ECON20003 · Quantitative Methods 2
The Complete Exam Bible · S1 2026Quantitative Methods 2
ECON20003 Quantitative Methods 2 (QM2) is the University of Melbourne's second-year business statistics and applied-econometrics subject. It teaches you to identify the right quantitative technique, run it in R/RStudio, and interpret the results — spanning estimation and hypothesis testing of a mean, normality diagnostics and nonparametric tests, comparing two populations, inferences on variances and proportions, ANOVA, χ² frequency analysis, simple and multiple regression with the general F-test, heteroskedasticity and dummy variables, binary logit/probit models, cross-validation with ridge and LASSO, and time-series regression with autocorrelation and unit-root testing.
It is assessed by three assignments (10% each), a mid-semester test (10%), tutorial participation and weekly homework (10%), and a closed-book end-of-semester exam worth 50% that is also a hurdle — you must pass the exam to pass the subject, even if your coursework already totals 50%. The exam supplies a formula sheet identical to the Canvas one plus statistical tables, so the marks come not from memorising formulas but from choosing the right test among look-alikes, reading R printouts correctly, and writing the full five-step reasoning chain.
What ECON20003 covers
The whole subject → one exam-ready map. Each topic links to its free chapter guide.
How ECON20003 is assessed
| Component | Weight | Format |
|---|---|---|
| First Assignment (individual or group of 2-4) | 10% | Quantitative analysis using a calculator and/or R/RStudio; typed solutions plus R code/printouts uploaded to Canvas as a single PDF; due 10am Mon 30 Mar (Week 5) |
| Mid-semester test (individual) | 10% | In-person, Wilson Hall (details TBA); held Fri 17 Apr |
| Second Assignment (individual or group of 2-4) | 10% | As Assignment 1; due 10am Mon 4 May (Week 9) |
| Third Assignment (individual or group of 2-4) | 10% | As Assignment 1; due 10am Mon 18 May (Week 11) |
| Tutorial participation & homework exercises (individual) | 10% | Weekly Canvas Homework Quizzes; one mark per week for attending + participating AND submitting the prior week's homework by 10am Wed, on at least 10 weeks |
| End-of-semester exam (individual) · hurdle | 50% | Closed-book, in-person; 2h writing + 15 min reading; THREE long-answer questions each with several tasks, covering all 12 weeks; a formula sheet identical to the Canvas one plus statistical tables (z, t, χ², F, Wilcoxon/Durbin-Watson) are provided; an approved Casio FX-82 calculator (any suffix). HURDLE: you must pass the exam to pass the subject |
Dates, weights and the test venue are indicative (from a recent Semester 1 subject guide) and are subject to confirmation — always check your own unit outline and Canvas/LMS for the authoritative timetable.
One-sample t-test for a mean (closed-book, formula-sheet style)
- 2 marksState the hypotheses for a one-tailed (right) test, because the concern is exceeding the target: H₀: μ = 12.0 versus H₁: μ > 12.0.
- 1 markChoose the test statistic. The population standard deviation σ is unknown, so use the t-statistic with df = n − 1 = 24.
- 1 markCompute the standard error: S/√n = 1.5/√25 = 1.5/5 = 0.30 min.
- 2 marksCompute the test statistic: t = (X̄ − μ₀)/(S/√n) = (12.8 − 12.0)/0.30 = 0.8/0.30 = 2.67.
- 1 markApply the decision rule. The right-tail critical value t₀.₀₅,₂₄ = 1.711. Since 2.67 > 1.711, the statistic falls in the rejection region (equivalently the p-value < 0.05).
- 1 markConclude in context: reject H₀. There is significant evidence at the 5% level that the mean parcel-sorting time exceeds the 12-minute target.
Key terms
- Point-estimator properties
- Three things we want of an estimator θ̂ of a parameter θ: unbiasedness (E(θ̂) = θ), efficiency (smallest variance among unbiased estimators), and consistency (θ̂ collapses onto θ as n → ∞). The sample mean X̄ is the standard example for μ.
- Standard error (SE)
- The standard deviation of a sample statistic across all possible samples. For a sample mean it is σ/√n (or S/√n when σ is unknown); it shrinks as n grows, which is why larger samples estimate more precisely.
- Five-step hypothesis test
- The spine of every ECON20003 exam answer: (1) state H₀ and H₁, (2) give the test statistic and its distribution under H₀, (3) state the decision rule (critical value or p-value), (4) make the statistical decision, (5) write a plain-English conclusion in context. Marks attach to each step, and failing to reject H₀ is never the same as accepting it.
- Parametric vs nonparametric test
- A parametric test (t, F, ANOVA) assumes a distributional form, usually normality; a nonparametric test (sign, Wilcoxon, Kruskal-Wallis) works on ranks or signs and makes weaker assumptions. When normality fails, you switch to the matching nonparametric alternative.
- Reading an R printout
- The exam-critical skill of locating the Estimate, Std. Error, t value, Pr(>|t|) and significance codes, the residual standard error, Multiple and Adjusted R², the F-statistic and its p-value, and the diagnostic blocks (Breusch-Pagan, White, Durbin-Watson, Breusch-Godfrey, VIF, Shapiro-Wilk, Wilcoxon, Kruskal-Wallis) on a stylised RStudio output.
- Which-test decision tree
- The habit of classifying any prompt before computing: identify the data and the goal (estimate, test one mean/variance/proportion, compare two groups, ANOVA across several, model with regression, or analyse a time series), then check assumptions (normality, equal variance) to land on the correct parametric or nonparametric procedure.
ECON20003 FAQ
Is ECON20003 hard?
ECON20003 is demanding because of its breadth, not because of heavy algebra: it covers a large menu of tests (parametric and nonparametric two-sample tests, ANOVA, χ², simple and multiple regression, logit/probit, time-series and unit roots) and expects you to run and read them in R/RStudio. The good news is that the closed-book exam provides the formula sheet and statistical tables, so the difficulty is in choosing the right test, reading R printouts correctly, and writing the five-step answer — all very learnable with structured practice. The biggest risk to a pass is the exam hurdle (below).
Is the end-of-semester exam a hurdle in ECON20003?
Yes. The exam is worth 50% AND is a hurdle: you must pass the exam itself to pass the subject. That means you cannot coast on the 50% available from the assignments, the mid-semester test and tutorial marks and skip the exam — failing the exam fails the subject regardless of your coursework. This makes the exam the single highest-stakes artefact in QM2.
Is the exam open- or closed-book, and do I need to memorise the formulas?
The exam is closed-book and in-person. You may NOT bring textbooks, notes or your own copies of tables or formulas. You do not need to memorise formulas, though: the paper supplies a formula sheet identical to the one on Canvas, plus the relevant statistical tables (z, t, χ², F, and Wilcoxon/Durbin-Watson critical values). Practise with that sheet's layout, because the marks come from choosing the right procedure, substituting correctly, reading the R printout, and writing the reasoning chain — not from rote recall.
What can I bring into the exam, and which calculator is allowed?
Bring your student card, a pen (plus a spare), and an approved Casio FX-82 calculator (any suffix, e.g. FX-82AU/ES). Other models such as the Casio fx-8200 AU are not in the FX-82 series — confirm the permitted-calculator list in your unit outline / UniMelb exam rules before exam day. The formula sheet and statistical tables are provided in the paper, so you do not bring those. There is no R/RStudio in the exam room — you interpret printed R printouts by hand. The total sitting is 2 h 15 min (15 minutes reading + 120 minutes writing).
How many questions are on the exam and what does it cover?
The official ECON20003 Week-12 exam-information slide states the paper contains THREE long-answer questions, each with several tasks, and that every topic across all 12 weeks is examinable (sample exam: a two-sample location/variance question, a multiple-regression question, and a time-series question). A 'Final Exam FAQ' file circulating online actually belongs to the sibling subject ECON20004 and says 'four questions / Semester 2' — do not rely on it; the ECON20003 Week-12 slide is authoritative. Confirm the final logistics in your own unit outline and exam timetable.
How to study for the exam
Treat ECON20003 as a decision-making subject, not a memorisation subject: the formula sheet is given, so your edge comes from picking the right test and reading the R output fast and correctly. (1) Build the which-test decision tree until it is automatic — for any prompt, classify the data and the goal (estimate, test one mean/variance/proportion, compare two groups, ANOVA across several, model with regression, or analyse a time series), then check assumptions (normality via Q-Q/Shapiro-Wilk, equal variance via the F-test) to land on parametric vs nonparametric. (2) Drill the five-step ritual on every test — H₀/H₁ → statistic & its distribution → decision rule → statistical decision → contextual conclusion — because partial marks attach to each line. (3) Learn to read R printouts cold: know where the Estimate, Std. Error, t value, Pr(>|t|), residual std error, Multiple/Adjusted R², F-statistic and the diagnostic blocks (BP, White, DW, BG, VIF, Shapiro-Wilk, Wilcoxon, Kruskal-Wallis) sit, and what a small p means for each. (4) Map the provided formula sheet so exam time goes to setup, not searching. (5) Because the exam is a 50% hurdle, treat it as the priority artefact — keep up weekly with the tutorial homework that rehearses exactly these moves, and use the assignments and mid-semester test as dress rehearsals for reading R output under pressure.
