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Date None Specimen Marks available 3 Reference code SPNone.2.sl.TZ0.8
Level SL only Paper 2 Time zone TZ0
Command term Find Question number 8 Adapted from N/A

Question

Each day, a factory recorded the number ( \(x\) ) of boxes it produces and the total production cost ( \(y\) ) dollars. The results for nine days are shown in the following table.

Write down the equation of the regression line of y on x .

[2]
a.

Use your regression line from part (a) as a model to answer the following.

Interpret the meaning of

(i)     the gradient;

(ii)    the y-intercept.

[2]
b(i) and (ii).

Estimate the cost of producing 60 boxes.

[2]
c.

The factory sells the boxes for $19.99 each. Find the least number of boxes that the factory should produce in one day in order to make a profit.

[3]
d.

Comment on the appropriateness of using your model to

(i)     estimate the cost of producing 5000 boxes;

(ii)    estimate the number of boxes produced when the total production cost is $540.

[4]
e(i) and (ii).

Markscheme

\(y = 10.7x + 121\)     A1A1     N2

[2 marks]

a.

(i) additional cost per box (unit cost)     A1     N1

(ii) fixed costs     A1     N1

[2 marks]

b(i) and (ii).

attempt to substitute into regression equation     M1

e.g. \(y = 10.7 \times 60 + 121\) , \(y = 760.12 \ldots \)

\({\text{cost}} = \$ 760\) (accept \(\$ 763\) from 3 s.f. values)     A1    N2

[2 marks]

c.

setting up inequality (accept equation)     M1

e.g. \(19.99x > 10.7x + 121\)

\(x > 12.94 \ldots \) A1

13 boxes (accept 14 from \(x > 13.02\) , using 3 s.f. values)     A1     N2

Note: Exception to the FT rule: if working shown, award the final A1 for a correct integer solution for their value of x.

[3 marks]

d.

(i) this would be extrapolation, not appropriate     R1R1     N2

(ii) this regression line cannot predict x from y, not appropriate     R1R1     N2

[4 marks]

e(i) and (ii).

Examiners report

[N/A]
a.
[N/A]
b(i) and (ii).
[N/A]
c.
[N/A]
d.
[N/A]
e(i) and (ii).

Syllabus sections

Topic 5 - Statistics and probability » 5.4 » Equation of the regression line of \(y\) on \(x\).
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