Assessment and exam practice.

Try this with General Mathematics

1. Check the task format, time limit and permitted materials with your school.
2. Start with one timed section or task and record where you spent the most time.
3. Review errors before attempting another section. Separate missing knowledge from timing problems.

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Practice paper focus areas

• Exam 1 — Time series analysis: 4-quarter centred moving averages, ratio-to-moving-average seasonal indices, deseasonalising (divide by SI), and reseasonalised next-year forecasting of Queensland seasonal rainfall across four weather stations; Bivariate data: scatterplots, Pearson's r, coefficient of determination R², least-squares regression and prediction (rainfall vs reservoir inflow); Arithmetic and geometric sequences and recurrence relations modelling annual rainfall trend and drought storage decay
• Exam 2 — Bivariate data analysis: scatterplots, Pearson's r, coefficient of determination R-squared, least-squares regression, residuals, correlation vs causation (reef health score vs water temperature); Time series analysis: trend/seasonal/cyclical/irregular components, 4-point centred moving averages, ratio-to-moving-average seasonal indices, deseasonalising, regression-based forecasting (Great Barrier Reef visitor numbers); Growth and decay in sequences: arithmetic and geometric rules and recurrence relations, simultaneous equations for unknown terms, threshold/doubling problems
• Exam 3 — Bivariate data: Pearson r, R-squared, least-squares regression, residuals (cane yield vs rainfall); Time series: trend/seasonal/cyclical/irregular, centred moving averages, seasonal indices, deseasonalising, forecasting (quarterly cane crush); Arithmetic and geometric sequences and recurrence relations (annual sugar cane harvest growth; machinery depreciation)
• Exam 4 — Unit 4 Networks: weighted directed graphs, adjacency matrices, Dijkstra's shortest path, Euler/Hamilton, TSP; Unit 4 Decision maths: minimum spanning trees (Kruskal/Prim), max-flow min-cut, Hungarian assignment, critical path analysis (forward/backward pass, float); Unit 4 Finance: compound interest with varied compounding, reducing-balance loans and amortisation, annuities (FV) and perpetuities
• Exam 5 — Project network critical path analysis: precedence table to activity network, dummy activities, forward/backward pass, EST/EFT/LST/LFT, float, critical activities, crashing; Graph and network theory: adjacency matrices, Euler's formula, Eulerian/Hamiltonian graphs, Dijkstra shortest path, travelling salesman; Decision mathematics: minimum spanning trees (Kruskal/Prim), maximum-flow minimum-cut, bipartite assignment
• Exam 6 — Bivariate data: least-squares regression, Pearson r and r-squared interpretation in context; Time series: 4-quarter centred moving averages, seasonal indices, deseasonalising actual data; Arithmetic and geometric sequences: equipment depreciation and ore-stockpile decay modelling
• Exam 7 — Unit 4 Topic 1 financial mathematics as the spine — compound interest with monthly compounding (A=P(1+r/n)^nt and recurrence A_{n+1}=(1+i)A_n), future value of a regular-deposit annuity, reducing-balance loan recurrence, and perpetuity — set in a Sunshine Coast investment-property renovation-savings context; Full seven-topic EA coverage: bivariate data (Pearson's r, R^2, least-squares line), time series (seasonal indices, deseasonalisation, centred moving averages, reseasonalised forecasts), arithmetic and geometric sequences and recurrence relations, earth geometry (parallel and meridian distances) and time zones; Networking and decision mathematics: graph terminology and degree/edge counting, Euler's formula, Dijkstra shortest path, minimum spanning tree (Kruskal/Prim), and critical path analysis with full forward/backward pass, float times and critical-activity identification
• Exam 8 — Earth geometry: latitude/longitude positions, great-circle and same-meridian/same-parallel distances using the QCAA formula (R = 6400 km); Longitude-based time differences (15 degrees = 1 hour) and International Date Line day changes across the Pacific; Bivariate data: scatterplots, Pearson's r, the coefficient of determination R squared, and least-squares regression with contextual interpretation
• Exam 9 — Geometric sequences and recurrence relations (herd growth with culling); Arithmetic sequences and series; Time series: seasonal indices, deseasonalising, moving-average smoothing, trend forecasting
• Exam 10 — Bivariate data analysis (Pearson's r, R squared, least-squares regression, prediction, correlation vs causation); Time series analysis (4-point centred moving averages, seasonal indices, deseasonalising, forecasting, seasonal vs cyclical vs structural change); Arithmetic and geometric sequences and recurrence relations (growth/decay modelling, simultaneous equations)
• Exam 11 — Bivariate data: scatterplots, Pearson's r, R-squared, least-squares regression and prediction (solar install cost vs panel count); Time series: trend/seasonal/cyclical/irregular components, 4-quarter centred moving averages, seasonal indices, deseasonalising and forecasting; Arithmetic and geometric sequences and recurrence relations modelling panel installation rates and output degradation
• Exam 12 — Networks and decision mathematics (graph terminology, adjacency matrices, Euler's formula, Eulerian and Hamiltonian paths/circuits, Dijkstra's shortest path, travelling salesman, minimum spanning trees via Kruskal's and Prim's, maximum-flow minimum-cut, critical path analysis) applied to a South-East Queensland multimodal public transport network; Bivariate data and least-squares regression, Pearson's r and R-squared; Time series analysis: trend, seasonal indices, centred moving averages, deseasonalising and forecasting
• Exam 13 — Bivariate data: Pearson's r, R-squared and least-squares regression with contextual interpretation (Q16, Q27); Time series: 3-point moving averages, seasonal indices, deseasonalising by division, and forecasting (Q17, Q18, Q26, Q27); Arithmetic and geometric sequences via recurrence relations for passenger/route growth (Q6, Q7, Q19)
• Exam 14 — Earth geometry: latitude/longitude positions, great-circle meridian arc distance (d = angle/360 x 2 pi R, R = 6400 km), three-figure bearings and reverse bearings; Time zones and the International Date Line: AEST (UTC+10) to Vanuatu (UTC+11) conversions, longitude-to-time, eastbound IDL day adjustment; Bivariate data: scatterplots, Pearson r, coefficient of determination R-squared, least-squares regression line, interpolation/extrapolation and contextual interpretation
• Exam 15 — Bivariate data: two-way frequency tables, association between categorical variables, Pearson's r, least-squares regression and R-squared evaluation (UQ research-funding context); Time series: centred moving averages, seasonal indices, deseasonalisation and forecasting of quarterly grant income; Sequences: arithmetic and geometric models with recurrence relations for research output growth and funding
• Exam 16 — Time series analysis: trend, seasonal indices, deseasonalisation and deseasonalised-trend forecasting (Queensland Health flu vaccination context); Bivariate data: Pearson's r, coefficient of determination R^2, least-squares regression, residuals and two-way tables; Arithmetic and geometric sequences and recurrence relations (dose delivery and cold-chain decay)
• Exam 17 — Sequences & recurrence relations (arithmetic/geometric, loan-balance modelling); Financial mathematics: perpetuities, annuities, reducing-balance loans, amortisation, compound interest with varied compounding; Bivariate data & least-squares regression (Pearson's r, prediction)
• Exam 18 — Bivariate data: scatterplots, Pearson's r, R-squared, least-squares regression, residuals (Mackay cable cost vs length); Time series: seasonal indices, deseasonalising, centred moving averages, trend and forecasting (desalination water demand); Arithmetic and geometric sequences and recurrence relations (cable-laying schedule, maintenance cost escalation)
• Exam 19 — Bivariate data analysis: scatterplots, Pearson's r, coefficient of determination R-squared, least-squares regression, interpolation vs extrapolation, residuals (athlete age vs finish time); Time series analysis: trend/seasonal/cyclical/irregular components, 4-point centred moving averages, seasonal indices, deseasonalising, forecasting (annual triathlon entries); Arithmetic and geometric sequences: nth-term and recurrence relations, series sums (entry growth, prize pools)
• Exam 20 — Financial mathematics: simple and compound interest, reducing-balance loans, annuities/savings plans, effective interest rates, depreciation, GST, CPI/inflation; Applied trigonometry: Pythagoras, right-angled trig, sine rule, cosine rule, area of triangles, bearings, angles of elevation/depression; Graphs and networks: minimum spanning trees, shortest path, vertex degree/handshake lemma, connected weighted graphs