BUSS1020 Quantitative Business Analysis
The Complete Exam Bible · S1 2026BUSS1020 Overview
- University of Sydney
- S1 2026
- 11 chapters
- Statistics
BUSS1020 Quantitative Business Analysis is the University of Sydney Business School's first-year introduction to statistics for decision-making — covering descriptive measures, probability, distributions, sampling, confidence intervals, hypothesis testing and regression, all built on the Berenson 15e text and worked through in Excel.
It is assessed by weekly MyLab homework (15%), a closed-book in-semester test (20%), a group data-analysis assignment (25%) and a closed-book final exam worth 40% (120 minutes plus 10 minutes reading time, 18 MCQ at 2 marks each plus 5 written-answer questions totalling 100 marks).
- Assessed by Homework (MyLab, 11 weekly tasks) 15% · In-semester test 20% · Group assignment 25% · Final exam 40%
- Key terms Parameter vs statistic, Standard error (SE), Central Limit Theorem (CLT), Confidence interval
- How to prepare Treat BUSS1020 as a skills subject, not a memorisation subject: the formula sheet is given, so your edge comes from fast, correct application.
- Most asked Is the final exam a hurdle in BUSS1020?
What BUSS1020 covers
The whole subject → one exam-ready map. Each topic links to its free chapter guide.
Foundations: Data, Variables & Sampling
Week 1 · Berenson Ch 1. Population vs sample, parameter vs statistic, variable types, levels of measurement, and how to collect a good sample.02Numerical Descriptive Measures
Week 2 · Berenson Ch 3.1–3.4, 3.6 & Ch 2. Summarising centre, spread and shape, plus quartiles, boxplots and visualisation.03Basic Probability
Week 3 · Berenson Ch 4. Probability rules, conditional probability, Bayes' theorem and counting.04Discrete Probability Distributions
Week 4 · Berenson Ch 5. Random variables and the binomial, Poisson and hypergeometric models.05Continuous Probability Distributions
Week 5 · Berenson Ch 6.1–6.5. The uniform, normal and exponential distributions and the standard normal.06Sampling Distributions & the CLT
Week 6 · Berenson Ch 7. How sample means and proportions behave, and why the Central Limit Theorem matters.07Confidence Intervals
Week 7 · Berenson Ch 8.1–8.6. Estimating population means and proportions, plus required sample sizes.08Hypothesis Testing: One-Sample Tests
Week 8 · Berenson Ch 9. Testing a single mean or proportion, with ethics of statistical reporting.09Hypothesis Testing: Two-Sample Tests
Week 9 · Berenson Ch 10.1–10.3. Comparing two means (pooled, separate, paired) and two proportions.10Simple Linear Regression
Weeks 10–11 · Berenson Ch 13. Covariance, correlation, the SLR model, fit, inference, and prediction vs confidence intervals.11Multiple Linear Regression
Week 12 · Berenson Ch 14.1–14.4, 14.6. Several predictors, model fit, the F-test, dummy variables and residual diagnostics.There is no single-component hurdle — you pass on a weighted average of at least 50% — but the exam is the largest stake and a provided formula sheet means marks come from correct setup and interpretation, not memorised formulas.
How BUSS1020 is assessed
| Component | Weight | Format |
|---|---|---|
| Homework (MyLab, 11 weekly tasks) | 15% | Online individual in MyLab Statistics; 3 attempts per question with no penalty for extra attempts; tasks weighted unequally (four worth 2% each, the rest 1% each) |
| In-semester test | 20% | Closed-book, in-person, 90 minutes; mix of MCQ and short-answer covering Weeks 1–6; formula sheet and Excel-function page supplied; non-programmable calculator permitted |
| Group assignment | 25% | Group of about 5; analyse a dataset with course techniques across three deliverables (team charter, written report 20%, video presentation 5%); peer assessment can scale non-contributors down |
| Final exam | 40% | Closed-book, in-person, 120 minutes + 10 minutes reading; Part A = 18 MCQ × 2 = 36 marks (Weeks 1–12); Part B = 5 written-answer = 64 marks (Weeks 7–12); formula sheet provided in the paper |
One-sample t-test for a mean (closed-book, formula-sheet style)
- 2 marksState hypotheses for a one-tailed (left) test, because the concern is under-filling: H₀: μ = 250 versus H₁: μ < 250.
- 1 markChoose the test statistic. The population standard deviation σ is unknown, so use the t-statistic with df = n − 1 = 35.
- 1 markCompute the standard error: S/√n = 9/√36 = 9/6 = 1.5 ml.
- 2 marksCompute the test statistic: t = (X̄ − μ₀)/(S/√n) = (246 − 250)/1.5 = −4/1.5 = −2.67.
- 1 markCompare to the critical value. The left-tail critical value t₀.₀₅,₃₅ ≈ −1.69. Since −2.67 < −1.69, the test statistic falls in the rejection region (equivalently the p-value < 0.05).
- 1 markConclude in context: reject H₀. There is sufficient evidence at the 5% level that the bottling line is under-filling (mean fill below 250 ml).
Key terms
- Parameter vs statistic
- A parameter is a numerical summary of a whole population (Greek letters: μ, σ, π); a statistic is the matching summary computed from a sample (Latin letters: X̄, S, p). Course mnemonic: Parameter–Population, Statistic–Sample.
- 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 sample size n grows, which is why larger samples estimate more precisely.
- Central Limit Theorem (CLT)
- For a large enough sample (rule of thumb n ≥ 30) the sampling distribution of the sample mean is approximately normal regardless of the population's shape, which justifies using Z and t procedures.
- Confidence interval
- An interval of the form point estimate ± (critical value × standard error) that, over repeated sampling, captures the true parameter a stated percentage of the time. '95% confident' refers to the long-run procedure, not a probability about one fixed parameter.
- Type I and Type II error
- A Type I error rejects a true H₀ (probability α); a Type II error fails to reject a false H₀ (probability β). Power = 1 − β is the chance of correctly detecting a real effect.
- Coefficient of determination (r²)
- In regression, the proportion of variation in Y explained by the model, r² = SSR/SST, ranging from 0 to 1. An r² of 0.49 means 49% of the variation in Y is explained by the predictor(s).
BUSS1020 FAQ
Is the final exam a hurdle in BUSS1020?
No. According to the official exam brief there is no single-component hurdle; you pass the unit on a weighted average of at least 50% across homework, the in-semester test, the group assignment and the final. That said, the final is worth 40%, so it carries the most weight on your mark.
Do I need to memorise the formulas?
No. Both the in-semester test and the final supply a formula sheet, and the final also includes an Excel-function page. The marks come from choosing the right tool, substituting the numbers correctly and interpreting the result — so practise application, not rote recall.
Can I use a calculator and Excel in the exam?
You may bring an approved non-programmable (non-graphing) scientific or financial calculator and a physical dictionary, but there is no computer or Excel in the exam room itself. Excel is your everyday learning and homework tool; the exam tests the same concepts by hand using the provided tables and formula sheet.
What does the in-semester test cover versus the final?
The in-semester test (90 minutes, 20%) covers up to and including Week 6 — data, descriptive measures, probability, distributions and sampling. The final's Part A MCQ covers Weeks 1–12, while Part B written-answer questions focus on Weeks 7–12 (confidence intervals, hypothesis testing and regression).
How is the group assignment marked and what if a teammate doesn't contribute?
The 25% group assignment has three parts — a team charter, a written report (20%) and a video presentation (5%) — and applies course techniques to a real dataset. Peer assessment through FeedbackFruits can scale non-contributors' marks down (commonly 10–40%, or to zero), so individual effort is recorded and rewarded.
How to study for the exam
Treat BUSS1020 as a skills subject, not a memorisation subject: the formula sheet is given, so your edge comes from fast, correct application. (1) Keep up weekly — each MyLab homework set rehearses the exact concept the test and exam reward, and three no-penalty attempts mean there is no excuse to leave marks on the table.
(2) Build a 'choose-the-right-tool' decision habit: for every question first classify the data and the goal (describe, estimate with a CI, test a hypothesis, or model with regression), then pick the matching formula. (3) Map the formula sheet — know where each formula sits and what its symbols mean, so exam time goes to setup not searching.
(4) Practise the written-answer ritual for Part B (hypotheses → formula in symbols → substitution → decision rule → one-sentence business conclusion); partial marks reward every step. (5) Use Excel to check your by-hand work while learning (NORM.DIST, BINOM.DIST, T.INV), but rehearse the table-and-calculator method that the closed-book exam actually uses.
(6) The in-semester test is your dress rehearsal for Weeks 1–6 — review it carefully, because those foundations resurface in the final's Part A.
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