Mana Azizsoltani
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UNLV syllabus

HOA 730: Statistical Analysis for Hospitality

Spring 2026

InstructorMana Azizsoltani, PhD

mana.azizsoltani@unlv.edu

MeetingMondays, 2:30 PM - 5:15 PM

HOS 234

Credits3 credits

Graduate standing or department approval

Description

Course description

HOA 730 introduces the concepts and techniques of statistical analysis used in hospitality, tourism, and leisure research. Students describe and explore data, conduct and interpret statistical inference, analyze real data reproducibly in R, and communicate findings clearly to academic and professional audiences.

Objectives

What students should be able to do

  1. Explain foundational concepts in probability, sampling, descriptive statistics, and statistical inference, and select useful numerical and graphical summaries for a dataset.
  2. Translate hospitality and leisure research questions into testable statistical questions and select methods that fit the study design and variables involved.
  3. Construct and interpret confidence intervals and conduct hypothesis tests for means and proportions.
  4. Conduct and interpret analysis of variance, chi-square tests, correlation, simple linear regression, and multiple linear regression.
  5. Use R and RStudio to manage, summarize, visualize, and analyze data reproducibly while evaluating assumptions and diagnosing common statistical problems.
  6. Interpret statistical evidence critically and communicate results accurately without overstating causal or practical conclusions.

Evaluation

Assignments and grading

TaskWeightDue
Professionalism and participation10%Throughout the semester
Homework and in-class assignments30%Across five applied modules
Midterm exam/project20%Midsemester
Applied take-home final project30%Final week
Online multiple-choice final quiz10%Final week

Schedule

Weekly teaching schedule

WeekTopic
Jan 19Martin Luther King Jr. Day Recess
Jan 26Course introduction; R and RStudio; importing, summarizing, and visualizing data
Feb 2Casino games, elementary probability, random variables, and probability distributions
Feb 9Sampling distributions, standard error, and the central limit theorem
Feb 16Presidents’ Day Recess
Feb 23Confidence intervals for means, proportions, and differences between groups
Mar 2Hypothesis tests, p-values, and practical versus statistical significance
Mar 9Analysis of variance, the F test, assumptions, and follow-up comparisons
Mar 16Spring Break Recess
Mar 23Correlation, simple linear regression, prediction, residuals, and model fit
Mar 30Multiple linear regression, indicator variables, model comparison, and prediction
Apr 6Multicollinearity, variance inflation factors, and correlated predictors
Apr 13Multiple-regression diagnostics and model limitations
Apr 20Model selection, chi-square tests, goodness of fit, and independence
Apr 27Study week, project support, interpretation, and reporting workshop
May 4Course synthesis and communicating results

Approach

How the course was taught

The course combined explanation, discussion, hands-on R work, written-in-class assignments, and projects using recognizable hospitality and business settings.