IB Mathematics SL 4.1 Concepts of population, sample AI HL Paper 2- Exam Style Questions- New Syllabus
The scores of the eight highest scoring countries in the 2019 Eurovision song contest are shown in the following table.
Eurovision score | |
---|---|
Netherlands | 498 |
Italy | 472 |
Russia | 370 |
Switzerland | 364 |
Sweden | 334 |
Norway | 331 |
North Macedonia | 305 |
Azerbaijan | 302 |
(a)
For this data, find:
(i) the upper quartile. [2]
(ii) the interquartile range. [2]
(b) Determine if the Netherlands’ score is an outlier for this data. Justify your answer. [3]
Chester is investigating the relationship between the highest-scoring countries’ Eurovision score and their population size to determine whether population size can reasonably be used to predict a country’s score.
The populations of the countries, to the nearest million, are shown in the table.
Population (\( x \)) (millions) | Eurovision score (\( y \)) | |
---|---|---|
Netherlands | 17 | 498 |
Italy | 60 | 472 |
Russia | 145 | 370 |
Switzerland | 9 | 364 |
Sweden | 10 | 334 |
Norway | 5 | 331 |
North Macedonia | 2 | 305 |
Azerbaijan | 10 | 302 |
Chester finds that, for this data, the Pearson’s product moment correlation coefficient is \( r = 0.249 \).
(c) State whether it would be appropriate for Chester to use the equation of a regression line for \( y \) on \( x \) to predict a country’s Eurovision score. Justify your answer. [2]
Chester then decides to find the Spearman’s rank correlation coefficient for this data, and creates a table of ranks.
Population rank (to the nearest million) | Eurovision score rank | |
---|---|---|
Netherlands | 3 | 1 |
Italy | 2 | 2 |
Russia | 1 | 3 |
Switzerland | \( a \) | 4 |
Sweden | \( b \) | 5 |
Norway | 7 | 6 |
North Macedonia | 8 | 7 |
Azerbaijan | \( c \) | 8 |
(d) Write down the value of:
(i) \( a \),
(ii) \( b \),
(iii) \( c \).
(e) Find the value of the Spearman’s rank correlation coefficient \( r_s \). [2]
(ii) Interpret the value obtained for \( r_s \). [1]
(f) When calculating the ranks, Chester incorrectly read the Netherlands’ score as 478. Explain why the value of the Spearman’s rank correlation \( r_s \) does not change despite this error. [2]
▶️ Answer/Explanation
(a)(i)
Order the scores (ascending): 302, 305, 331, 334, 364, 370, 472, 498
With \( n = 8 \), use the median-of-halves rule:
Upper half = (364, 370, 472, 498) \( \Rightarrow Q_3 = \frac{370 + 472}{2} = 421 \) (A1)
Result:
\( Q_3 = 421 \)
(a)(ii)
Lower half = (302, 305, 331, 334) \( \Rightarrow Q_1 = \frac{305 + 331}{2} = 318 \)
\( IQR = Q_3 – Q_1 = 421 – 318 = 103 \) (A1)
Result:
\( IQR = 103 \)
(b)
Upper fence: \( Q_3 + 1.5 \times IQR = 421 + 1.5 \times 103 = 575.5 \)
Lower fence: \( Q_1 – 1.5 \times IQR = 318 – 1.5 \times 103 = 163.5 \)
Netherlands’ score \( 498 \) lies between 163.5 and 575.5 \( \Rightarrow \) not an outlier (A1)(A1)
Result:
Netherlands is not an outlier
(c)
Pearson’s \( r = 0.249 \) is close to 0 \( \Rightarrow \) weak linear association, so a regression line is not appropriate for prediction (A1)
Result:
Not appropriate (r too close to zero)
(d)(i)
Population ranks (to the nearest million): Russia 1, Italy 2, Netherlands 3, Sweden 4.5, Azerbaijan 4.5, Switzerland 6, Norway 7, North Macedonia 8
For Switzerland (9 million), \( a = 6 \) (A1)
Result:
\( a = 6 \)
(d)(ii)
For Sweden (10 million), \( b = 4.5 \) (tied with Azerbaijan) (A1)
Result:
\( b = 4.5 \)
(d)(iii)
For Azerbaijan (10 million), \( c = 4.5 \) (tied with Sweden) (A1)
Result:
\( c = 4.5 \)
(e)(i)
Using average ranks for ties and computing Spearman’s \( r_s \) (Pearson on ranks) gives
\( r_s \approx 0.683 \) (to 3 d.p.) (A1)
Result:
\( r_s = 0.683 \)
(e)(ii)
There is a positive association between population and Eurovision score ranks (A1)
Result:
Positive association between population and score ranks
(f)
Reading Netherlands as 478 instead of 498 does not change its rank (still the highest)
Since Spearman uses ranks, all rank differences — and thus \( r_s \) — are unchanged (A1)
Result:
Netherlands remains top rank, so \( r_s \) unchanged