Explain foundational concepts in probability, sampling, descriptive statistics, and statistical inference, and select useful numerical and graphical summaries for a dataset.
HOA 730: Statistical Analysis for Hospitality
A graduate hospitality statistics course that builds practical confidence with R, statistical inference, regression, and evidence-based decision-making.
Course description
Course description
A graduate-level applied statistics course for hospitality students, covering data summarization, probability, statistical inference, ANOVA, regression, diagnostics, and chi-square analysis through R.
This course introduces statistical analysis for hospitality, tourism, and leisure research. Students describe and explore real data in R, conduct statistical inference, examine the assumptions behind each method, and communicate credible conclusions to academic and professional audiences.
Learning objectives
Learning objectives
Translate hospitality and leisure research questions into testable statistical questions and select methods that fit the study design and variables involved.
Construct and interpret confidence intervals and conduct hypothesis tests for means and proportions.
Conduct and interpret analysis of variance, chi-square tests, correlation, simple linear regression, and multiple linear regression.
Use R and RStudio to manage, summarize, visualize, and analyze data reproducibly while evaluating assumptions and diagnosing common statistical problems.
Interpret statistical evidence critically and communicate results accurately without overstating causal or practical conclusions.
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
January
202628
29
30
31
1
2
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4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Martin Luther King Jr. Day Recess
25
26
27
28
29
30
31
Course introduction; R and RStudio; importing, summarizing, and visualizing data
February
20261
2
3
4
5
6
7
Casino games, elementary probability, random variables, and probability distributions
8
9
10
11
12
13
14
Sampling distributions, standard error, and the central limit theorem
15
16
17
18
19
20
21
Presidents’ Day Recess
22
23
24
25
26
27
28
Confidence intervals for means, proportions, and differences between groups
March
20261
2
3
4
5
6
7
Hypothesis tests, p-values, and practical versus statistical significance
8
9
10
11
12
13
14
Analysis of variance, the F test, assumptions, and follow-up comparisons
15
16
17
18
19
20
21
Spring Break Recess
22
23
24
25
26
27
28
Correlation, simple linear regression, prediction, residuals, and model fit
29
30
31
1
2
3
4
Multiple linear regression, indicator variables, model comparison, and prediction
April
202629
30
31
1
2
3
4
5
6
7
8
9
10
11
Multicollinearity, variance inflation factors, and correlated predictors
12
13
14
15
16
17
18
Multiple-regression diagnostics and model limitations
19
20
21
22
23
24
25
Model selection, chi-square tests, goodness of fit, and independence
26
27
28
29
30
1
2
Study week, project support, interpretation, and reporting workshop
May
202626
27
28
29
30
1
2
3
4
5
6
7
8
9
Course synthesis and communicating results
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
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6
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.