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Linear functions of a DRVAQA A-Level Further Maths: Revision notes

Section 1

What a linear function does to a distribution

A linear function of a random variable has the form Y=aX+bY=aX+b. The constant aa scales every value and bb shifts every value; the probabilities are unchanged. Typical uses: converting units (∘C→∘F{}^\circ\mathrm{C}\to{}^\circ\mathrm{F}), profit == price ×\times number sold −- fixed cost, and standardising a variable. Example: if X=1,2,3,4X=1,2,3,4 with probabilities 0.3,0.4,0.2,0.10.3,0.4,0.2,0.1, then Y=5−2XY=5-2X takes the values 3,1,−1,−33,1,-1,-3 with the same probabilities.

Key termslinear functionscalingshifting
Exam tip

The probabilities attached to each outcome stay the same; only the values change.

Section 2

Expectation of aX + b

E(aX+b)=aE(X)+b.E(aX+b)=aE(X)+b. The mean is scaled and shifted in exactly the same way as the values. The proof uses the definition: E(aX+b)=∑(axi+b)pi=a∑xipi+b∑pi=aE(X)+bE(aX+b)=\sum(ax_i+b)p_i=a\sum x_ip_i+b\sum p_i=aE(X)+b, since ∑pi=1\sum p_i=1. Example: E(X)=5E(X)=5, so E(3X+2)=3(5)+2=17E(3X+2)=3(5)+2=17 and E(2−4X)=2−4(5)=−18E(2-4X)=2-4(5)=-18.

Key termsexpectation of a linear function
Common mistake

Writing E(aX+b)=a(E(X)+b)E(aX+b)=a(E(X)+b). The bb is added after multiplying by aa.

Section 3

Variance and standard deviation of aX + b

Var(aX+b)=a2Var(X).\mathrm{Var}(aX+b)=a^2\mathrm{Var}(X). A shift by bb does not change the spread, so bb disappears. Scaling by aa multiplies the spread by ∣a∣|a|, so the variance is multiplied by a2a^2. Hence the standard deviation is ∣a∣×|a|\times the standard deviation of XX. Because a2≥0a^2\ge0, a negative aa never gives a negative variance: Var(2−4X)=(−4)2Var(X)=16Var(X)\mathrm{Var}(2-4X)=(-4)^2\mathrm{Var}(X)=16\mathrm{Var}(X). Example: Var(X)=4\mathrm{Var}(X)=4, so Var(3X+2)=9×4=36\mathrm{Var}(3X+2)=9\times4=36 and the standard deviation is 66.

Key termsvariance of a linear function
Common mistake

Using aa instead of a2a^2, adding bb to the variance, or leaving a negative variance when a<0a<0.

Section 4

Worked example and checking directly

A café sells XX meals with E(X)=8.5E(X)=8.5 and Var(X)=2.25\mathrm{Var}(X)=2.25; profit T=6X−20T=6X-20. E(T)=6(8.5)−20=31E(T)=6(8.5)-20=31, Var(T)=62(2.25)=81\mathrm{Var}(T)=6^2(2.25)=81 and the standard deviation is 99. You can always check with the distribution itself: list the new values y=ax+by=ax+b with the same probabilities, then compute E(Y)=∑ypE(Y)=\sum yp and Var(Y)=E(Y2)−[E(Y)]2\mathrm{Var}(Y)=E(Y^2)-[E(Y)]^2. The results must match the formulas. To standardise, choose aa and bb so that E(Y)=0E(Y)=0 and Var(Y)=1\mathrm{Var}(Y)=1: a=1σa=\frac1\sigma and b=−μσb=-\frac\mu\sigma.

Key termsstandardise
Exam tip

Use the formulas to find mean and variance quickly, and use the table method only when asked or as a check.

That's the notes covered.

Carry on to the next subtopic.

Exam questions on Linear functions of a DRV

  1. The discrete random variable XX has E(X)=5E(X)=5 and Var(X)=4\mathrm{Var}(X)=4. The random variable YY is defined by Y=3X+2Y=3X+2.
    Find the standard deviation of 2−4X2-4X.2 marks
  2. The number of special meals, XX, sold at a café in one day has E(X)=8.5E(X)=8.5 and Var(X)=2.25\mathrm{Var}(X)=2.25. The daily profit, in pounds, is T=6X−20T=6X-20.
    The café changes its pricing so that the daily profit becomes T=8X−30T=8X-30. Find E(T)E(T) and Var(T)\mathrm{Var}(T).2 marks
  3. The noon temperature CC (∘^\circC) in a greenhouse is modelled as a discrete random variable with E(C)=22E(C)=22 and standard deviation 33. The temperature in degrees Fahrenheit is F=1.8C+32F=1.8C+32.
    Find E(F)E(F) and Var(F)\mathrm{Var}(F).3 marks
See the full worksheet

Written by the Exaim team, led by Shaun Daswani (Head of Upper Secondary, Improve ME Institute; MSc Financial Mathematics, Imperial College London; BSc, UCL) and Jason Daswani (operational lead, Improve ME Institute; LSE).