
Lectures
Lectures and class sessions
A session-by-session home for lecture videos, slide decks, and transcripts as they become available.
AssignmentsAssignments and projects
Homework assignments, activities, and major projects collected in one place.
CodeCode and datasets
Course code, datasets, and the GitHub repository for reproducible examples.