BUSS1020: pass the exams, not just read the notes
Your complete guide to University of Sydney's quantitative business analysis unit. See where the marks are, work real practice questions, and study with an AI tutor that knows BUSS1020.
Sia generates BUSS1020 practice questions, walks through confidence intervals and hypothesis testing step by step, and quizzes you on the material the exam weights most heavily.
Worked example
A vending machine is meant to pour 250 ml per cup. Quality control suspects under-filling and takes a random sample of 36 cups, finding a sample mean of 246 ml and a sample standard deviation of 9 ml. Testing H0: mu = 250 against H1: mu < 250 at the 5% level (critical t with 35 df is about -1.69), what is the test statistic and the decision?
The population standard deviation is unknown and only the sample S = 9 is given, so use a one-sample t-test with df = n − 1 = 35, not a Z-test.
Compute the test statistic: t = (X-bar − mu0) / (S / sqrt(n)) = (246 − 250) / 1.5 = -4 / 1.5 = -2.67.
This is a left-tailed test, so compare with the critical value -1.69. Since -2.67 < -1.69, the statistic falls in the rejection region.
Reject H0: there is evidence at the 5% level that the machine under-fills (mean pour below 250 ml).
The trap: Using a Z-test because n is 36. With sigma unknown the correct statistic is t (df = 35), so option D is wrong even though the number matches; and dividing by 9 instead of by the standard error 9/sqrt(36) gives the wrong t = -0.44 in option C. classic slip!
One exam decides 40% of your grade. All weeks for MCQ; Weeks 7 to 12 for written answers. This whole page is built around that.
Overview
What BUSS1020 is, and where it sits
BUSS1020 is the University of Sydney Business School's introductory statistics unit for business. Over twelve teaching weeks it moves from describing data (central tendency, variation, shape, visualisation), through probability and probability distributions (binomial, Poisson, hypergeometric, normal, exponential), into statistical inference (sampling distributions and the Central Limit Theorem, confidence intervals, one and two sample hypothesis tests), and finishes with simple and multiple linear regression.
The prescribed text is Berenson et al., Basic Business Statistics, 15th edition (Pearson), and weekly homework runs through Pearson's MyLab Statistics platform. Excel functions such as NORM.DIST, BINOM.DIST and T.INV are taught as the working tool, but the in-person test and exam are closed-book with a supplied formula sheet and no computer, so you must also work from the formula sheet and a basic calculator.
It is a foundational quantitative unit in the Bachelor of Commerce and related Business School degrees. There are no prerequisites, but it is prohibited against several other intro-stats units (ECMT1010, MATH1005, MATH1905, MATH1015, MATH1115, STAT1021, ENVX1001), so students take one statistics gateway, not two. The descriptive-stats, probability and inference skills feed directly into later analytics, econometrics, finance and marketing-research units.
Official outline: sydney.edu.au · BUSS1020 outline. Always treat the official outline and the exam timetable as authoritative.
Difficulty & time commitment
Is BUSS1020 hard, and how much time does it take?
BUSS1020 is manageable if you keep a weekly rhythm and treat the back half as the main event. Across student reviews the pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
A read across student reviews and course feedback. See what students say ↓
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the unit.
Is this unit for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You keep up with the weekly MyLab homework every week, since reviewers describe the early marks as near-free if you stay current
- You are comfortable, or willing to get comfortable, with Excel statistical functions and basic algebra
- You attend workshops and use the drop-in tutor consultations and PASS sessions for the concepts that do not click
- You practise on the closed-book formula sheet from Week 7 rather than relying on software, since the exam has no computer
You may struggle if
- You let MyLab homework pile up, because the material is cumulative and inference builds on probability builds on descriptive stats
- You are shaky on algebra or have never used Excel and skip the Maths-in-Business support
- You try to memorise formulas instead of learning which test or distribution fits which situation
- You coast on the easy Weeks 1 to 6 and underestimate the Weeks 7 to 12 written section that the final exam's Part B targets
- Treat the provided formula sheet as a study tool from Week 7 onward, so you know exactly where each formula lives and save exam time
- Drill the which-model and which-test decision (binomial vs Poisson vs hypergeometric; pooled vs separate-variance vs paired t) rather than just plugging numbers
- Interpret every result in business language, not just compute it, because the written answers and group report reward a clear story
- Pull your weight early in the group assignment so peer assessment (FeedbackFruits) protects rather than penalises your 25%
- Rebuild past-paper-style questions with fresh numbers to test the skill, not memorise specific answers
Syllabus
The 13 topics, week by week
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Introduction to data
Berenson 15e Ch 1Population vs sample, parameter vs statistic (Greek vs Latin), variable types and levels of measurement, and sampling methods plus the biases each risks.
T2 · Numerical descriptive measures
Berenson 15e Ch 3.1 to 3.4, 3.6, Ch 2Central tendency, variation and shape: mean, median, geometric mean, variance and SD, coefficient of variation, Z-scores, quartiles, IQR, boxplots and the Empirical vs Chebyshev rules.
T3 · Basic probability
Berenson 15e Ch 4Probability approaches, contingency tables, the addition and multiplication rules, conditional probability and independence, Bayes' theorem and counting rules.
T4 · Discrete probability distributions
Berenson 15e Ch 5Expected value and variance of a discrete random variable, plus the binomial, Poisson and hypergeometric distributions and when each applies.
T5 · Continuous probability distributions
Berenson 15e Ch 6.1 to 6.5Density and area under the curve, the uniform, normal and exponential distributions, standardisation and the inverse normal, and the Poisson to exponential link.
T6 · Sampling distributions
Berenson 15e Ch 7The sampling distribution of the mean and of a proportion, standard error, and the Central Limit Theorem with its rules of thumb (n at least 30; n-pi at least 5).
T7 · Confidence intervals
Berenson 15e Ch 8.1 to 8.6Point estimate plus or minus critical value times standard error: intervals for a mean (Z vs t) and a proportion, required sample size, and interpretation discipline.
T8 · Hypothesis testing, one-sample
Berenson 15e Ch 9H0 vs H1, Type I and Type II error and power, one vs two tailed tests, the critical-value and p-value approaches, and tests for a mean and a proportion.
T9 · Hypothesis testing, two-sample
Berenson 15e Ch 10.1 to 10.3Pooled-variance, separate-variance and paired t-tests for two means, two-proportion tests, and the link between a confidence interval for a difference and the test.
T10 · Linear regression 1: covariance, correlation, SLR
Berenson 15e Ch 13.1 to 13.3, 3.5Covariance and correlation, the simple linear regression model, slope and intercept interpretation, the SST equals SSR plus SSE decomposition and r-squared.
T11 · Linear regression 2: inference and intervals
Berenson 15e Ch 13.4 to 13.8The t-test and confidence interval for the slope, the confidence interval for the mean of Y, and why the prediction interval for an individual Y is always wider.
T12 · Multiple linear regression
Berenson 15e Ch 14.1 to 14.4, 14.6Multiple predictors holding others constant, r-squared and adjusted r-squared, the F-test for overall significance, individual t-tests, dummy variables and residual diagnostics.
T13 · Review
Consolidation across all twelve modules ahead of the final exam, spanning descriptive stats through multiple regression.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Weekly homework (MyLab Statistics) | 15% | Individual online homework in Pearson MyLab across about eleven weeks, with up to three attempts per question and no penalty for extra attempts. Task weights are uneven (four tasks at 2% each, the rest at 1%). Most weeks, generally due Sundays 11:59pm. Covers the week's topic. |
| In-semester test | 20% | Closed-book, in-person, 90 minutes. Multiple choice plus short answer (the brief states 18 MCQ then 12 short answer; the S2 2025 past paper ran 15 MCQ plus 15 short answer). Formula sheet and an Excel-formula page supplied; non-programmable calculator; no Excel. Around the end of Week 7. Covers up to and including Week 6. |
| Group assignment | 25% | Groups of about five (allocated within the same workshop) analyse a dataset using course techniques. Three deliverables: a team charter, progress report and completion plan (avoids a 10% penalty), a written report (20%) and a video presentation (5%). Peer assessment via FeedbackFruits can scale a low contributor down by about 10 to 40% or to zero. Generative AI is permitted if acknowledged, but AI characters or voices may not appear in the video. Charter early to mid semester; report and video due Week 13. Applied use of course quantitative techniques on a real dataset. |
| Final exam | 40% | Closed-book, in-person, 120 minutes plus 10 minutes reading. Part A is 18 multiple-choice questions at 2 marks each (36 marks, Weeks 1 to 12); Part B is 5 written-answer questions (64 marks, Weeks 7 to 12); 100 marks total. A supplied formula sheet (including an Excel-functions page), a non-programmable non-graphing calculator and a physical dictionary are allowed; no computer. Final exam period. All weeks for MCQ; Weeks 7 to 12 for written answers. |
- Weighted average of at least 50% across all assessments. The final exam is explicitly not a hurdle and there is no single-component hurdle.
- Final exam Part A is 18 MCQ (36 marks, Weeks 1 to 12); Part B is 5 written-answer questions (64 marks, Weeks 7 to 12).
- Calculator policy: Non-programmable non-graphing calculator and a physical language dictionary allowed in the test and exam; supplied formula sheet plus an Excel-formula page; no computer.
This is an exam-cram unit. With the exams at 60% of the grade and the final exam alone at 40%, your result is overwhelmingly decided by how well you perform under time pressure. All weeks for MCQ; Weeks 7 to 12 for written answers.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The unit rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Complete each week's MyLab homework before its Sunday deadline (three attempts, no penalty, so use them)
- Keep a one-line note per week of which test or distribution applies when
- Drill the descriptive-stats and probability of Weeks 1 to 6, since the in-semester test covers up to Week 6 only
- Memorise the decision logic for the discrete models: binomial vs Poisson vs hypergeometric
- Enrol in the Maths-in-Business Excel and algebra modules early if your maths or Excel is rusty
Before the final heaviest topics
- Rebuild past-paper-style problems with new numbers so you test the skill, not the answer
- Memorise the decision logic: Z vs t, sigma known vs unknown, and pooled vs separate-variance vs paired t
- Know exactly where each formula sits on the supplied sheet to save time under closed-book conditions
- Practise reading and interpreting Excel regression output (coefficients, t-stats, p-values, r-squared, adjusted r-squared, ANOVA F)
- For Part B, drill Weeks 7 to 12 hardest, since confidence intervals, hypothesis tests and regression are the written-answer pool
- Practise without software, because the exam has no computer, only a non-programmable calculator
The mistakes that cost marks
Coasting on the easy weeks. Descriptive stats and probability in Weeks 1 to 6 feel gentle, but the final exam's Part B written section is drawn only from Weeks 7 to 12 (confidence intervals through multiple regression), which reviewers flag as a step up. Front-load practice on the back half.
Confusing population and sample notation. Mixing up Greek vs Latin (parameter vs statistic) and population vs sample formulas under exam pressure loses easy marks. Keep the mnemonic: Parameters describe a Population (Greek), Statistics describe a Sample (Latin).
Forgetting the CLT conditions and the Z vs t choice. Skipping the rules of thumb (n at least 30; n-pi and n(1 minus pi) at least 5) and the sigma-known to Z vs sigma-unknown to t decision is a common trap on inference questions.
Treating correlation as causation. Reading r or r-squared loosely, or claiming a regression slope proves causation, is penalised in the written answers. State relationships as linear association and interpret r-squared as variation explained.
Letting the group assignment drift. Low FeedbackFruits peer scores can cut your 25% by 10 to 40% or to zero. Contribute early and visibly so peer assessment protects rather than penalises you.
Relying on generative AI for the calculations. The unit warns that AI tools regularly compute the statistics incorrectly. Use them for explanation if acknowledged, but verify every number yourself.
Teaching team
Who teaches BUSS1020
The bios below are factual. The star ratings are not ours: they are impressions from students who have taken the unit, so you can hear from people who sat in the lectures.
Dr Bern Conlon
Researches market research with a focus on choice modelling and marketing-mix modelling, and quantitative risk modelling (market and credit risk). PhD in Econometrics; has received the Dean's Citation for teaching in BUSS1020. Staff profile
Dr Yves Tam
Researches time series analysis and econometrics (PhD in Statistics) and coordinates and teaches across business-analytics units in the Discipline of Business Analytics.
Teaching team as listed in the unit materials reviewed. AskSia does not rate lecturers; star ratings are submitted by students who have taken BUSS1020.
Formula & concept sheet
The vocabulary and formulas you must own
- Parameter vs statistic
- A parameter describes a population (Greek letters: mu, sigma, pi); a statistic describes a sample (Latin letters: X-bar, S, p).
- Coefficient of variation (CV)
- Relative spread, (S / X-bar) times 100%, used to compare variability across series on different scales.
- Empirical Rule vs Chebyshev
- For bell-shaped data about 68/95/99.7% fall within plus or minus 1/2/3 SD; Chebyshev guarantees at least (1 minus 1/k-squared) within k SD for any distribution.
- Conditional probability
- P(A given B) = P(A and B) / P(B); A and B are independent when P(A given B) = P(A).
- Bayes' theorem
- Reverses a conditional probability using priors and likelihoods to obtain the posterior P(Bj given A).
- Binomial, Poisson, hypergeometric
- Discrete models for, respectively, fixed-n two-outcome trials; counts of events in a continuous interval; and sampling without replacement from a finite population.
- Central Limit Theorem (CLT)
- For large enough n the sampling distribution of X-bar is approximately normal regardless of population shape (rule of thumb n at least 30).
- Standard error
- The standard deviation of a sampling statistic, sigma / sqrt(n) for the mean and sqrt(pi(1 minus pi)/n) for a proportion.
- Confidence interval
- Point estimate plus or minus (critical value)(standard error); 95% confident means 95% of such intervals capture the true parameter.
- Type I vs Type II error
- Type I rejects a true H0 (probability alpha); Type II fails to reject a false H0 (probability beta); power = 1 minus beta.
- Pooled vs separate-variance t
- Two-sample mean tests: pooled assumes equal population variances; separate-variance (Welch) does not; paired t handles matched data.
- Coefficient of determination (r-squared)
- SSR / SST, the proportion of variation in Y explained by the regression; adjusted r-squared penalises adding predictors.
- Heteroscedasticity
- Non-constant variance of regression residuals, shown by a funnel-shaped residual-vs-fitted plot, violating a regression assumption.
Common acronyms: CV · IQR · CLT · CI · SE · SLR · MLR · SST/SSR/SSE · ANOVA.
What students say
What students actually say about BUSS1020
Recurring themes from student reviews, paraphrased in our own words.
- Moderate difficulty: not particularly hard but does take some thinking, and more approachable for students with a stats background
- Weekly homework is substantial but manageable; staying current makes the early marks easy and reduces final-exam pressure
- Which lecturer you get is mentioned as affecting the experience
- High demand for week-by-week lecture notes and chapter summaries
- Heavy student appetite for Excel formula cheat sheets and exam or mid-semester revision notes
- Cheat-sheet and condensed-notes content is in demand, signalling students want compact revision aids
- Workshops, tutor support and PASS sessions are valued for retention
- The final exam is a noticeable step up from the mid-semester assessment
- Active sharing of practice exams and tutorial worksheets
- Demand for short concept-by-concept video walkthroughs
Recurring student opinions, paraphrased and aggregated, not official course information.
Set texts
The prescribed reading
The syllabus references map straight onto these.
Basic Business Statistics: Concepts and Applications, 15th edition
Berenson, M. et al. (2023). Publisher page
Where it fits
Prerequisites, related units & why it matters
No prerequisites. Prohibited against ECMT1010, MATH1005, MATH1905, MATH1015, MATH1115, STAT1021 and ENVX1001, so it is one of several intro-stats gateways and you take just one.
Your BUSS1020 study toolkit
Study the unit with Sia, not just read about it
Each tool already knows BUSS1020: your syllabus, your texts, and where the marks are. Grouped by how you study, from first contact to exam week.
FAQ
Frequently asked questions
Is the final exam a hurdle?
No. The final exam is explicitly not a hurdle, and there is no single-component hurdle. You pass by reaching a weighted average of at least 50% across all assessments.
How is BUSS1020 assessed?
Four pieces: weekly MyLab homework (15%), an in-semester test (20%, around end of Week 7, covering Weeks 1 to 6), a group dataset-analysis assignment (25%), and a final exam (40%). So 60% is closed-book in-person and 40% is continuous coursework.
What does the final exam look like?
Closed-book, in-person, 120 minutes plus 10 minutes reading. Part A is 18 multiple-choice questions (36 marks, drawn from Weeks 1 to 12); Part B is 5 written-answer questions (64 marks, drawn from Weeks 7 to 12). You get a supplied formula sheet, a non-programmable non-graphing calculator and a physical dictionary, but no computer.
Do I need a maths or stats background?
There are no prerequisites. Reviewers find it moderate and approachable, and it is easier if you keep up weekly. If your algebra or Excel is rusty, the Business School's free Maths-in-Business and PASS programs are there to close the gap.
Can I take it alongside another stats unit?
Generally no. BUSS1020 is prohibited against ECMT1010, MATH1005, MATH1905, MATH1015, MATH1115, STAT1021 and ENVX1001, so it is one of several intro-stats gateways and you take just one.
How important is Excel?
Excel statistical functions such as NORM.DIST, BINOM.DIST and T.INV are taught as the working tool and matter for the homework and the group assignment. But the in-semester test and final exam are closed-book with no computer, so you must also be able to work from the formula sheet and a basic calculator.
How does the group assignment work and what is the risk?
You analyse a dataset in a group of about five from your workshop, submitting a charter or progress plan, a written report (20%) and a video presentation (5%). Contribution is measured by FeedbackFruits peer assessment, where a low contributor can be scaled down by roughly 10 to 40% or to zero, so pulling your weight early protects your 25%.
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