Home / IB Mathematics SL 4.1 Concepts of population, sample AI HL Paper 2- Exam Style Questions

IB Mathematics SL 4.1 Concepts of population, sample AI HL Paper 2- Exam Style Questions- New Syllabus

Question

The scores of the eight highest scoring countries in the 2019 Eurovision song contest are shown in the following table.

 

 Eurovision score
Netherlands498
Italy472
Russia370
Switzerland364
Sweden334
Norway331
North Macedonia305
Azerbaijan302

 

(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 (\( y \))
Netherlands17498
Italy60472
Russia145370
Switzerland9364
Sweden10334
Norway5331
North Macedonia2305
Azerbaijan10302

 

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
Netherlands31
Italy22
Russia13
Switzerland\( a \)4
Sweden\( b \)5
Norway76
North Macedonia87
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
Markscheme

(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

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