Quiz 2
Registry Synced

Statistics I · Week 2 — Categorical data

1021 words
5 min read
2026-08-16

Reading compass

Now · Week map

Week 2 — categorical data

Quiz 2 scope: Weeks 1–8 per IITM May 2026 foundation courses. Source baseline: IITM BS admissions important-dates calendar · May 2026 cycle. Times on assessments are operational conventions — verify hall ticket.

Week map

Categories → counts → relative frequency → bar/pie chart choice

Classify → Represent → Execute → Trap-check

  • Recognize: Ask: How compute relative frequency?
  • Procedure: Tally each category. Total n is sum of counts. Relative freq = count/n.
  • Variations / traps: Watch for: Percents not summing to 100 due to rounding.

Formula chain (compressed)

frequency table → relative freq = count/n → bar/pie for categories.
  1. Frequencycount per category — raw tallies
  2. Relative frequencycount / total — proportions sum to 1
  3. Modemost frequent category — categorical centre
  4. Bar chartheight = count or % — compare categories
  5. Missing categoryinclude "other" or NA — complete sample space

Deep study

Statistics I · Week 2 — Categorical data

Once variables are categories, analysis is counting and comparing shares — tables and charts, not means.

Week map

Categories → frequency table → relative frequency → percent → bar chart → pie chart → complements → rounding totals.

Frequency notation

  • Frequency ff → count in category → 23 students chose tea.
  • Relative frequencyf/nf / n → proportion of whole → 0.23 if n=100n=100.
  • Percent → relative × 100 → 23%.
  • Complement → all categories not A → if A is 30%, complement is 70%.
Constraint: relative frequencies sum to 1 (percents sum to 100%) except rounding drift.
Mini-table: colors Red 40, Blue 35, Green 25, n=100n=100.
  • Rel freq: 0.40, 0.35, 0.25
  • Percents: 40%, 35%, 25%

Charts

Bar chart

  • Categories on axis (nominal or ordinal).
  • Bar height = count or percent.
  • Good for compare across categories.

Pie chart

  • Wedges show part-whole.
  • Works for few categories; weak for many thin slices.
  • Angles proportional to relative frequency.
Trap: pie for unrelated totals not forming one meaningful whole.

Pattern families

Easy — Build frequency table

  • Tally categories from raw list.
  • Compute nn as sum of counts.
  • Relative freq = count/nn.

Medium — Chart choice and read

  • Pick bar vs pie from question goal.
  • Read missing count from percent and nn: count = percent/100 × nn.
  • Compare two categories via difference in counts or percents.

Hard — Rounding and complements

  • Percents sum to 99% or 101% due to rounding — know largest category still identifiable often.
  • Given two category percents, find third when three categories total 100%.
  • Two-way categorical preview: row percent vs overall percent (setup for week 4).

Worked mini-examples

Example 1 — Relative frequency.
120 voters: A 54, B 36, C 30.
n=120n=120. Rel: 0.45, 0.30, 0.25.
Example 2 — Percent to count.
n=250n=250, 16% chose option D → count = 0.16 × 250 = 40.
Example 3 — Complement.
40% walked → 60% did not walk (single binary split).
Example 4 — Missing category.
Three flavors: chocolate 45%, vanilla 30%, strawberry ? → strawberry 25%.
Example 5 — Bar vs pie.
Six transport modes with similar counts → bar chart clearer. Two outcome yes/no → pie or bar both OK.

Traps

  • Confusing frequency with relative frequency.
  • Pie chart with dozens of categories.
  • Percents not summing to 100 — rounding, not always error.
  • Bar chart ordered as if ordinal when nominal — order arbitrary unless by count.
  • Using pie when categories are not parts of one group total.

Diagnostic (try yourself)

  1. Raw: [R,R,B,G,R,B,G,G,R] — build frequency table and nn.
  2. n=80n=80, counts A=32, B=20, C=28. Find relative frequencies and percents.
  3. In a survey, 35% preferred online and 45% preferred hybrid. The only other option is in-person. What percent chose in-person?
  4. n=200n=200, 12.5% chose “other”. How many cases?
  5. When would a bar chart be better than a pie chart for the same categorical data? One sentence.

ChatGPT prep archive

Archived import for extra depth — complements the notes above, not official IITM material.

Core concepts

  • Frequency: count per category.
  • Relative frequency: proportion or percent of total; sums to 1 (or 100%).
  • Bar chart: categories on axis; heights show counts or percents.
  • Pie chart: wedges show parts of whole—meaningful for few categories.

Notation & vocabulary

TermFormula
rel freqcount / n
percentrel freq × 100
complement catall others combined

Pattern families

Easy — Frequency table

Tally each category. Total n is sum of counts. Relative freq = count/n.

Medium — Choose chart

Bar for compare categories; pie only for part-whole with moderate category count. Avoid pie with many thin slices.

Hard — Missing category

Given percents summing to <100, infer missing share. Or find count from percent and n.
Drill these on the pattern atlas — filter to week 2.

Traps

  • Percents not summing to 100 due to rounding.
  • Bar chart with ordered nominal when order arbitrary.
  • Pie chart for unrelated categories not one whole.
  • Confusing frequency with relative frequency.

Retrieval prompts

  1. How compute relative frequency?
  2. When bar vs pie?
  3. What must pie wedges sum to?

Practice loop

  1. Read Deep study (if present) or core concepts once.
  2. Recite the formula chain without looking.
  3. Open one easy pattern on the interactive atlas for week 2.
  4. Attempt without solutions; mark studied after an honest try.
  5. Say one trap aloud before closing the tab.
Document outline

Keep your place and jump directly to a heading.

Table of Contents
System Normal // Awaiting Context

Intelligence Hub

Navigate the knowledge graph to generate context. The Hub adapts dynamically to surface backlinks, related notes, and metadata insights.