WACE Mathematics Methods past exams 2020–2025, by year and topic

The rest of the syllabus

Question-level mapping for these areas is still being verified. Every official paper covering them is in the year table above.

• Unit 3 — Topic 3.1: Further differentiation and applications — Euler's number e, the limit (a^h - 1)/h and the derivative of e^x and Ae^(kx) · Derivatives of sin x and cos x and their use in practical problems · The product, quotient and chain rules and composite functions · Second derivatives, concavity, inflection, the increments formula, curve sketching and optimisation
• Unit 3 — Topic 3.2: Integrals — Indefinite integrals of x^n, e^x, sin x, cos x and f(ax - b), linearity and initial conditions · Area as a limit of sums, signed areas, additivity and linearity of definite integrals · The signed area function and the fundamental theorem of calculus · Areas under and between curves, total change from a rate, and linear motion
• Unit 3 — Topic 3.3: Discrete random variables — Discrete random variables: probability functions, expected value, variance and linear changes of scale and origin · Bernoulli trials and the binomial distribution: probabilities, mean np and variance np(1 - p)
• Unit 4 — Topic 4.1: The logarithmic function — Logarithms as indices, the log laws and solving index equations · Graphs of y = log_a x and its translations, logarithmic scales and logarithmic models · The natural logarithm: derivative of ln x and ln f(x), and integrals of 1/x and f'(x)/f(x)
• Unit 4 — Topic 4.2: Continuous random variables and the normal distribution — Probability density functions, cumulative distribution functions and probabilities as integrals · Expected value, variance and standard deviation of continuous random variables, including uniform and triangular · The normal distribution: features, standardisation, and probabilities and quantiles with technology
• Unit 4 — Topic 4.3: Interval estimates for proportions — Random samples, sources of bias and variability of samples from uniform, normal and Bernoulli distributions · The sample proportion p-hat as a random variable: mean, standard deviation and approximate normality · Confidence intervals for a proportion and the margin of error · Sample size, confidence level and the interpretation of repeated confidence intervals by simulation