Class 11 Statistics Notes · GSEB
Range
Range — compute the simplest spread in one step and judge it with the coefficient of range. GSEB Class 11 Statistics notes with grouped-class rules, solved sums and the quality-control use.
Last updated: 5 Oct 2026
Notes
Range — Definition and Formula
Words marked with a dotted underline carry the question they usually appear in — tap them to reveal it.
“Uncontrolled variation is the enemy of quality.”— Edward Deming
The bracket alone is the range — unit-bound. Divide it by x_H + x_L and it becomes a pure number.
Coefficient of range = (x_H − x_L) ÷ (x_H + x_L) = 24 ÷ 118 = 0.20
Only about 20% of (x_H + x_L) is “spread” — a small coefficient means low variability.
Frequency Plays No Role
⭐ What the book insists on
Grouped data — take the limits, never the mid-values
| Class | 10 – 15 | 15 – 20 | 20 – 25 | 25 – 30 | 30 – 35 |
|---|---|---|---|---|---|
| Reading | x_L = 10 | — | — | — | x_H = 35 |
R = 35 − 10 = 25. Using mid-values (32.5 − 12.5 = 20) would be wrong — the limits are what the class intervals actually span.
Coefficient of Range
The range carries the data's unit, so it cannot compare two datasets. Its paired relative measure removes the unit: Coefficient of range = (x_H − x_L)/(x_H + x_L).
A typo in the book's formula table
Reading the coefficient
Where the Range Is Used
Statistical quality control
Control charts plot the range of a sample over time. A packaging line filling 500 g pouches is checked by weighing a handful each hour — the spread of that handful, not its average, tells the operator whether the machine has drifted.
Money, exchange and share prices
A share that moved between ₹80 and ₹115 over 8 weeks has a range of ₹35— a one-line answer to “how volatile has this stock been?”
Range alone
A Zomato delivery partner is judged on his best and worst run of the week. Two riders with the same range look identical — yet one is steady every other day and the other swings wildly. The range never looked at those days.
Range + a deviation-based measure
Quote the range for a quick first look, then confirm with standard deviation (or at least the quartile deviation) before making the call — that is the habit this chapter is building.
Solved Example — Illustration 1
Solved Example
Problem
Solution
R = 75 runs · coefficient ≈ 0.68
Worked Example Gallery
Range — solved practicals
5 problemsRaw data — Illustration 1 (runs)
The runs scored by a batsman in his last 10 innings of cricket matches are 48, 75, 37, 52, 93, 81, 25, 72, 18 and 60. Find the range and the coefficient of range of his runs.
| x_H | x_L | R = x_H − x_L | x_H + x_L | Coefficient |
|---|---|---|---|---|
| 93 | 18 | 75 runs | 111 | 75/111 ≈ 0.68 |
R = 75 runs, coefficient ≈ 0.68
- x_H = 93, x_L = 18.
- R = 93 − 18 = 75 runs.
- Coeff = 75/(93 + 18) = 75/111 = 0.6757 ≈ 0.68.
Discrete frequency distribution — Illustration 2 (salaries)
From the following information of monthly salary (in ₹) of workers of a factory, find the range and the coefficient of range of the salary.
| Monthly Salary (₹) | 3500 | 4000 | 5000 | 7500 | 10,000 | 12,000 |
|---|---|---|---|---|---|---|
| No. of workers | 3 | 21 | 30 | 19 | 6 | 5 |
| Salary (₹) | 3,500 | 5,000 | 6,500 | 8,000 | 9,500 | 12,000 |
|---|---|---|---|---|---|---|
| Workers (f) | 3 | 4 | 5 | 5 | 4 | 3 |
R = ₹8,500, coefficient ≈ 0.55
- Highest salary x_H = 12,000, lowest salary x_L = 3,500 — frequencies never enter.
- R = 12,000 − 3,500 = ₹8,500.
- Coefficient = 8,500/(12,000 + 3,500) = 8,500/15,500 = 0.548 ≈ 0.55.
Continuous classes — weights
The items produced in a factory are packed into different boxes according to their weight. Using the following information, find the range and the relative range of weight of boxes :
| Weight (kg) | 10 - 15 | 15 - 20 | 20 - 25 | 25 - 30 | 30 - 35 |
|---|---|---|---|---|---|
| No. of boxes | 8 | 15 | 26 | 47 | 4 |
| x_H | x_L | R | x_H + x_L | Coefficient |
|---|---|---|---|---|
| 35 | 10 | 25 kg | 45 | 25/45 ≈ 0.56 |
R = 25 kg, coefficient ≈ 0.56
Mid-values would have given 32.5 − 12.5 = 20 — wrong for range.
- Take limits, not mid-values: x_H = 35 (upper limit of 30 – 35), x_L = 10 (lower limit of 10 – 15).
- R = 35 − 10 = 25 kg.
- Coefficient = 25/(35 + 10) = 25/45 = 0.555… ≈ 0.56.
Inclusive classes — marks
Find the range and the coefficient of range of the marks from the following frequency distribution of marks scored by 50 students of a school in a certain examination.
| Marks | 50 - 59 | 60 - 69 | 70 - 79 | 80 - 89 | 90 - 99 |
|---|---|---|---|---|---|
| No. of Students | 2 | 15 | 23 | 6 | 4 |
| x_H | x_L | R | x_H + x_L | Coefficient |
|---|---|---|---|---|
| 99 | 50 | 49 marks | 149 | 49/149 ≈ 0.33 |
R = 49 marks, coefficient ≈ 0.33
- x_H = 99 (upper limit of 90 – 99), x_L = 50 (lower limit of 50 – 59).
- R = 99 − 50 = 49 marks.
- Coefficient = 49/(99 + 50) = 49/149 = 0.3289 ≈ 0.33.
Grouped, exercise — pipe diameters
For a company making pipes, the following information of diameter (in cm) of pipes is obtained. Find the range of the diameter of the pipes.
| Diameter (cm) | 20 - 40 | 40 - 60 | 60 - 80 | 80 - 100 | 100 - 120 |
|---|---|---|---|---|---|
| No. of pipes | 15 | 40 | 75 | 20 | 11 |
Range = upper limit of the last class − lower limit of the first = 120 − 20 = 100 cm.
R = 100 cm
- Grouped rule: upper limit of the last class − lower limit of the first class.
- 120 − 20 = 100 cm.
Advantages and Limitations
Range — merits and demerits
Advantages
- It is clearly defined — every student gets the same answer from the same data.
- Its computation is the simplest of all measures: identify two values, subtract.
- It is useful when the variability in the data is known to be small — a quick, cheap first look.
Disadvantages
- It ignores every observation except the two extremes.
- It is very sensitive to sampling fluctuations — one unusual item changes it completely.
- It is not suitable for further algebraic or statistical operations.
- It cannot be computed for open-ended frequency distributions (for example "100 and above").
Quick Reference
| Idea | Statement |
|---|---|
| Definition | Range = difference between the highest and lowest observation of the data |
| Formula | R = x_H − x_L |
| Grouped data | Upper limit of the highest class − lower limit of the lowest class (take limits, not mid-values) |
| Frequencies | Irrelevant — frequency does not play any role in determining the range |
| Coefficient | (x_H − x_L)/(x_H + x_L) — the book table prints x_H − x_L as the denominator; worked examples use the sum |
| Type | Range is an absolute measure — it carries the data unit |
| Open-ended classes | Cannot be computed — no upper limit to read off |
Key Takeaways
Key Takeaways
- Range = x_H − x_L — the difference between the highest and the lowest observation, and nothing else.
- For grouped data take the limits (upper limit of the last class − lower limit of the first), never the mid-values.
- Frequency does not play any role in determining the range, even for grouped data. So what: a class with 2 students and a class with 200 students give the identical range.
- Coefficient of range = (x_H − x_L)/(x_H + x_L); small means little variability, large means a lot — and the book's printed denominator (x_H − x_L) is a typo.
- Range is trivial to compute but blind to everything between the extremes, unstable under sampling, and impossible for open-ended classes — so it is a first look, never the final word.
- So what: control charts on a factory floor, a share's ₹80–₹115 swing, and a rider's best/worst run all start with the range — and all of them need a second measure before a decision is made.