ETC1000 Business and Economic Statistics
The Complete Exam Bible · S2 2026ETC1000 Overview
- Monash University
- Semester 2 2026
- First year business unit
- Tutorials run on Excel
- R introduced from week 7
Business and Economic Statistics at Monash University
ETC1000 Business and economic statistics is a first year Monash unit in which students learn to read quantitative evidence and turn it into decisions across business and economics.
- Assessed by weekly in-tutorial exercises, two in-class tests, a recorded group project and an end of semester examination
- Where the marks accumulate three of the four components are earned in a room on a schedule, so a missed tutorial is a missed exercise
- Hardest step writing the sentence after the number: saying what a result establishes and what it does not
- How to prepare work the weekly exercises with the answer covered, then compare your wording rather than your arithmetic
How ETC1000 is assessed
| Component | Weight | Format |
|---|---|---|
| Exercises, completed during your allocated tutorial | 20% | Individual, weekly from week 1. The exercises are opened inside the tutorial and cannot be completed outside class, so attending the tutorial you were allocated to is what makes them available. |
| In-Class Tests, two tests worth 10 per cent each | 20% | Individual, weeks 6 and 11, sat during tutorials. Test 1 opens the material of weeks 1 to 5; the week 5 lecture is given over to revision and questions for it. |
| Group Project | 20% | Group, weeks 9 to 10. A recorded 12 to 15 minute presentation analysing house and unit prices across Melbourne suburbs, submitted with slides, a spreadsheet, R code and a link to the recording. |
| Examination | 40% | Individual, during the end of semester examination period. The unit materials give the period rather than a date, so confirm your own sitting on the University timetable. |
The four weights for Semester 2 2026 are 20, 20, 20 and 40 per cent and they sum to 100. They appear identically on two official surfaces, the unit schedule and assessment summary document and the assessments page of the unit site, and the in-class tests are described there as two tests worth 10 per cent each. On a pass condition the unit summary carries a row headed Hurdle requirement whose whole content refers students to the Monash Handbook, so no component is stated to carry one and none is stated not to. One conflict is worth resolving before week 9: the project brief gives 11.55pm on Friday 2 October, calling it the end of week 9, while the assessments page and the schedule give 11.55pm on Monday 5 October against week 10.
Assessment structure
Current ETC1000 dates
| Date | Item | Control |
|---|---|---|
| Weekly, during your allocated tutorial | Exercises | The exercises open and close inside the tutorial and cannot be attempted outside class. |
| Week 6, during your tutorial | In-Class Test 1 | The week 5 lecture is given over to revision and questions for this test. |
| Week 11, during your tutorial | In-Class Test 2 | The unit summary and the schedule both place the second test in week 11. |
| End of week 9 or the following Monday | Group Project | The project brief and the unit site give different dates for this deadline. Both are set out in the assessment note above. |
| During the end of semester examination period | Examination | The unit materials give the period rather than a date. Confirm your own sitting on the University timetable. |
Dates are as published in Confirm current dates and operational instructions on the live unit site.. Confirm exact deadlines and submission settings in the live LMS.
What ETC1000 covers
Business and Economic Statistics runs one arc across twelve weeks: describe the data you have, quantify how much a sample can be trusted, model how two or more variables move together, then predict a category rather than a quantity. The eleven chapters follow that order, and the last one is the assessed group project that draws on four of them at once.
Analysing Categorical Data
Week 1. Nominal, ordinal, discrete and continuous variables; frequency and relative frequency distributions; bar charts against pie charts; the contingency table and the marginal, joint and conditional probabilities read off it; the unit's own independence test.02Analysing Numerical Data
Week 2. Mean, median and mode and when each misleads; range, interquartile range, variance and standard deviation; skew read from the gap between mean and median; histograms; the standardised score and comparison across groups with different spreads.03Using Data in an Uncertain World
Week 3. Population against sample and parameter against statistic; sampling design, selection and survivor bias; the law of large numbers; the sampling distribution of the mean; standard error; confidence intervals; why the unit works with the t-distribution.04Models of Relationships Between Data
Week 4. Reading a scatter for direction, form, strength and exceptions; covariance and why it is unusable alone; the correlation coefficient; the least-squares line fitted end to end on one dataset; residual patterns; the coefficient of determination.05Hypothesis Testing and Statistical Evidence
Week 5. The inference framework and the step a test exists to justify; writing the null and alternative about the population; the test statistic and the p-value; one-sided against two-sided; Type I and Type II errors; testing a regression slope.06Multiple Regression, Dummies and Interactions
Weeks 5 and 6. Reading a coefficient with its holding-fixed clause; why a coefficient changes when a variable is added; dummy variables and the omitted baseline category; interaction terms and the slope-difference identity; the adjusted fit measure.07Introduction to R and RStudio
Week 7. Objects, functions, arguments and packages; the four hops a line of code makes and the error that belongs to each; data frames and column types; missing values; the join, the missing-value investigation and the grouped summary the project requires.08Data Wrangling with the tidyverse
Week 8. Filtering, selecting, mutating and arranging against summarising; which verbs change rows, columns and the grain; grouping as a label that changes nothing visible; carrying counts beside averages; why a mean of means is not the overall mean.09Using Models for Classification and Prediction
Week 9. Predicting a category rather than a quantity; the threshold as a business decision; the four counts and the rates built from them; accuracy against sensitivity, specificity and precision; the base rate; scoring on data the model has not seen.10Logistic Regression for Binary Outcomes
Week 10. Why a straight line predicts probabilities outside zero and one; the logistic curve and what it guarantees; odds, log odds and the odds multiplier; reading a coefficient as a direction and a factor; the tipping point; how the model is judged.11The Group Project on Melbourne Suburb Prices
Weeks 9 and 10, 20 per cent. Four data sources joined on suburb name; reducing four quarterly medians to one price; the four tasks and the method each draws on; the recorded 12 to 15 minute presentation; the three assessment criteria; the two published deadlines.It moves from summarising data with tables, charts and measures of centre and dispersion, through standardisation, probability and sampling, to statistical inference with confidence intervals and hypothesis testing, and on to regression models of the relationships between variables.
The unit states that these concepts are applied to real world scenarios throughout.
The twelve week arc
The schedule runs in four movements. Weeks 1 and 2 describe data, first categorical and then numerical. Week 3 introduces uncertainty, separating what you want to know from what you can afford to measure. Weeks 4 to 6 model relationships, from a scatter plot to multiple regression with dummy variables.
Weeks 7 to 10 change the tool to R and extend the models to categorical outcomes through classification and logistic regression. Weeks 6 and 11 are the two in-class tests, and week 12 is consolidation and exam revision.
How the unit is taught
Lectures and workshops run online on Friday mornings and are recorded.
Tutorials run weekly on campus from week 1, are not recorded, and are the sessions in which the assessed weekly exercises are completed, so students must attend the class they were allocated to. Tutorials are bring your own device and use a full installation of Excel rather than the browser version.
R and RStudio enter from week 7, and one part of the group project must be completed in R.
What this guide covers
Eleven chapters follow the unit's own topic order, from categorical data through to logistic regression, with a final chapter on the group project. A whole unit drill chapter and an exam morning recap close the book. Current dates, venues and any changes are published on the unit site.
Deciding whether two categories are independent
- 1Fix the condition and read each row as its own total.
- 1Compute the same conditional probability inside each row.
- 1Compare them, and say what the comparison licenses.
Key terms
- Categorical variable
- A variable recording which group a case belongs to, with no arithmetic meaning to its values.
- Contingency table
- A table crossing two categorical variables so that every case falls in exactly one cell.
- Conditional probability
- A probability computed inside one row or column rather than over the whole table.
- Interquartile range
- The distance covering the middle half of the data, from the first quartile to the third.
- Standardised score
- A value rewritten as the number of standard deviations it sits from its own group mean.
- Sampling distribution
- The distribution a statistic would follow across repeated samples of the same size.
- Standard error
- The spread of a statistic across samples, which shrinks with the square root of the sample size.
- Confidence interval
- A range produced by a procedure that captures the parameter at a stated long run rate.
- Correlation coefficient
- A unit free measure of how tightly two variables sit around a straight line, between minus one and plus one.
- Least squares line
- The line chosen to make the sum of the squared vertical misses as small as possible.
- Residual
- The vertical gap between an observed value and the value the fitted line predicts for it.
- Dummy variable
- A variable taking one when a case is in a category and zero otherwise, measured against an omitted baseline.
ETC1000 FAQ
How is this unit assessed, and does anything have to be passed?
Four components are published for Semester 2 2026: weekly exercises worth 20 per cent completed during tutorials, two in-class tests worth 20 per cent between them, a group project worth 20 per cent, and an examination worth 40 per cent. They sum to 100 and appear identically on the unit summary document and the assessments page.
On a pass condition, the unit summary lists a hurdle requirement and answers it by referring students to the Monash Handbook, so nothing in the unit materials states that a component must be passed and nothing states that none must be.
Why does it matter which tutorial I attend?
The weekly exercises are worth 20 per cent and are completed inside the tutorial, and the unit states that they cannot be opened outside class. Tutors mark off only the students allocated to that class, so attending a different tutorial without prior written approval means the exercise is not credited. The unit also notes that University policy rules out the short extension route for these weekly assessed quizzes.
When are the in-class tests and what do they cover?
There are two, each worth 10 per cent, sat during tutorials. The unit summary places them in weeks 6 and 11, and the schedule labels those weeks as In-Class Test 1 and In-Class Test 2. The first follows the five teaching weeks that cover categorical data, numerical data, uncertainty, models of relationships, and hypothesis testing, and the week 5 lecture is given over to revision and questions for it.
What does the group project ask for?
It asks how house and unit prices are distributed across the suburbs of Melbourne and what explains the differences. Groups merge quarterly median price data with a socio economic index and a list of suburb distances from the city, describe the distribution, compare house against unit prices, estimate two multiple regression models, and complete one task in R that joins a council file and summarises prices by council.
The product is a recorded presentation of 12 to 15 minutes in which every member speaks.
Which software does the unit use, and when does R start?
Tutorials are bring your own device and run on a full installation of Excel rather than the browser version, from week 1. R and RStudio are introduced in week 7 and the tidyverse in week 8. The group project brief allows most tasks in either Excel or R, and requires one part to be completed in R.
What does the twelve week sequence cover?
Four movements. The first two weeks describe data, categorical in week 1 and numerical in week 2. Week 3 turns to uncertainty. Weeks 4 and 5 move to relationships and inference, first fitting a model and then testing it, with further regression alongside. Week 6 is the first in-class test. Weeks 7 and 8 change the tool to R and then to the tidyverse.
Week 9 turns models towards classification and prediction and week 10 to logistic regression. Week 11 is the second in-class test, and week 12 is consolidation with exam revision.
What is the examination worth and where is its date published?
It is worth 40 per cent, the largest single component. The unit summary places it during the examination period rather than on a stated date, and no calendar date for it appears anywhere in the unit materials, so the University examination timetable is where students find their own sitting.
How to study for the exam
Three of the four components in this unit are earned in a room on a fixed schedule, so the highest return activity is preparing for the tutorial rather than revising after it: watch the Friday lecture and workshop before your allocated class, because the exercise is completed there and cannot be opened later. Build revision around writing rather than arithmetic.
Every question in this unit has two halves, a quantity and a sentence saying what that quantity establishes, and the spreadsheet is available for the first half while nothing helps with the second.
Work each drill item with the model answer covered, then compare the wording rather than the number, and keep three refusal clauses ready: no causation from observed data, no prediction outside the range the data covered, and no statement about individuals drawn from a statement about a mean.
In the final fortnight, sit the whole unit drill cold and timed, then sort the errors into the ones where you computed the wrong quantity and the ones where you computed the right quantity and described it indefensibly, because those two piles need different work.
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