Find papers

STT 301

Statistics

TU BHM · Semester 5 · Recent BHM syllabus series

Requirement
required
Credits
3
Study Guide
7 units available

Chapter notes

Authoritative study guide across 7 syllabus units

Open a unit to read its study notes.

Unit 1: Introduction to Statistics and the Role in Tourism· 5 LHs· ~6 marks

Suggest correction

Pass path: Memorize must-know definitions, operational roles, and core terminology of Statistics, Parameter vs. Statistic, and Primary Data. Top path: Master advanced exam requirements: diagram and execute the Classification of Statistical Data in Hospitality Research and accurately contrast Descriptive Statistics vs Inferential Statistics, resolving TU board problem scenarios with complete working steps.

Part 1: Core Concepts (2 Marks)

  • Statistics: The scientific discipline concerned with the collection, organization, presentation, analysis, and interpretation of numerical data to make informed managerial decisions under uncertainty.
  • Parameter vs. Statistic: A parameter is a numerical summary measure describing an entire target population (e.g., mean hotel room rate across all 150 hotels in Kathmandu, μ\mu); a statistic is a numerical summary calculated from a sample subset (e.g., sample mean xˉ\bar{x}).
  • Primary Data: Original raw statistical data collected first-hand by the researcher directly from the primary source for a specific investigation (e.g., guest feedback comment cards).
  • Secondary Data: Statistical data previously gathered, compiled, and published by other agencies for another purpose (e.g., Nepal Tourism Board Annual Arrival Reports).
  • Data Editing & Coding: The rigorous screening of raw survey responses to detect errors, omissions, and ambiguities, followed by assigning numeric codes to qualitative responses for computational analysis.

Part 2: Structural Frameworks (10 Marks)

Classification of Statistical Data in Hospitality Research

DATA TAXONOMY IN TOURISM RESEARCH

MEASUREMENT SCALE DEFINING CHARACTERISTIC HOSPITALITY EXAMPLE
1. NOMINAL SCALE Categorical labels; zero Hotel Department (Kitchen,
(Qualitative) mathematical order or rank. Front Office, Accounts).
2. ORDINAL SCALE Ranked order; relative gaps Hotel Star Rating (3-Star,
(Qualitative) are non-measurable. 4-Star, 5-Star Deluxe).
3. INTERVAL SCALE Ranked with equal intervals; Room Temperature in
(Quantitative) arbitrary zero point. Celsius or Fahrenheit.
4. RATIO SCALE Equal intervals with an Daily Room Revenue (NPR),
(Quantitative) absolute true zero point. Number of Guests Seated.

Part 3: Distinction Tables

Basis of Comparison Descriptive Statistics Inferential Statistics
Primary Purpose Summarizes, organizes, and displays known data points. Draws conclusions, generalizations, and predictions about a population.
Tools Used Tables, bar charts, mean, median, standard deviation. Hypothesis testing (tt-test, zz-test, ANOVA), confidence intervals.
Scope of Findings Limited strictly to the specific observed group. Generalizes beyond the observed sample to the broader population.
Hotel Example Calculating yesterday’s hotel average occupancy percentage (78%). Predicting next year’s foreign tourist arrivals with 95% confidence.

Part 4: Worked Problems & Exam Traps

Population Mean vs. Sample Mean Formulas

Population Mean (μ)=∑i=1NXiN\text{Population Mean } (\mu) = \frac{\sum_{i=1}^N X_i}{N}
Sample Mean (xˉ)=∑i=1nxin\text{Sample Mean } (\bar{x}) = \frac{\sum_{i=1}^n x_i}{n}

TU Exam Trap: On questions distinguishing Data Scales, students frequently misclassify Hotel Star Ratings as Ratio Data because numbers (1 to 5) are used. Star ratings are Ordinal Data because a 4-star hotel is not mathematically “twice as good” as a 2-star hotel!

Students lose marks here because they overlook key operational criteria: on questions distinguishing Data Scales, students frequently misclassify Hotel Star Ratings as Ratio Data because numbers (1 to 5) are used. Star ratings are Ordinal Data because a 4-star hotel is not mathematically “twice as good” as a 2-star hotel.