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Mathematical Methods · QCAA

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Full Mathematical Methods syllabus

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142 LOs
1 — Surds, algebra, functions and probability40 LOs
Surds and quadratic functions9 LOs
Surds4 LOs
Quadratic functions5 LOs
Binomial expansion and cubic functions7 LOs
Functions and relations6 LOs
Trigonometric functions8 LOs
Circular measure and radian measure2 LOs
Introduction to trigonometric functions6 LOs
Probability10 LOs
Language of events and sets3 LOs
Conditional probability and independence7 LOs
Model and solve problems that involve probability, with and without technology.Understand and use the notion of independence of an event 𝐴 from an event 𝐵, as defined by 𝑃(𝐴|𝐵) = 𝑃(𝐴).Understand the notion of a conditional probability and recognise and use language that indicates conditionality.Use relative frequencies obtained from data as point estimates of conditional probabilities and as indications of possible independence of events.Use the formula 𝑃(𝐴 ∩ 𝐵) = 𝑃(𝐴)𝑃(𝐵) for independent events 𝐴 and 𝐵.Use the notation 𝑃(𝐴|𝐵) and the formula 𝑃(𝐴 ∩ 𝐵) = 𝑃(𝐴|𝐵)𝑃(𝐵) to solve problems.Use the rules 𝑃(𝐴) = 1 − 𝑃(𝐴) and 𝑃(𝐴 ∪ 𝐵) = 𝑃(𝐴) + 𝑃(𝐵) − 𝑃(𝐴 ∩ 𝐵).
2 — Calculus and further functions28 LOs
Exponential functions6 LOs
Logarithms and logarithmic functions6 LOs
Logarithms and logarithmic laws3 LOs
Logarithmic functions3 LOs
Introduction to differential calculus7 LOs
Applications of differential calculus5 LOs
Further differentiation4 LOs
Differentiation rules4 LOs
Solve problems that involve combinations of the chain rule, product rule and quotient rule to differentiate functions involving power and polynomial functions, expressing derivatives in simplest and factorised form.Use the chain rule, if 𝑦 = 𝑓(𝑢) and 𝑢 = 𝑔(𝑥) then 𝑑𝑦 𝑑𝑥 = 𝑑𝑦 𝑑𝑢 × 𝑑𝑢 𝑑𝑥, to determine the derivative of composite functions involving power and polynomial functions.Use the product rule, 𝑑(𝑢𝑣) 𝑑𝑥 = 𝑢 𝑑𝑣 𝑑𝑥 + 𝑣 𝑑𝑢 𝑑𝑥, to determine the derivative of products of functions involving power and polynomial functions.Use the quotient rule, 𝑑(𝑢 𝑣) 𝑑𝑥 = 𝑣𝑑𝑢 𝑑𝑥−𝑢𝑑𝑣 𝑑𝑥 𝑣2, to determine the derivative of quotients of functions involving power and polynomial functions.
3 — Further calculus and introduction to statistics43 LOs
Differentiation of exponential and logarithmic functions5 LOs
Differentiation of trigonometric functions and differentiation rules7 LOs
Calculus of trigonometric functions3 LOs
Differentiation rules4 LOs
Further applications of differentiation5 LOs
Introduction to integration11 LOs
Anti-differentiation11 LOs
Determine displacement given acceleration and initial values of displacement and vel ocity.Determine displacement given velocity and the initial value of displacement.Determine 𝑓(𝑥) given 𝑓′(𝑥) and an initial condition 𝑓(𝑎) = 𝑏.Determine indefinite integrals of the form ∫ 𝑓(𝑎𝑥 + 𝑏)𝑑𝑥.Model and solve problems that involve indefinite integrals, with and without technology. Mathematical Methods 2025 v1.3Understand and use the formulas ∫(𝑓(𝑥) + 𝑔(𝑥))𝑑𝑥 = ∫ 𝑓(𝑥)𝑑𝑥 + ∫ 𝑔(𝑥) 𝑑𝑥 and ∫ 𝑘 𝑓(𝑥)𝑑𝑥 = 𝑘 ∫ 𝑓(𝑥)𝑑𝑥.Use the formula ∫ 1 𝑥 𝑑𝑥 = ln(𝑥) + 𝑐, for 𝑥 > 0.Use the formula ∫ 𝑒𝑥 𝑑𝑥 = 𝑒𝑥 + 𝑐.Use the formula ∫ 𝑥𝑛 𝑑𝑥 = 𝑥𝑛+1 𝑛+1 + 𝑐 for 𝑛 ≠ − 1.Use the formulas ∫ sin (𝑥) 𝑑𝑥 = − cos(𝑥) + 𝑐 and ∫ cos(𝑥) 𝑑𝑥 = sin (𝑥) + 𝑐.Use the notation ∫ 𝑓(𝑥) 𝑑𝑥 for anti-derivatives or indefinite integrals.
Discrete random variables15 LOs
General discrete random variables6 LOs
Determine and use the mean (expected value) of a discrete random variable as a measurement of centre, 𝐸(𝑋) = 𝜇 = ∑ 𝑝𝑖 𝑥𝑖 where 𝑝𝑖 is the probability of outcome 𝑥𝑖 occurring.Determine and use the standard deviation of a discrete random variable, √𝑉𝑎𝑟(𝑋), as a measure of spread.Determine and use the variance of a discrete random variable as a measure of spread, 𝑉𝑎𝑟 (𝑋) = ∑ 𝑝𝑖 (𝑥𝑖 − 𝜇)2 where 𝑝𝑖 is the probability of outcome 𝑥𝑖 occurring, 𝜇 is the mean.Model and solve problems that involve discrete random variables and associated probabilities, with and without technology.Understand the concepts of a discrete random variable and its associat ed probability function, and its use in modelling data.Use relative frequencies obtained from data to determine point estimates of probabilities associated with a discrete random variable.
Bernoulli distributions3 LOs
Binomial distributions6 LOs
Calculate the mean 𝑛𝑝 and variance 𝑛𝑝(1 − 𝑝) of a binomial distribution using technology and algebraic methods.Determine and use the probabilities 𝑃(𝑋 = 𝑟) = (𝑛 𝑟) 𝑝𝑟 (1 − 𝑝)𝑛−𝑟 associated with the binomial distribution with parameters 𝑛 and 𝑝.Identify contexts suitable for modelling by binomial random variables.Model and solve problems that involve binomial distributions and associated probabilities with and without technology.Understand the concepts of Bernoulli trials and the concept of a binomial rand om variable as the number of ‘successes’, 𝑟, in 𝑛 independent Bernoulli trials, with the same probability of success 𝑝 in each trial.Use the language of probability, including at most, at least, no more than, no less than, inclusive and between.
4 — Further calculus, trigonometry and statistics31 LOs
Further integration8 LOs
Fundamental theorem of calculus and definite integrals3 LOs
Applications of integration5 LOs
Trigonometry4 LOs
Continuous random variables and the normal distribution8 LOs
General continuous random variables5 LOs
Calculate the expected value, 𝐸 (𝑋) = 𝜇 = ∫ 𝑥𝑝(𝑥) 𝑑𝑥 ∞ −∞, of a continuous random variable where 𝑝(𝑥) is the probability density function.Calculate the variance, 𝑉𝑎𝑟 (𝑋) = 𝜎 2 = ∫ (𝑥 − 𝜇) ∞ −∞ 2 𝑝(𝑥)𝑑𝑥, and standard deviation 𝜎, of a continuous random variable.Understand standardised normal variables (𝑧-values, 𝑧-scores) and use these to compare samples.Understand the concepts of a probability density function, cumulative distribution function, and probabilities associated with a continuous random variable given by integrals; examine simple types of continuous random variables and use them in appropriate contexts.Use relative frequencies and histograms obtained from data to estimate probabilities associated with a continuous random variable.
Normal distributions3 LOs
Sampling and proportions5 LOs
Interval estimates for proportions6 LOs
Confidence intervals for proportions6 LOs
Model and solve problems that involve interval estimates for proportions, with and without technology.Understand and use the approximate confidence interval, (𝑝̂ − 𝑧√𝑝̂(1−𝑝̂) 𝑛, 𝑝̂ + 𝑧√𝑝̂(1−𝑝̂) 𝑛), as an interval estimate for 𝑝, the population proportion, where 𝑧 is the appropriate quantile for the standard normal distribution.Understand and use the approximate margin of error, 𝑧√𝑝̂(1−𝑝̂) 𝑛.Understand and use the relationship between margin of error, level of confidence and sample size.Understand that there are variations in confidence intervals between samples and that most, but not all, confidence intervals contain 𝑝.Understand the concept of an interval estimate for a parameter associated with a random variable.

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