Types of Data (Numeric and Categorical)

Context: FIT1043_MOC · the foundation that dictates which chart and which statistic are valid · four basic types

Quick Revision

• 🎯 Objective: classify a variable as numeric (discrete/continuous) or categorical (nominal/ordinal) ➔ this choice drives every downstream method.
• ⚡ Key Constraint: the split is numeric vs categorical first, then enumerable-vs-real / ordered-vs-unordered — a wrong classification picks the wrong chart or statistic.

📝 Core

• Numeric–Discrete ➔ numeric but enumerable (countable) values, e.g. number of live births, age in whole years.
• Numeric–Continuous ➔ numeric, not enumerable (real numbers), e.g. weight, height, distance from CBD.
• Categorical–Nominal ➔ discrete values with no inherent ordering, e.g. country, state, gender.
• Categorical–Ordinal ➔ discrete values with an ordering, e.g. education status, disease-progression stage.

⚠️ Common Mistakes

• 💡 Numbers can be categorical ➔ a coded state (0/1) is nominal, not numeric; don’t average a code.
• 💡 Ordinal ≠ numeric ➔ ordered categories have rank but not meaningful arithmetic (the “gap” between stages isn’t fixed).

🧠 Active Recall

Classify: distance from CBD, education level, gender, number of rooms.

Answer

• Short answer: distance from CBD = numeric-continuous; education level = categorical-ordinal; gender = categorical-nominal; number of rooms = numeric-discrete.
• Why: Enumerable vs real, ordered vs not ➔ real-valued ⇒ continuous; countable ⇒ discrete; ranked labels ⇒ ordinal; unranked labels ⇒ nominal.