2️⃣ Organising qualitative data

Abstract

Learning objectives

By the end of this module, you will be able to:

Build a frequency table with absolute counts ($n_i$), relative frequencies ($f_i$), and cumulative frequencies ($N_i^{+}$, $F_i^{+}$) for a qualitative variable, by hand.
Distinguish nominal from ordinal qualitative variables and choose the appropriate chart (bar chart for nominal, bar chart with ordered categories for ordinal, pie chart only for nominal with few categories).
Read the mode as the modal category.
Read the frequency and cumulative-frequency formulas aloud as sentences, and write them from a description in words.
Use Excel’s COUNTIF, COUNTIFS, PivotTable counts, and conditional formatting to build the table and chart (homework).

1 Theory

1.1 The business question

The Wandering Fork’s owner wants to know: on which weather conditions does the truck sell most? How concentrated is the payment-method mix? This module turns a column of categories into a frequency table and the right chart, the first step in summarising any qualitative variable. We start with five days small enough to tally by hand, and only then write the formulas.

1.2 Five days, one column of words

Five days of the Harbour truck, as the owner’s notebook records them. The values are a simplified extract chosen so that you can tally everything by hand.

Day	`weather`	`satisfaction_rating` (1–5)
1	Sunny	4
2	Sunny	5
3	Rainy	3
4	Cloudy	4
5	Sunny	4

There is no number to average here: the weather is a word, and even the rating is a score on a scale. The owner asks you for a one-line summary of each column. What would you say — and how would you show it on a single chart?

1.3 Frequency table for a qualitative variable

Note

Try it. Tally the weather column of the five days above ($n = 5$) and complete the table.

Category	Tally	Count	Share of days
Sunny	?	?	?
Cloudy	?	?	?
Rainy	?	?	?
Total		?	?

Count the days in each category. Do the counts add up to 5?
Divide each count by 5. What do the shares add up to?

Check your answer

Category	Tally	Count	Share of days
Sunny	III	3	3/5 = 60%
Cloudy	I	1	1/5 = 20%
Rainy	I	1	1/5 = 20%
Total		5	100%

The counts add up to the number of days, and the shares add up to 100%.

Why do the totals come out exactly 5 and 100%? Every day falls into exactly one category, so nothing is counted twice and nothing is left out. That is also why the shares must add up to 1 — the derivation below makes it precise.

See it. The bars below have a height equal to the count of each category; the share is the count divided by the total, computed by the page from the same five days.

forkDays = [
  {weather: "Sunny", rating: 4},
  {weather: "Sunny", rating: 5},
  {weather: "Rainy", rating: 3},
  {weather: "Cloudy", rating: 4},
  {weather: "Sunny", rating: 4}
]

{
  const cats = ["Sunny", "Cloudy", "Rainy"];
  const counts = cats.map(c => forkDays.filter(d => d.weather === c).length);
  const n = counts.reduce((a, b) => a + b, 0);
  const W = 420, H = 200, base = 150, unit = 30, BLUE = "steelblue";
  let g = "";
  cats.forEach((c, i) => {
    const x = 50 + i * 120, h = counts[i] * unit;
    g += `<rect x="${x}" y="${base - h}" width="80" height="${h}"
      fill="${BLUE}" fill-opacity="0.6" stroke="${BLUE}"/>`;
    g += `<text x="${x + 40}" y="${base - h - 6}" text-anchor="middle"
      fill="currentColor" font-size="13">n = ${counts[i]}
      (${Math.round(100 * counts[i] / n)}%)</text>`;
    g += `<text x="${x + 40}" y="${base + 18}" text-anchor="middle"
      fill="currentColor" font-size="13">${c}</text>`;
  });
  g += `<line x1="40" x2="${W - 10}" y1="${base}" y2="${base}"
    stroke="currentColor"/>`;
  return html`<svg viewBox="0 0 ${W} ${H}" width="100%" role="img"
    aria-label="Bar chart of the weather counts over five days">
    ${g}</svg>`;
}

{
  const cats = ["Sunny", "Cloudy", "Rainy"];
  const counts = cats.map(c => forkDays.filter(d => d.weather === c).length);
  const n = counts.reduce((a, b) => a + b, 0);
  return html`<p><em>${cats.map((c, i) => `${c}: ${counts[i]}/${n} =
    ${Math.round(100 * counts[i] / n)}%`).join(" · ")} — total
    ${counts.reduce((a, b) => a + b, 0)} days.</em></p>`;
}

In plain words: count how many times each category appears, divide by the total, and you have the share of each category.

Definition — absolute and relative frequency

For a qualitative variable with $k$ categories (modalities) observed on $n$ individuals, category $i$ has an absolute frequency (count) $n_i$ and a relative frequency \[ f_i = \frac{n_i}{n}, \] usually reported as a percentage. By construction, $\sum_{i=1}^{k} n_i = n$ and $\sum_{i=1}^{k} f_i = 1$.

Notation decoder: absolute and relative frequency

Symbol	Read aloud	In our example (`weather`, five days)
$k$	“k”	3 categories (Sunny, Cloudy, Rainy)
$n$	“n”	5 days
$n_i$	“n sub i”	the count of category $i$, e.g. $n_{\text{Sunny}} = 3$
$f_i$	“f sub i”	the share of category $i$, e.g. $f_{\text{Sunny}} = 3/5 = 60\%$
$\sum_{i=1}^{k}$	“add up, for i from 1 to k”	$3+1+1 = 5$, and $0.6+0.2+0.2 = 1$

Whole formula as a sentence: “the relative frequency of category $i$ is its count divided by the total number of individuals; the counts add up to $n$ and the relative frequencies add up to 1.”

1.4 Cumulative frequency for ordinal data

Try it. The ratings have a natural order (1 < 2 < 3 < 4 < 5). Complete the running totals for the five days.

Rating	Count	Running count	Running share
1	0	?	?
2	0	?	?
3	1	?	?
4	3	?	?
5	1	?	?

Each running count is the count of that row plus the running count of the row above.
Divide each running count by 5. What share of days had a rating of 4 or less?

Check your answer

Rating	Count	Running count	Running share
1	0	0	0%
2	0	0	0%
3	1	1	20%
4	3	4	80%
5	1	5	100%

80% of the days had a rating of 4 or less. The last running count is always $n = 5$ and the last running share is always 100%.

See it. Each bar of the figure below is a rating level, in order; the number under each bar is the running total of the bar heights from the left, computed from the same five days.

{
  const levels = [1, 2, 3, 4, 5];
  const counts = levels.map(l => forkDays.filter(d => d.rating === l).length);
  const n = counts.reduce((a, b) => a + b, 0);
  let run = 0;
  const cum = counts.map(c => (run += c));
  const W = 420, H = 230, base = 150, unit = 30, BLUE = "steelblue";
  let g = "";
  levels.forEach((l, i) => {
    const x = 40 + i * 72, h = counts[i] * unit;
    g += `<rect x="${x}" y="${base - h}" width="50" height="${h}"
      fill="${BLUE}" fill-opacity="0.6" stroke="${BLUE}"/>`;
    g += `<text x="${x + 25}" y="${base - h - 6}" text-anchor="middle"
      fill="currentColor" font-size="13">${counts[i]}</text>`;
    g += `<text x="${x + 25}" y="${base + 18}" text-anchor="middle"
      fill="currentColor" font-size="13">rating ${l}</text>`;
    g += `<text x="${x + 25}" y="${base + 40}" text-anchor="middle"
      fill="darkorange" font-size="13">N = ${cum[i]}
      (${Math.round(100 * cum[i] / n)}%)</text>`;
  });
  g += `<line x1="30" x2="${W - 10}" y1="${base}" y2="${base}"
    stroke="currentColor"/>`;
  return html`<svg viewBox="0 0 ${W} ${H}" width="100%" role="img"
    aria-label="Bar chart of ratings in order with running totals">
    ${g}</svg>`;
}

In plain words: for ordinal data like satisfaction_rating (1 < 2 < 3 < 4 < 5), you can ask “what share of days had a rating of 3 or less?” and the cumulative frequency answers it.

Formula — cumulative frequency (ordinal only)

If the categories have a natural order (ordinal), you can cumulate: \[ N_i^{+} = \sum_{j=1}^{i} n_j, \qquad F_i^{+} = \frac{N_i^{+}}{n}. \] $N_i^{+}$ is the running total of counts up to and including category $i$; $F_i^{+}$ is the running share. For nominal data, cumulative frequencies are meaningless because there is no order.

Notation decoder: cumulative frequency

Symbol	Read aloud	In our example (ratings, five days)
$n_j$	“n sub j”	the count of rating $j$, e.g. $n_4 = 3$
$\sum_{j=1}^{i}$	“add up, for j from 1 to i”	stop at the rating $i$ you ask about
$N_i^{+}$	“capital N sub i plus”	the running count: $N_4^{+} = 0+0+1+3 = 4$
$n$	“n”	5 days
$F_i^{+}$	“capital F sub i plus”	the running share: $F_4^{+} = 4/5 = 80\%$

Whole formula as a sentence: “the cumulative count up to category $i$ is the sum of the counts of categories 1 through $i$; the cumulative frequency is that running count divided by the total.”

1.5 Nominal vs ordinal — choosing the chart

Try it. Decide, for each column, whether it is nominal or ordinal, and which chart you would use.

Column	Nominal or ordinal?	Chart?
`weather` (Sunny, Cloudy, Rainy)	?	?
`satisfaction_rating` (1 to 5)	?	?

Then compute the angle of the Sunny slice if you drew a pie chart of the five days.

Check your answer

Column	Nominal or ordinal?	Chart?
`weather`	nominal (no order)	bar chart, or pie chart (3 categories)
`satisfaction_rating`	ordinal (1 < … < 5)	bar chart, categories in order 1 to 5

Sunny’s share is 60%, so its slice spans $0.6 \times 360° = 216°$ (Cloudy and Rainy: $72°$ each; $216°+72°+72° = 360°$).

See it. The two bar charts above are the right pictures: the weather bars can be in any order, while the rating bars must follow 1 to 5, because the running totals only make sense in that order. A pie chart has no starting point, so it could not show that order.

In plain words: a pie chart slices a circle; it has no beginning or end, so it cannot show order. A bar chart can be sorted by the meaningful order of the categories.

Definition — nominal vs ordinal charts

Nominal (no order): bar chart (categories in any order) or pie chart (if few categories). The bar order is arbitrary; alphabetical or by frequency are common choices.
Ordinal (natural order): bar chart with categories in their natural order. A pie chart is not appropriate because it ignores the order.

1.6 The mode

Try it. In the two frequency tables you built, which category has the highest count? Is it the same kind of answer for weather and for satisfaction_rating?

Check your answer

weather: Sunny (3 days). satisfaction_rating: 4 (3 days). In both cases the answer is a category, not a number computed from the data: you cannot average words, but you can always name the most common one.

See it. In both bar charts above, the mode is the tallest bar.

In plain words: the mode is the “most common” category.

Definition — mode of a qualitative variable

The mode is the category with the highest absolute frequency $n_i$. For a frequency table, it is simply the row with the largest count.

Notation decoder: mode of a qualitative variable

Symbol	Read aloud	In our example (five days)
$n_i$	“n sub i”	the count of category $i$: 3, 1, 1 for Sunny, Cloudy, Rainy
mode	“the modal category”	Sunny, the row with the largest $n_i$

Whole formula as a sentence: “the mode is the category whose absolute frequency is the largest.”

2 Worked example

We use the Harbour 30-day subset (sheet harbour_30d). The variable weather (nominal, 3 categories) and satisfaction_rating (ordinal, 5 categories) are summarised below.

2.1 `weather` (nominal, $n=30$)

Category	$n_i$	$f_i$ (%)
Sunny	13	43.3
Cloudy	12	40.0
Rainy	5	16.7
Total	30	100.0

Step 1 — counts. Each day’s weather value is tallied: 13 Sunny, 12 Cloudy, 5 Rainy; $13+12+5=30=n$. ✓

Step 2 — relative frequencies. $f_{\text{Sunny}} = 13/30 = 43.3\%$, $f_{\text{Cloudy}} = 12/30 = 40.0\%$, $f_{\text{Rainy}} = 5/30 = 16.7\%$; the three sum to $100.0\%$.

Step 3 — choosing a chart. weather is nominal with only 3 categories: both a bar chart (one bar per category, height = count or frequency) and a pie chart (one slice per category, angle $= f_i \times 360°$) are appropriate. Sunny’s slice spans $0.433 \times 360° \approx 156°$; Cloudy’s $\approx 144°$; Rainy’s $\approx 60°$.

Step 4 — mode. The modal category is Sunny ($n_i = 13$).

2.2 `satisfaction_rating` (ordinal, $n=30$)

Rating	$n_i$	$f_i$ (%)	$N_i^{+}$	$F_i^{+}$ (%)
1	0	0.0	0	0.0
2	6	20.0	6	20.0
3	6	20.0	12	40.0
4	16	53.3	28	93.3
5	2	6.7	30	100.0

Step 1 — counts and shares. Tallied from the 30 days. No day was rated 1, but the category stays in the table with a count of 0: an ordinal scale keeps all its levels.

Step 2 — cumulative frequencies. $N_i^{+}$ and $F_i^{+}$ are the running totals. For example, $F_3^{+} = 40.0\%$ means 40% of days had a rating of 3 or less.

Step 3 — chart. A bar chart with categories in order 1, 2, 3, 4, 5. The bars show the distribution’s shape: more than half of the days are rated 4.

Step 4 — mode. The modal category is 4 ($n_i = 16$).

3 Proof / derivation

Derivation — relative frequencies always sum to 1 (click to expand)

Let a qualitative variable have $k$ categories with counts $n_1, \dots, n_k$, and let $n = \sum_{i=1}^{k} n_i$ be the total number of individuals (every individual falls into exactly one category, so the counts partition the population — the same partition idea used for PivotTable groups in Module 1). Then \[ \sum_{i=1}^{k} f_i = \sum_{i=1}^{k} \frac{n_i}{n} = \frac{1}{n}\sum_{i=1}^{k} n_i = \frac{n}{n} = 1. \] For “The Wandering Fork” weather: $f_{\text{Sunny}}+f_{\text{Cloudy}}+f_{\text{Rainy}} = 0.433+0.400+0.167 = 1.000$. ✓

4 Visual intuition

The pie chart’s geometry makes the frequency-sum-to-1 property visible: the three slices’ angles, $156°+144°+60°=360°$, always complete a full circle no matter how the 30 days split across Sunny/Cloudy/Rainy — exactly as the algebra above guarantees.

For ordinal data, the bar chart with ordered categories makes the cumulative frequency visible: the running total of bar heights up to category $i$ is exactly $N_i^{+}$.

5 Interactive demo

Important

TODO: an interactive frequency table builder (type categories and counts, see the bar/pie chart update) would be a good OJS demo for this module.

6 Exercises

All exercises below are done by hand; the Excel ones are in the homework.

Exercise 1 (Frequency table for satisfaction_rating) Using the worked-example table for satisfaction_rating, verify that the cumulative frequencies $N_i^{+}$ and $F_i^{+}$ are correct. What percentage of days had a rating of 3 or less?

Solution

$F_3^{+} = 40.0\%$ (from the table). Check: $N_3^{+} = 0+6+6 = 12$; $12/30 = 0.400 = 40.0\%$.

Exercise 2 (Choosing the right chart) Which chart type(s) would you use for weather, and why would a line chart be a poor choice?

Solution

A bar chart or a pie chart, because weather is nominal qualitative with no meaningful order between Sunny, Cloudy, and Rainy. A line chart implies a continuous progression between consecutive categories, which does not exist here — connecting “Sunny” to “Cloudy” to “Rainy” with a line would suggest an ordering that isn’t real.

Exercise 3 (Pie-slice angle) What angle does Cloudy’s pie slice occupy, and what percentage of the circle’s area does it represent?

Solution

Angle $= f_{\text{Cloudy}} \times 360° = 0.400 \times 360° = 144°$. Since a pie chart’s slice area is proportional to its angle, Cloudy also represents $40.0\%$ of the circle’s area — matching its relative frequency exactly, by construction.

Exercise 4 (Formula to words) Read $F_i^{+} = \dfrac{1}{n}\sum_{j=1}^{i} n_j$ aloud as one sentence. Which indicator is it, and for which kind of variable does it make sense?

Solution

“The cumulative relative frequency up to category $i$ is the sum of the counts of categories 1 through $i$, divided by the total number $n$ of individuals.” It is the running share, and it only makes sense for ordinal variables, where the categories have an order.

Exercise 5 (Words to formula) Write in symbols: (a) “the share of category $i$ is its count divided by the total”; (b) “the counts of the $k$ categories add up to the total”; (c) “the running count up to category $i$”.

Solution

$f_i = \dfrac{n_i}{n}$. (b) $\sum_{i=1}^{k} n_i = n$.
$N_i^{+} = \sum_{j=1}^{i} n_j$.

7 Common mistakes

Watch out

Using a pie chart for ordinal data. A pie chart destroys the order information; use a bar chart with categories in their natural order.
Cumulative frequencies for nominal data. $N_i^{+}$ and $F_i^{+}$ only make sense when the categories have a natural order.
Forgetting that percentages may not sum to exactly 100% due to rounding. $43.3\% + 40.0\% + 16.7\% = 100.0\%$ here, but with more categories you might get $99.9\%$ or $100.1\%$ — this is a rounding artifact, not an error.
Using COUNT instead of COUNTA for text categories. COUNT only counts numbers; COUNTA counts everything non-empty.

8 Exit check

Five minutes, no calculator needed.

In one sentence, what does a relative frequency $f_i$ measure?
Why are cumulative frequencies meaningless for weather?
Read $f_i = n_i / n$ aloud as a sentence.
Five days are rated 1, 2, 2, 4, 5. Build the table of counts and running shares, and give the mode.
Which chart would you use for the five ratings, and why not a pie chart?

Answers

The share of the $n$ individuals that fall into category $i$: its count divided by the total.
weather is nominal: there is no order, so “running total up to Rainy” depends on an arbitrary listing order.
“The relative frequency of category $i$ is its count divided by the total number of individuals.”
Counts for ratings 1 to 5: 1, 2, 0, 1, 1. Running counts: 1, 3, 3, 4, 5; running shares: 20%, 60%, 60%, 80%, 100%. The mode is rating 2 (2 days).
A bar chart with the ratings in the order 1 to 5: the rating is ordinal, and a pie chart has no starting point, so it cannot show the order.

9 Homework — Excel lab

Reproduce, in Excel, every table you built by hand in class, with COUNTIF, COUNTIFS, running sums, a PivotTable and conditional formatting, then apply them to the full dataset.

Estimated time: about 50–60 minutes. Any spreadsheet application works, at home or in a campus computer room.

Follow-up: a 10-minute debrief at the start of Module 3. Bring your Step 0 results and any question on Homework 1–3.

9.1 Using Excel

The sheet harbour_30d of wandering-fork.xlsx has the data. The complete list of function names, with their French equivalents, is on the Excel functions page.

Concept	Excel function / steps
Count one category	`=COUNTIF(range, "Sunny")`
Count with several criteria	`=COUNTIFS(range1, criterion1, range2, criterion2, …)`
Relative frequency	`=count/COUNT(range)` (or `/COUNTA` for text data)
Cumulative count	First row `=count`; below `=above + current` (running sum)
Cumulative share	`=cumulative_count/COUNT(range)`
Bar chart	Select the summary table → Insert → Chart → Clustered Column
Pie chart (nominal only)	Select the summary table → Insert → Chart → Pie
PivotTable counts	Insert → PivotTable, put the qualitative field in Rows and the same field (or any non-empty field) in Values set to Count
Conditional formatting	Select the count column → Home → Conditional Formatting → Highlight Cell Rules → Greater Than (or a custom formula)

For the full table (120 days per franchise) use the Excel Table tblFork and add the franchise as one more condition, for example =COUNTIFS(tblFork[franchise],"Harbour",tblFork[weather],"Sunny").

9.2 Step 0 — check your class results

Type the five weather values (Sunny, Sunny, Rainy, Cloudy, Sunny) in A2:A6 and the five ratings (4, 5, 3, 4, 4) in B2:B6.
Count each weather category with =COUNTIF($A$2:$A$6, "Sunny") (and the same for Cloudy and Rainy), then divide by =COUNTA($A$2:$A$6).
Count each rating level 1 to 5 with =COUNTIF($B$2:$B$6, 1) (and so on), then build the running count (=above + current) and the running share.
Compare every result with your hand computation.

Expected results

Category	Count	Share
Sunny	3	60%
Cloudy	1	20%
Rainy	1	20%

Rating	Count	Running count	Running share
1	0	0	0%
2	0	0	0%
3	1	1	20%
4	3	4	80%
5	1	5	100%

9.3 Homework 1 (Excel) — Build the frequency table with `COUNTIFS`

In wandering-fork.xlsx, filter franchise to “Harbour” and use COUNTIFS to build the frequency table for weather over all 120 days. What are the counts and relative frequencies?

Solution

Formula for Sunny: =COUNTIFS(tblFork[franchise],"Harbour",tblFork[weather],"Sunny"). You should obtain: Sunny 53 (44.2%), Cloudy 42 (35.0%), Rainy 25 (20.8%). The proportions are close to the 30-day subset (43.3%, 40.0%, 16.7%) because the data were generated with the same weather probabilities; the gaps of a few points are ordinary chance variation.

9.4 Homework 2 (Excel) — PivotTable and conditional formatting

Build a PivotTable for satisfaction_rating (Harbour, 120 days) with the rating in Rows and Count of rating in Values. Apply conditional formatting to highlight the modal category (the one with the highest count). What is the mode?

Solution

The PivotTable gives counts: 1→4, 2→21, 3→40, 4→45, 5→10 (total 120). The mode is 4 (45 days). Conditional formatting rule: =B2=MAX($B$2:$B$6) applied to the count column highlights the row for rating 4.

9.5 Homework 3 (Excel) — Campus weather comparison

Build the weather frequency table for Campus (120 days) using COUNTIFS on tblFork. Compare the relative frequencies with Harbour’s. Are they similar?

Solution

Campus: Sunny 50 (41.7%), Cloudy 48 (40.0%), Rainy 22 (18.3%). The proportions are similar to Harbour’s (44.2%, 35.0%, 20.8%) because both franchises were generated with the same weather probabilities; the remaining differences are chance variation.

10 Further reading

Source	Where	What it adds	Time	Verified
OpenStax IBS 2e	§2.1 Display Data (book pages 46–65) — the parts on bar graphs, pie charts, and frequency tables	A second explanation of qualitative frequency tables and charts with more examples	~19 pages	checked against the PDF contents (T046)
Khan Academy	Unit Analyzing categorical data in the Statistics and probability course; look for lessons on frequency tables, bar charts, and pie charts	Short videos and practice questions on building and reading qualitative charts	~30–45 min (estimate)	link check pending (T048)
Khan Academy	Unit Displaying and comparing quantitative data in the Statistics and probability course; look for lessons on categorical data displays	Additional practice on bar charts and pie charts	~20 min (estimate)	link check pending (T048)

OpenStax IBS 2e: Alexander Holmes, Barbara Illowsky and Susan Dean, Introductory Business Statistics 2e, OpenStax, Rice University, https://openstax.org/details/books/introductory-business-statistics-2e, licensed under CC BY-NC-SA 4.0. Sections are cited, not reproduced.

2️⃣ Organising qualitative data

1 Theory

1.1 The business question

1.2 Five days, one column of words

1.3 Frequency table for a qualitative variable

Notation decoder: absolute and relative frequency

1.4 Cumulative frequency for ordinal data

Notation decoder: cumulative frequency

1.5 Nominal vs ordinal — choosing the chart

1.6 The mode

Notation decoder: mode of a qualitative variable

2 Worked example

2.1 `weather` (nominal, \(n=30\))

2.2 `satisfaction_rating` (ordinal, \(n=30\))

3 Proof / derivation

4 Visual intuition

5 Interactive demo

6 Exercises

7 Common mistakes

8 Exit check

9 Homework — Excel lab

9.1 Using Excel

9.2 Step 0 — check your class results

9.3 Homework 1 (Excel) — Build the frequency table with `COUNTIFS`

9.4 Homework 2 (Excel) — PivotTable and conditional formatting

9.5 Homework 3 (Excel) — Campus weather comparison

10 Further reading

1 Theory

1.1 The business question

1.2 Five days, one column of words

1.3 Frequency table for a qualitative variable

Notation decoder: absolute and relative frequency

1.4 Cumulative frequency for ordinal data

Notation decoder: cumulative frequency

1.5 Nominal vs ordinal — choosing the chart

1.6 The mode

Notation decoder: mode of a qualitative variable

2 Worked example

2.1 weather (nominal, \(n=30\))

2.2 satisfaction_rating (ordinal, \(n=30\))

3 Proof / derivation

4 Visual intuition

5 Interactive demo

6 Exercises

7 Common mistakes

8 Exit check

9 Homework — Excel lab

9.1 Using Excel

9.2 Step 0 — check your class results

9.3 Homework 1 (Excel) — Build the frequency table with COUNTIFS

9.4 Homework 2 (Excel) — PivotTable and conditional formatting

9.5 Homework 3 (Excel) — Campus weather comparison

10 Further reading

2.1 `weather` (nominal, \(n=30\))

2.2 `satisfaction_rating` (ordinal, \(n=30\))

9.3 Homework 1 (Excel) — Build the frequency table with `COUNTIFS`