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Date May 2012 Marks available 7 Reference code 12M.3sp.hl.TZ0.4
Level HL only Paper Paper 3 Statistics and probability Time zone TZ0
Command term Find and Hence or otherwise Question number 4 Adapted from N/A

Question

The continuous random variable X has probability density function f given by

f(x)={2x,0

Sketch the function f and show that the lower quartile is 0.5.

[3]
a.

(i)     Determine E(X ).

(ii)     Determine {\text{E}}({X^2}).

[4]
b.

Two independent observations are made from X and the values are added.

The resulting random variable is denoted Y .

(i)     Determine {\text{E}}(Y - 2X) .

(ii)     Determine {\text{Var}}\,(Y - 2X).

[5]
c.

(i)     Find the cumulative distribution function for X .

(ii)     Hence, or otherwise, find the median of the distribution.

[7]
d.

Markscheme

piecewise linear graph

 


correct shape     A1

with vertices (0, 0), (0.5, 1) and (2, 0)     A1

LQ: x = 0.5 , because the area of the triangle is 0.25     R1

[3 marks]

a.

(i)     {\text{E}}(X) = \int_0^{0.5} {x \times 2x{\text{d}}x + \int_{0.5}^2 {x \times \left( {\frac{4}{3} - \frac{2}{3}x} \right){\text{d}}x = \frac{5}{6}{\text{ }}( = 0.833...)} }     (M1)A1

 

(ii)     {\text{E}}({X^2}) = \int_0^{0.5} {{x^2} \times 2x{\text{d}}x + \int_{0.5}^2 {{x^2} \times \left( {\frac{4}{3} - \frac{2}{3}x} \right){\text{d}}x = \frac{7}{8}{\text{ }}( = 0.875)} }     (M1)A1

[4 marks]

b.

(i)     {\text{E}}(Y - 2X) = 2{\text{E}}(X) - 2{\text{E}}(X) = 0     A1

 

(ii)     {\text{Var}}\,(X) = \left( {{\text{E}}({X^2}) - {\text{E}}{{(X)}^2}} \right) = \frac{{13}}{{72}}     A1

Y = {X_1} + {X_2} \Rightarrow {\text{Var}}\,(Y) = 2{\text{Var }}(X)     (M1)

{\text{Var}}\,(Y - 2X) = 2{\text{Var}}\,(X) + 4{\text{Var}}\,(X) = \frac{{13}}{{12}}     M1A1

[5 marks]

c.

(i)     attempt to use cf(x) = \int {f(u){\text{d}}u}     M1

 

obtain cf(x) = \left\{ {\begin{array}{*{20}{c}}   {{x^2},}&{0 \leqslant x \leqslant 0.5,} \\   {\frac{{4x}}{3} - \frac{1}{3}{x^2} - \frac{1}{3},}&{0.5 \leqslant x \leqslant 2,} \end{array}} \right.     \begin{array}{*{20}{c}}   {{\boldsymbol{A1}}} \\   {{\boldsymbol{A2}}} \end{array}

 

(ii)     attempt to solve cf(x) = 0.5     M1

\frac{{4x}}{3} - \frac{1}{3}{x^2} - \frac{1}{3} = 0.5     (A1)

obtain 0.775     A1 

 

Note: Accept attempts in the form of an integral with upper limit the unknown median.

 

Note: Accept exact answer 2 - \sqrt {1.5} .

 

[7 marks]

d.

Examiners report

There was a curious issue about the lower quartile in part (a): The LQ coincides with a quarter of the range of the distribution \frac{2}{4} = 0.5. Sadly this is wrong reasoning – the correct reasoning involves a consideration of areas.

a.

There was a curious issue about the lower quartile in part (a): The LQ coincides with a quarter of the range of the distribution \frac{2}{4} = 0.5. Sadly this is wrong reasoning – the correct reasoning involves a consideration of areas.

In part (b) many candidates used hand calculation rather than their GDC.

The random variable Y was not well understood, and that followed into incorrect calculations involving Y – 2X.

b.

There was a curious issue about the lower quartile in part (a): The LQ coincides with a quarter of the range of the distribution \frac{2}{4} = 0.5. Sadly this is wrong reasoning – the correct reasoning involves a consideration of areas.

In part (b) many candidates used hand calculation rather than their GDC.

The random variable Y was not well understood, and that followed into incorrect calculations involving Y – 2X.

c.

There was a curious issue about the lower quartile in part (a): The LQ coincides with a quarter of the range of the distribution \frac{2}{4} = 0.5. Sadly this is wrong reasoning – the correct reasoning involves a consideration of areas.

In part (b) many candidates used hand calculation rather than their GDC.

The random variable Y was not well understood, and that followed into incorrect calculations involving Y – 2X.

d.

Syllabus sections

Topic 7 - Option: Statistics and probability » 7.1 » Cumulative distribution functions for both discrete and continuous distributions.
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