ECMT1010 · Introduction To Economic Statistics
The Complete Exam Bible · S2 2026Introduction to Economic Statistics
ECMT1010 Introduction to Economic Statistics is the University of Sydney School of Economics' first-year gateway to statistics for economists — a MEGA-enrolment unit the course itself calls "one of the harder undergraduate units at the University of Sydney," recommending 6–8 hours of private study a week. It runs a modern simulation track (the bootstrap and randomization in StatKey) alongside the classical formula/CLT track, then turns to probability theory and random variables, all built on the free Lock5 text (Statistics: Unlocking the Power of Data) and worked in StatKey and Excel.
It is assessed by a closed-book in-semester test in Week 7 (35 MCQ covering Weeks 1–6), an individual data-analysis assignment, and a final exam whose short-answer Section B is worth 50 marks. Both the test and the exam supply the formula sheet and the N(0,1) and t distribution tables in the paper — so marks are not lost on recall but on choosing the wrong test, fumbling the H₀/Hₐ setup, misreading the table for the right df and tail, or skipping the one-sentence conclusion in context. (Exact component weights and the official exam date are subject to confirmation — the LMS pull did not capture the assessment-information page or a published timetable; confirm in your unit outline.)
What ECMT1010 covers
The whole unit → one exam-ready map. Each topic links to its free chapter guide.
How ECMT1010 is assessed
| Component | Weight | Format |
|---|---|---|
| In-semester test (Week 7) | subject to confirmation | Closed-book, in-person, 1 hour writing + 5 min reading; 35 MCQ on a generalised answer sheet; non-programmable calculator; formula sheet + distribution tables supplied in the paper; covers Weeks 1–6 |
| Individual assignment | subject to confirmation | Individual data-analysis assignment using StatKey/Excel; due ~Week 11 (the captured run: 11.59pm Sydney time, Sun 24 May) |
| Final exam | subject to confirmation | In-person; short-answer Section B = 50 marks (~100 min suggested) plus an implied MCQ Section A; formula sheet + N(0,1)/t tables supplied in the paper; non-programmable calculator; covers the whole unit |
| Workshops / Muddy cards / participation | subject to confirmation | Weekly workshops (1–12) and the muddy-card feedback loop |
One-mean t-test with the provided formula sheet (final exam, Section B style)
- 2 marksDefine the parameter and state the hypotheses in population terms. Let μ = the true mean fill weight (g). Because the concern is under-filling, run a one-sided (left) test: H₀: μ = 250 versus Hₐ: μ < 250.
- 1 markChoose the right method. The population SD σ is unknown and we have one quantitative sample, so use the one-sample t-statistic with df = n − 1 = 24 (the exam supplies the t table).
- 1 markCompute the standard error: SE = s/√n = 6/√25 = 6/5 = 1.2 g.
- 2 marksCompute the test statistic: t = (x̄ − μ₀)/(s/√n) = (247.4 − 250)/1.2 = −2.6/1.2 ≈ −2.17.
- 1 markApply the decision rule. The one-sided 5% critical value from the t table is t(24) ≈ −1.711. Since −2.17 < −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 bag weight is below 250 g, so the bags appear to be under-filled.
Key terms
- Parameter vs statistic
- A parameter is a numerical summary of a whole population, written in Greek (μ, σ, p, ρ); a statistic is the matching summary computed from a sample, written in Latin (x̄, s, p̂, r). Inference uses the statistic to estimate the unknown parameter.
- Standard error (SE)
- The standard deviation of a sample statistic across all possible samples. For a sample mean it is σ/√n (estimated by s/√n); it shrinks as n grows, which is why larger samples estimate more precisely. The bootstrap estimates the SE from a single sample.
- Confidence interval
- An interval of the form statistic ± z*·SE (or statistic ± 2·SE for 95%) that, over repeated sampling, captures the true parameter a stated percentage of the time. '95% confident' refers to the long-run procedure (19 of 20 samples), not a probability about one fixed interval.
- p-value
- The probability of getting a statistic as extreme as — or more extreme than — the one observed, assuming H₀ is true. If the p-value < α you reject H₀ ('statistically significant'); if it exceeds α the result is inconclusive.
- Central Limit Theorem (CLT)
- For a large enough sample (rule of thumb n ≥ 30 for a mean; np ≥ 10 and n(1−p) ≥ 10 for a proportion) the sampling distribution of the statistic is approximately normal regardless of the population's shape, which justifies the normal- and t-based formula procedures.
ECMT1010 FAQ
Is ECMT1010 hard?
The unit itself flags it as 'one of the harder undergraduate units at the University of Sydney' and recommends 6–8 hours of private study a week on top of the lecture and workshop. The difficulty is structural, not arithmetic: it runs a modern simulation track (the bootstrap and randomization in StatKey) alongside the classical formula/CLT track and then takes a hard probability-theory turn in Weeks 11–13. Keeping up weekly and drilling the 'choose-the-right-test → set it up → conclude in context' ritual is what gets students through.
Do I need to memorise the formulas?
No. Both the Week-7 in-semester test and the final exam supply the formula sheet and the N(0,1) and t distribution tables bound into the paper. The marks come from choosing the right method, defining notation and H₀/Hₐ in population parameters, substituting correctly, reading the table for the right df and tail, and writing the one-sentence conclusion in context — so practise application, not rote recall.
What does the Week-7 in-semester test cover and what is its format?
The in-semester test is closed-book, in-person, 1 hour of writing plus 5 minutes reading time, and consists of 35 multiple-choice questions on a generalised answer sheet. It covers Weeks 1–6 (sampling and study design, descriptive statistics, confidence intervals and the bootstrap, and hypothesis testing and randomization). A non-programmable calculator is permitted and the formula sheet and distribution tables are printed in the paper.
Can I use StatKey, Excel and a calculator in the exam?
StatKey and Excel are your everyday learning, homework and assignment tools — StatKey for the bootstrap, randomization and distribution areas, Excel (with the Data Analysis ToolPak) for EDA and regression — but there is no computer in the exam room. In the closed-book test and final you bring an approved non-programmable calculator and work by hand using the provided formula sheet and N(0,1)/t tables.
How is ECMT1010 assessed overall and are the weights confirmed?
The grounded components are a closed-book Week-7 in-semester test (35 MCQ on Weeks 1–6), an individual data-analysis assignment due around Week 11, a final exam with a 50-mark short-answer Section B (and an implied MCQ Section A), plus weekly workshops and muddy cards. The exact percentage weights and the official exam date were not in the LMS pull, so they are subject to confirmation — check your current unit outline for the authoritative numbers.
How to study for the exam
Treat ECMT1010 as a skills unit, not a memorisation unit: the formula sheet and tables are given, so your edge is fast, correct application. (1) Keep up weekly — at 6–8 hours of private study the content compounds, and the two tracks (simulation via StatKey and the classical formula/CLT method) must both be fluent because the exam mixes them. (2) Build a 'name-the-right-test' decision habit: for every question first classify the data and the goal — describe it, estimate it with a CI, test a hypothesis, or model it with regression — then ask one mean, two means, paired, one proportion, two proportions, or a regression slope before you pick a formula. (3) Map the provided formula sheet and the N(0,1)/t tables so exam time goes to setup and reading the right df and tail, not searching. (4) Rehearse the Section-B ritual: parameter definition → H₀/Hₐ in population parameters → formula in symbols → substitution → table value with df and tail → decision rule → one-sentence conclusion in context; partial marks reward every step. (5) Use StatKey to build intuition for sampling distributions, bootstraps and randomization, and Excel for EDA and regression, but rehearse the by-hand table method the closed-book exam actually uses. (6) The Week-7 test is your dress rehearsal for Weeks 1–6 — review it carefully because those foundations resurface in the final.
