Notes/Mathematics/Paper 6/Continuous Random Variables
CAIEA Level9709§6.3

Continuous Random Variables

Probability becomes area under a curve rather than the height of a bar — the algebra of finding a constant, a probability, a mean, a variance and a median, all by integration.

260 min read 5 sub-topics
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Every distribution so far — binomial, Poisson — has been discrete: XX jumps between whole numbers, and P(X=r)P(X=r) is a genuine, calculable probability. Many real quantities aren't like that at all — a component's exact lifetime, a chemical's exact mass, the exact time a bus arrives — they vary continuously, so the exact value X=cX=c for any single cc has probability zero. What replaces a distribution table is a probability density function f(x)f(x), and what replaces "add up the probabilities" is integration: probability becomes the area under ff between two values, not the height of a single bar.

Across 2021–2025 this topic carried 322 marks over 112 tagged parts spread over 3737 papers — an average of about 8.7 of the 50 marks on every Paper 6, the lightest of the five S2 topics by mark share, though only middling in difficulty (2.312.31 out of 44). Being the lightest doesn't make it optional: it is very nearly a whole question on every paper, it almost always appears as the long 88–1212 mark question near the end, and the marks are among the most reliably winnable on the paper — provided the integration is clean.

Nearly every part you will meet is one of five jobs, and this note takes them in that order:

  • §01 — the defining properties of a pdf: non-negativity and total area 11, used to find an unknown constant, to verify a proposed density, to exploit a stated symmetry, and to sketch the density of a transformed variable.
  • §02 — probability as area: P(a<X<b)=∫abf(x) dxP(a<X<b)=\int_a^b f(x)\,dx, the case of a constant density where no integration is needed at all, and what happens when the observation is repeated several times.
  • §03 — mean and variance: E(X)=∫xf(x) dxE(X)=\int xf(x)\,dx, the second moment E(X2)E(X^2), and Var(X)=E(X2)−[E(X)]2Var(X)=E(X^2)-[E(X)]^2 — including the harder integrals recent papers have favoured, which need logarithms or integration by parts.
  • §04 — the median: the value mm that splits the area in half, how it compares with the mean for a skewed density, and using E(X)E(X) or mm as a limit of a further integral.
  • §05 — percentiles and unknown limits: the same area equation set equal to something other than 12\tfrac12, with the unknown at either end; the cubics it often produces; rejecting roots that fall outside the support; and verifying an answer that cannot be solved for exactly.

This topic looks like pure integration, and the integration is rarely the hard part — the marks are lost on the surrounding decisions: which limits to use, whether symmetry makes the integral unnecessary, and which of two algebraic roots is actually possible. Read every question to the end before integrating anything, because later parts very often hand back the work you have just done.

Before you start you should be able to
  • Definite integration of polynomials, of 1x\frac1x (giving ln⁡x\ln x) and of negative powers such as 1x2\frac{1}{x^2} (§1.8/§2.5/§3.5)

  • Integration of sin⁡\sin and cos⁡\cos, including forms like cos⁡πx\cos\pi x — recent papers use trigonometric densities (§3.5)

  • Integration by parts, ∫u dvdx dx=uv−∫v dudx dx\int u\,\frac{dv}{dx}\,dx = uv-\int v\,\frac{du}{dx}\,dx — needed whenever xf(x)xf(x) is a polynomial times a trigonometric function (§3.5)

  • The reverse chain rule, for integrating a bracket raised to a power such as (x−20)2(x-20)^2 (§3.5)

  • Solving quadratic and cubic equations — including spotting an obvious root and factorising it out — and rejecting any root outside a given domain (§2.6/§3.6)

  • Laws of logarithms, for tidying an answer such as 40ln⁡20−40ln⁡1040\ln20-40\ln10 into 40ln⁡240\ln2 (§2.2)

  • Area formulas for a rectangle, a triangle, a semicircle and a circular sector

By the end of this page you can
  • State and check the two defining properties of a probability density function, and use them to find an unknown constant

  • Use symmetry of a density to read off probabilities, and the mean, without integrating

  • Find P(a<X<b)P(a<X<b) as a definite integral of f(x)f(x), and handle a constant density by proportion of length instead

  • Sketch the density of a related variable specified by its mean and variance — getting the centre from the mean, the width from the square root of the variance ratio, and the height from "area stays 11"

  • Use a single probability pp across repeated independent observations of XX — pnp^n, 1−(1−p)n1-(1-p)^n, and 2p(1−p)2p(1-p)

  • Find E(X)=∫xf(x) dxE(X)=\displaystyle\int xf(x)\,dx and Var(X)=E(X2)−[E(X)]2Var(X)=E(X^2)-[E(X)]^2 by integration, including integrals that need a logarithm or integration by parts

  • Find the median by solving ∫lowermf(x) dx=12\displaystyle\int_{\text{lower}}^{m}f(x)\,dx=\tfrac12 from the bottom of the support, and explain how median compares with mean for a skewed density

  • Use E(X)E(X) or the median as a limit of a further integral, e.g. P(X<E(X))P(X<E(X)) or P(E(X)⩽X⩽m)P(E(X)\leqslant X\leqslant m)

  • Locate any percentile by solving ∫f(x) dx=q\displaystyle\int f(x)\,dx = q for an unknown limit — upper or lower — and verify a value by bracketing

  • Solve the quadratic or cubic that results, and reject every root lying outside the support, stating the reason

01

Probability density functions: properties and finding constants

Syllabus requirement · §6.3

“

understand the concept of a continuous random variable, and recall and use properties of a probability density function. Notes: for density functions defined over a single interval only; the domain may be infinite.

”

A probability density function (pdf), f(x)f(x), describes a continuous random variable XX — one that can take any value in an interval, not just whole numbers. f(x)f(x) is not itself a probability (it can even exceed 11); it's a density, and probability comes from the area underneath it. Two properties define any valid pdf, over its support (the interval of xx-values where XX can actually fall — f(x)=0f(x)=0 everywhere else):

f(x)⩾0for all x,∫f(x) dx=1  over the supportf(x) \geqslant 0 \quad \text{for all } x, \qquad \int f(x)\,dx = 1 \ \text{ over the support}

The first is just common sense — a density can't be negative. The second is the continuous version of "a distribution table's probabilities sum to 11": the whole area under the curve, across every value XX could take, must account for all the probability there is.

Almost every question gives you a support with two ordinary numbers at its ends, like 0⩽x⩽20\leqslant x\leqslant2. The syllabus does allow it to be unbounded — its own example is f(x)=3x4f(x)=\dfrac{3}{x^4} for x⩾1x\geqslant1, and ∫1∞3x4dx=[−1x3]1∞=0−(−1)=1\int_1^{\infty}\frac{3}{x^4}dx = \left[-\frac{1}{x^3}\right]_1^{\infty} = 0-(-1) = 1, so that really is a pdf. Nothing about the method changes: the upper limit is just ∞\infty, and a term like −1x3-\frac{1}{x^3} goes to 00 as x→∞x\to\infty.

P(a < X < b) is the area under f(x) between a and bf(x)P(1<X<2.5)x = 2012.54a single point has zero width, so no area:P(X = 2) = 0the total area under the whole curve is exactly 1 — every probability is a slice of that area

Probability is the shaded area under f(x) — and a single point, having zero width, has zero area and therefore zero probability.

P(X = c) = 0 for any continuous X

Because a single value has zero width, it contributes zero area, so P(X=c)=0P(X=c)=0 for every cc. This has a genuinely useful consequence: for a continuous variable, P(X<c)P(X<c), P(X⩽c)P(X\leqslant c), P(a<X<b)P(a<X<b) and P(a⩽X⩽b)P(a\leqslant X\leqslant b) are all equal — unlike the discrete case (§5.4/§6.1), strict and non-strict inequalities never need separate treatment here.

Finding an unknown constant from total area = 1

A random variable XX has probability density function f(x)={kx20⩽x⩽20otherwisef(x) = \begin{cases} kx^2 & 0\leqslant x\leqslant2 \\ 0 & \text{otherwise} \end{cases} Find the value of the constant kk.

Show full working
  1. 1

    Step 1 — write the total-area condition for this support. Since f(x)=0f(x)=0 outside [0,2][0,2], the whole area is the integral from 00 to 22: ∫02kx2 dx=1\int_0^2 kx^2\,dx = 1

  2. 2

    Step 2 — integrate, treating kk as a constant multiplier. k[x33]02=1k\left[\frac{x^3}{3}\right]_0^2 = 1

  3. 3

    Step 3 — substitute the limits. k(83−0)=1k\left(\frac{8}{3}-0\right) = 1

  4. 4

    Step 4 — solve for kk. 8k3=1⟹k=38\frac{8k}{3} = 1 \quad\Longrightarrow\quad k = \frac38

Answer

k=38k = \dfrac38.

Write the total-area equation with the correct support limits before integrating anything — an unknown constant is found from exactly one equation, so getting the limits right the first time avoids redoing the whole integral.

The same idea with a second unknown left in the answer

9709/62 M/J 2025 Q7(a)3 marks

The random variable XX has probability density function f(x)={kx2a20⩽x⩽a0otherwisef(x) = \begin{cases} \dfrac{kx^2}{a^2} & 0\leqslant x\leqslant a \\ 0 & \text{otherwise} \end{cases} where kk and aa are positive constants. Show that k=3ak=\dfrac{3}{a}.

Show full working
  1. 1

    Step 1 — write the total-area condition. ∫0akx2a2 dx=1\int_0^a \frac{kx^2}{a^2}\,dx = 1

  2. 2

    Step 2 — take the constant ka2\dfrac{k}{a^2} outside the integral, then integrate x2x^2. ka2[x33]0a=1\frac{k}{a^2}\left[\frac{x^3}{3}\right]_0^a = 1

  3. 3

    Step 3 — substitute the limits. ka2×a33=1\frac{k}{a^2}\times\frac{a^3}{3} = 1

  4. 4

    Step 4 — simplify the power of aa (a3÷a2=aa^3\div a^2=a), then solve for kk. ka3=1⟹k=3a\frac{ka}{3} = 1 \quad\Longrightarrow\quad k = \frac{3}{a}

Answer

k=3ak=\dfrac3a, as required.

When the answer is a 'show that', every intermediate line — the integrated expression, the substituted limits, the simplification — must actually appear; quoting only the final equality earns no method credit.

Verifying a pdf from a graph — no calculus needed

9709/61 M/J 2023 Q2(a)2 marks

The graph of the function ff is a straight line segment from (0,0)(0,0) to (2,1)(2,1). Show that ff could be a probability density function.

The figure printed with the question: f is the line segment from (0, 0) to (2, 1), and zero everywhere else.

The figure printed with the question: f is the line segment from (0, 0) to (2, 1), and zero everywhere else.

Show full working
  1. 1

    Step 1 — recall what has to be shown. "Could be a pdf" means exactly two things, and the mark scheme awards one mark for each: f(x)⩾0f(x)\geqslant0 everywhere, and the total area under the graph is 11. So the answer needs both, not just the more interesting one.

    Candidates routinely compute the area, get 1, and stop — throwing away the second mark. The non-negativity line is one sentence and is worth the same as the whole area calculation.

  2. 2

    Step 2 — check non-negativity. The segment runs from (0,0)(0,0) up to (2,1)(2,1), so it lies on or above the xx-axis for every xx in [0,2][0,2], and f(x)=0f(x)=0 outside. Hence f(x)⩾0f(x)\geqslant0 for all xx — the first condition holds.

  3. 3

    Step 3 — identify the shape under the graph. From (0,0)(0,0) to (2,1)(2,1) with the xx-axis beneath it, the region is a right-angled triangle: base along the xx-axis of length 22, and vertical height 11 at x=2x=2.

  4. 4

    Step 4 — find the total area with the triangle formula. Area=12×base×height=12×2×1=1\text{Area} = \frac12\times\text{base}\times\text{height} = \frac12\times2\times1 = 1

    The mark scheme explicitly allows either ½ × 2 × 1 or ∫₀² ½x dx — so the geometry route earns full marks and takes one line instead of four. Recognising the shape first can save the whole calculation.

  5. 5

    Step 5 — state the conclusion. The total area is exactly 11 and f(x)⩾0f(x)\geqslant0 throughout, so both defining conditions are satisfied and ff could be a probability density function.

Answer

f(x)⩾0f(x)\geqslant0 everywhere, and the area under the graph is 12×2×1=1\frac12\times2\times1=1 — so ff satisfies both conditions and could be a pdf.

Before reaching for an integral, check whether the region under the graph is a standard shape — a triangle, a rectangle, a semicircle — whose area formula is faster and less error-prone than setting up ∫f(x)dx from scratch. And on any 'show it could be a pdf' part, always write both conditions down.

The same two conditions, used in reverse to find an unknown

9709/61 M/J 2023 Q2(b)2 marks

The graph of the function gg is a semicircle, centre (0,0)(0,0), entirely above the xx-axis. Given that gg is a probability density function, find the radius of the semicircle.

The figure printed with the question: g is the upper half of a circle centred at the origin — so its radius is also the half-width of the support.

The figure printed with the question: g is the upper half of a circle centred at the origin — so its radius is also the half-width of the support.

Show full working
  1. 1

    Step 1 — see which way round the question runs. The previous part checked that the area was 11. Here we are told gg is a pdf, so the area is 11 — and that single fact is what pins down the one unknown, the radius.

    This is the standard move of the whole section: 'total area = 1' is one equation, so it determines exactly one unknown. Whether that unknown is a constant k or a radius r makes no difference to the method.

  2. 2

    Step 2 — write the area of the region in terms of the unknown. Call the radius rr. The graph is the upper half of a circle of radius rr, so the region between it and the xx-axis is a semicircle, of area 12πr2\frac12\pi r^2.

  3. 3

    Step 3 — set that area equal to 11. 12πr2=1\frac12\pi r^2 = 1

  4. 4

    Step 4 — multiply both sides by 22, then divide by π\pi. πr2=2⟹r2=2π\pi r^2 = 2 \quad\Longrightarrow\quad r^2 = \frac{2}{\pi}

  5. 5

    Step 5 — take the square root, keeping the positive root, since a radius cannot be negative. r=2π≈0.798r = \sqrt{\frac{2}{\pi}} \approx 0.798

Answer

r=2π≈0.798r=\sqrt{\dfrac2\pi}\approx0.798 (3 s.f.).

A circular or semicircular density looks alarming but never needs integration — it is there precisely so that the area formula does the work. Note also that the semicircle's radius doubles as the support: this X runs from −r to r.

Using symmetry, without any integration at all

If a pdf's graph is symmetric about some vertical line x=cx=c, then any two intervals that reflect onto each other across x=cx=c automatically have equal probability. Two facts follow, and between them they answer a surprising number of questions with no integration at all:

  1. Mirror-image intervals have equal probability. Measure each interval's distance from the line x=cx=c; if they match, so do the probabilities.
  2. Each side of the line carries exactly half the area. So everything below x=cx=c has probability 12\tfrac12, and so does everything above it.

A quick invented case to see both at work. Suppose ff is symmetric about x=6x=6 and you are told P(6<X<9)=0.31P(6<X<9)=0.31.

  • The interval from 66 to 99 reaches 33 units above the centre. Its mirror image reaches 33 units below, i.e. from 33 to 66 — so P(3<X<6)=0.31P(3<X<6)=0.31 as well, by fact 1.
  • The whole left half, below 66, has probability 12\tfrac12 by fact 2. The piece of it from 33 to 66 accounts for 0.310.31, so what remains — the tail below 33 — must be P(X<3)=0.5−0.31=0.19P(X<3) = 0.5-0.31 = 0.19
  • And then P(X>3)=1−0.19=0.81P(X>3) = 1-0.19 = 0.81.

No integral, and indeed no formula for ff was ever needed. The worked example below is the same two moves on a real paper.

A probability found purely from symmetry

9709/62 F/M 2024 Q6(a)2 marks

The graph of the probability density function ff of a random variable XX is symmetrical about the line x=2x=2. It is given that P(2<X<5)=117256P(2<X<5)=\dfrac{117}{256}. Using only this information, show that P(X>−1)=245256P(X>-1)=\dfrac{245}{256}.

Show full working
  1. 1

    Step 1 — use symmetry about x=2x=2 to find the reflection of the given interval. The interval 22 to 55 is 33 units to the right of the line of symmetry; reflecting it across x=2x=2 gives the interval −1-1 to 22, 33 units to the left. By symmetry, these have equal probability: P(−1<X<2)=P(2<X<5)=117256P(-1<X<2) = P(2<X<5) = \frac{117}{256}

  2. 2

    Step 2 — use the fact that the two halves of the total area, split at the line of symmetry x=2x=2, are each exactly 12\dfrac12. So the "far tail" beyond −1-1 (i.e. X⩽−1X\leqslant-1) has probability 12−117256=128256−117256=11256\frac12-\frac{117}{256} = \frac{128}{256}-\frac{117}{256} = \frac{11}{256}

    Since x = 2 is the centre of symmetry, exactly half the total area of 1 lies on each side of it — the interval from −1 to 2 accounts for part of the left half, and whatever's left over is the tail beyond −1.

  3. 3

    Step 3 — assemble P(X>−1)P(X>-1) as the complement of that far tail. P(X>−1)=1−11256=245256P(X>-1) = 1-\frac{11}{256} = \frac{245}{256}

Answer

P(X>−1)=245256P(X>-1)=\dfrac{245}{256}, as required.

Reflecting an interval across a stated line of symmetry, then using 'each half of the curve has area ½', very often answers a probability question with no integral at all — look for this before setting one up.

Sketching the density of a linear transformation

§6.2 gave the mean and variance of Y=aX+bY=aX+b in terms of XX's. The graph of YY's density follows the same idea, and Cambridge asks for it directly as a sketch. Every value xx that XX could take becomes the value ax+bax+b for YY, so take XX's density graph and, in this order (for a positive constant aa):

  1. stretch it horizontally by a factor of aa — the ×a\times a happens to xx first, so the support's width multiplies by aa;
  2. then shift it horizontally by bb — the +b+b is applied to the already-stretched picture, sliding it right if b>0b>0 and left if b<0b<0;
  3. and divide its height by aa — this is the part that's easy to forget, and it's forced by the total area staying equal to 11: the stretch multiplied the width by aa, so the height must be divided by aa to leave width ×\times height unchanged.

The order in steps 1 and 2 matters. Stretching first and then shifting sends xx to ax+bax+b, which is what YY actually is. Shifting first and then stretching would send xx to a(x+b)=ax+aba(x+b)=ax+ab — a different graph, in the wrong place.

Y = 2X − 1: the graph stretches by 2 and shrinks by ½ in height040.25f(x) — X has constant density on [0, 4]−170.125g(y) — Y = 2X − 1, on [−1, 7]width × height stays 1 on both sides: 4 × 0.25 = 1, and 8 × 0.125 = 1

Y = 2X − 1: the support doubles in width (4 → 8) and the height halves (0.25 → 0.125) — area stays exactly 1 either way.

Sketching g(y) for Y = aX + b from a known f(x)

A random variable XX has the constant density f(x)=0.25f(x)=0.25 for 0⩽x⩽40\leqslant x\leqslant4 (and 00 elsewhere). The random variable Y=2X−1Y=2X-1. State the probability density function of YY, and describe how its graph compares with ff's.

Show full working
  1. 1

    Step 1 — find the new support, mapping each endpoint of XX's support through y=2x−1y=2x-1. x=0  ↦  y=2(0)−1=−1,x=4  ↦  y=2(4)−1=7x=0 \;\mapsto\; y=2(0)-1=-1, \qquad x=4 \;\mapsto\; y=2(4)-1=7 So YY takes values on [−1,7][-1,7].

    Every x-value maps through the same rule that defines Y — the endpoints of the support are not a special case, they transform exactly like every other point.

  2. 2

    Step 2 — find the new width, and compare with the old. Old width =4−0=4=4-0=4; new width =7−(−1)=8=7-(-1)=8 — exactly double, matching the multiplier a=2a=2.

  3. 3

    Step 3 — find the new height, using the fact that width ×\times height must still equal 11. new height×8=1⟹new height=18=0.125\text{new height} \times 8 = 1 \quad\Longrightarrow\quad \text{new height} = \frac18 = 0.125

    This is exactly the old height (0.25) divided by a = 2 — doubling the width and halving the height are the same statement, both forced by total area = 1.

  4. 4

    Step 4 — write the density of YY explicitly. g(y)=0.125,−1⩽y⩽7g(y) = 0.125, \qquad -1\leqslant y\leqslant7

  5. 5

    Step 5 — describe the graph. Compared with ff, gg's graph is the same flat shape, shifted and stretched: twice as wide, positioned from −1-1 to 77 instead of 00 to 44, and exactly half as tall.

Answer

g(y)=0.125g(y)=0.125 for −1⩽y⩽7-1\leqslant y\leqslant7 — twice as wide and half as tall as ff.

This 'sketch the transformed density' question is common with X's own graph given only as a picture (often a symmetric quadratic or triangle) rather than a formula — the same three moves apply regardless of the shape: map the endpoints through y = ax + b, stretch the width by a, and divide the height by a to keep the area at 1.

When you're given the mean and variance instead of the rule

The exam more often gives you the effect of the transformation — a new mean and a new variance — and leaves you to deduce the picture. Two facts do all the work, and they are worth learning as a pair:

  • The mean says where the graph sits. Changing E(X)E(X) slides the whole graph sideways without altering its shape.
  • The variance says how wide it is. Variance is measured in squared units, so it is the standard deviation — the square root — that scales with width. Multiply the variance by 14\frac14 and the width halves, because 14=12\sqrt{\tfrac14}=\tfrac12.

Then, as always, the height moves opposite to the width so that the area stays 11: halve the width and the height doubles.

Sketching a transformed density from its new mean and variance

9709/61 O/N 2022 Q6(a)–(b)3 marks

The diagram shows the graph of the probability density function of a random variable XX that takes values between −1-1 and 33 only. The graph is symmetrical about the line x=1x=1, and between x=−1x=-1 and x=3x=3 it is a quadratic curve with maximum height 0.3750.375 at x=1x=1.

(a) The random variable SS is such that E(S)=2×E(X)E(S)=2\times E(X) and Var(S)=Var(X)Var(S)=Var(X). Sketch a quadratic graph for the probability density function of SS.

(b) The random variable TT is such that E(T)=E(X)E(T)=E(X) and Var(T)=14Var(X)Var(T)=\tfrac14 Var(X). Sketch a quadratic graph for the probability density function of TT.

The given density of X: a quadratic on [−1, 3], symmetric about x = 1, peaking at 0.375. The exam supplies blank grids of the same size to sketch on.

The given density of X: a quadratic on [−1, 3], symmetric about x = 1, peaking at 0.375. The exam supplies blank grids of the same size to sketch on.

Show full working
  1. 1

    Step 1 — read off what you need about XX itself. The graph is symmetric about x=1x=1, so E(X)=1E(X)=1. Its support is [−1,3][-1,3], so its width is 3−(−1)=43-(-1)=4, and its greatest height is 0.3750.375. Those three numbers — centre 11, width 44, height 0.3750.375 — are all that either sketch depends on.

    You are never asked for Var(X) as a number, and you could not easily find it from the picture anyway. Only the CHANGE in variance matters, so work in ratios throughout.

  2. 2

    Step 2 — (a) find SS's centre. E(S)=2×E(X)=2×1=2E(S)=2\times E(X) = 2\times1 = 2, so SS's graph is centred on s=2s=2 instead of x=1x=1 — shifted one unit to the right.

  3. 3

    Step 3 — (a) find SS's width. Var(S)=Var(X)Var(S)=Var(X) — the variance is unchanged, so the spread is unchanged and the width is still 44. A width of 44 centred on 22 runs from 2−2=02-2=0 to 2+2=42+2=4.

    Equal variance means this is a pure slide, with no stretching at all. Students often assume that because the mean doubled the graph must also stretch — but the mean and the variance are independent instructions, and only the variance controls width.

  4. 4

    Step 4 — (a) find SS's height. The width did not change, so the height must not change either, or the area would stop being 11. The peak stays at height 0.3750.375, now sitting above s=2s=2.

  5. 5

    Step 5 — (a) describe the sketch. The same quadratic shape, from s=0s=0 to s=4s=4, touching the axis at both ends, with its maximum at the point (2, 0.375)(2,\,0.375).

  6. 6

    Step 6 — (b) find TT's centre. E(T)=E(X)=1E(T)=E(X)=1, so this graph does not move — it stays centred on 11.

  7. 7

    Step 7 — (b) find TT's width. Var(T)=14Var(X)Var(T)=\frac14Var(X). Width follows the standard deviation, not the variance, so take the square root: 14=12\sqrt{\tfrac14} = \tfrac12 The width is halved, from 44 to 22. A width of 22 centred on 11 runs from 1−1=01-1=0 to 1+1=21+1=2.

    This square root is the single most common error in the question. Scaling the width by ¼ instead of ½ would give the support [0.5, 1.5] — a graph the mark scheme rejects outright.

  8. 8

    Step 8 — (b) find TT's height. The width halved, so the height must double to keep the area at 11: 0.375×2=0.750.375\times2 = 0.75

  9. 9

    Step 9 — (b) describe the sketch. The same quadratic shape but narrower and taller, from t=0t=0 to t=2t=2, with its maximum at the point (1, 0.75)(1,\,0.75).

Answer

(a) A quadratic curve from s=0s=0 to s=4s=4, maximum at (2, 0.375)(2,\,0.375). (b) A quadratic curve from t=0t=0 to t=2t=2, maximum at (1, 0.75)(1,\,0.75).

Reduce every sketch of this type to three numbers — centre, width, height — and get them in that order. Centre comes from the mean; width from the SQUARE ROOT of the variance ratio; height from 'area must stay 1'. The mark scheme here awards the endpoints and the coordinates of the maximum, so label those explicitly on your sketch and don't worry about drawing a beautiful curve.

The grid the exam gives you for part (a). It is printed at the same scale as the original — $s$ from $-2$ to $5$ — so your curve must start on the axis at $s=0$, return to it at $s=4$, and peak at $(2,\,0.375)$, a shade over a third of the way up to the marked $1$.

The grid the exam gives you for part (a). It is printed at the same scale as the original — ss from −2-2 to 55 — so your curve must start on the axis at s=0s=0, return to it at s=4s=4, and peak at (2, 0.375)(2,\,0.375), a shade over a third of the way up to the marked 11.

The identical grid for part (b). Here the curve is squeezed into $0\leqslant t\leqslant2$ and stretched upwards to a peak of $(1,\,0.75)$ — visibly narrower and twice as tall as the one above, which is exactly the point the examiner is checking.

The identical grid for part (b). Here the curve is squeezed into 0⩽t⩽20\leqslant t\leqslant2 and stretched upwards to a peak of (1, 0.75)(1,\,0.75) — visibly narrower and twice as tall as the one above, which is exactly the point the examiner is checking.

Common mistakes
  • Integrating over limits that don't match the pdf's actual support

    Always integrate over exactly the interval where f(x)f(x) is non-zero, as stated in the question

    The density is 0 outside its support by definition — integrating too far (or not far enough) silently adds or omits area that was never really there.

  • Treating f(c)f(c), the height of the density at a point, as if it were P(X=c)P(X=c)

    P(X=c)=0P(X=c)=0 always, for any continuous XX — f(c)f(c) is a density, not a probability

    f(x) can even be greater than 1 at some points; it only becomes a probability once it's integrated over a range with actual width.

  • When sketching Y=aX+bY=aX+b's density, scaling the height by aa instead of 1a\dfrac1a

    Width multiplies by aa; height divides by aa — they move in opposite directions, so the area stays 11

    Scaling both width and height by a would multiply the total area by a², so the total probability would come out as a² instead of 1 — right only in the trivial case a = 1. Whenever you sketch a transformed density, check the area of your sketch is still 1 before moving on.

Finding an unknown constant, or checking a pdf — start to finish
  1. 1

    Check f(x)⩾0f(x)\geqslant0 across the stated support (or find the support from where a given expression is ⩾0\geqslant0).

  2. 2

    Check for a standard area shape first — a rectangle, triangle or semicircle's formula beats setting up an integral.

  3. 3

    Otherwise, integrate f(x)f(x) over the support and set the result equal to 11.

  4. 4

    Solve for the unknown constant.

  5. 5

    Check for symmetry before doing any further work — it can hand you E(X)E(X) (§03) or a probability (this section) for free.

Your turn

  1. 1

    A random variable XX has probability density function f(x)=kxf(x)=kx for 0⩽x⩽60\leqslant x\leqslant6, and f(x)=0f(x)=0 otherwise. Find the value of kk.

    Stuck? Show hint

    Write the total-area condition ∫₀⁶ kx dx = 1, integrate, then solve for k.

    Show solution
    1. 1

      Step 1 — write the total-area condition over the support [0,6][0,6]. ∫06kx dx=1\int_0^6 kx\,dx = 1

    2. 2

      Step 2 — take the constant kk outside and integrate xx. k[x22]06=1k\left[\frac{x^2}{2}\right]_0^6 = 1

    3. 3

      Step 3 — substitute the limits. k(362−02)=1⟹18k=1k\left(\frac{36}{2}-\frac{0}{2}\right) = 1 \quad\Longrightarrow\quad 18k = 1

    4. 4

      Step 4 — solve for kk. k=118k = \frac{1}{18}

    5. 5

      Step 5 — sanity-check with the triangle. The region under ff is a right-angled triangle of base 66 and height f(6)=6kf(6)=6k, so its area is 12×6×6k=18k\frac12\times6\times6k = 18k — the same 18k18k, confirming Step 3.

    Answer

    k=118k=\dfrac{1}{18}.

  2. 2

    The probability density function of XX is symmetric about x=10x=10. Given P(X<7)=0.18P(X<7)=0.18, find P(X>13)P(X>13).

    Stuck? Show hint

    Reflect the interval "X < 7" (i.e. more than 3 below the centre) across x = 10 to find its mirror-image interval.

    Show solution
    1. 1

      Step 1 — measure the given interval from the line of symmetry. The centre is x=10x=10, and 77 is 10−7=310-7=3 units to its left. So "X<7X<7" means "XX is more than 33 units below the centre".

    2. 2

      Step 2 — reflect that description across x=10x=10. Three units to the right of the centre is 10+3=1310+3=13, so the mirror image of "X<7X<7" is "X>13X>13".

    3. 3

      Step 3 — equate the two probabilities. Reflected intervals enclose mirror-image regions under a symmetric curve, so they have equal area: P(X>13)=P(X<7)=0.18P(X>13) = P(X<7) = 0.18

    Answer

    P(X>13)=0.18P(X>13)=0.18.

  3. 3

    A random variable XX has the constant density f(x)=0.25f(x)=0.25 for 2⩽x⩽62\leqslant x\leqslant6 (and 00 elsewhere). The random variable W=3X+1W=3X+1. State the probability density function of WW, including its support.

    Stuck? Show hint

    Map the endpoints x = 2 and x = 6 through w = 3x + 1 to find W's support, then use width × height = 1 to find the new height.

    Show solution
    1. 1

      Step 1 — map each endpoint of XX's support through w=3x+1w=3x+1. x=2  ↦  w=3(2)+1=7,x=6  ↦  w=3(6)+1=19x=2 \;\mapsto\; w=3(2)+1 = 7, \qquad x=6 \;\mapsto\; w=3(6)+1 = 19 So WW's support is [7,19][7,19].

    2. 2

      Step 2 — compare the widths. Old width =6−2=4=6-2=4; new width =19−7=12=19-7=12. That is 33 times as wide, matching the multiplier a=3a=3 — a useful check that the endpoints were mapped correctly.

    3. 3

      Step 3 — find the new height from width ×\times height =1=1. The density is still flat (stretching and sliding a horizontal line leaves it horizontal), so the graph is still a rectangle: height×12=1⟹height=112\text{height}\times12 = 1 \quad\Longrightarrow\quad \text{height} = \frac{1}{12}

    4. 4

      Step 4 — check against the rule. This is the old height 0.250.25 divided by a=3a=3: 0.253=112\dfrac{0.25}{3}=\dfrac{1}{12} ✓ — the width tripled, so the height was divided by three.

    Answer

    g(w)=112g(w)=\dfrac{1}{12} for 7⩽w⩽197\leqslant w\leqslant19.

Practise pdf properties and finding constants from real Paper 6 papersReal past-paper questions · Probability density functions and their properties

The rest of this note

Checking your access…

Can you do all of these?

  • P(X = c) = 0 for any continuous X — strict and non-strict inequalities never need separate treatment here

  • Integrate only over the interval where f(x) is actually non-zero — its stated support

  • Check for symmetry first — it can give E(X) and the median directly, with no integration at all

  • Only when the density is constant is probability interval width ÷ support width — any other shape needs the integral

  • Find E(X²) as its own separate integral, never by squaring E(X)

  • The median solves ∫f(x)dx = ½ from the lower support boundary — always check any algebraic root actually lies inside the support

  • The same equation with ½ replaced by any q locates any percentile — nothing else about the method changes, and the unknown can be the lower limit as easily as the upper

  • Write the rejection down. Whenever a quadratic or cubic gives two or more roots, say which you reject and why — that sentence is worth a mark on its own

  • Multiply the x in before choosing a method: x × 1/x² becomes 1/x (a logarithm), while x × cos πx cancels to nothing and needs integration by parts with u = x

  • P(X ⩽ m) = ½ by definition, with no work — so P(E(X) ⩽ X ⩽ m) is just ½ − P(X < E(X)), provided you check which of the two is larger

  • To verify a value lies between two numbers, evaluate at BOTH ends and say that the target is between the two results — one endpoint proves nothing

  • If a part says 'without calculation' or 'without integration', reuse the earlier answer — integrating afresh scores almost nothing; after a 'hence' it usually just caps the method marks

  • Symmetry gives mean = median for free; without it, expect them to differ — a long tail pulls the mean towards it more than the median

Now do the questions
112 real Paper 6 parts from 2021–2025, sorted by difficulty, with mark schemes