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. |
Confirm current dates and operational instructions on the live unit site.
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.Name the variable type first
The unit opens on a decision that looks trivial and governs everything after it. A categorical variable records which group a case belongs to; a numerical variable records how much or how many.
- Nominal, ordinal, discrete and continuous, and the test that separates them
- Frequency, relative frequency and percentage as one table written three ways
- Why a sorted bar chart beats a pie chart beyond about four categories
- Reading marginal, joint and conditional probabilities off one contingency table
- Deciding independence by comparing conditional probabilities across rows
Analysing 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.Three answers to the question of centre
Mean, median and mode agree on symmetric data and diverge exactly where the data is interesting. The mean is the balance point and uses every value, so every value can move it.
- Mean, median and mode, and which question each of them answers
- Range, interquartile range, variance and standard deviation
- Reading skew from the gap between the mean and the median
- Choosing a histogram interval width and reporting it
- Standardised scores and comparing across groups with different spreads
Using 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.Two vocabularies that must not be mixed
From week 3 the data is treated as a sample drawn from a larger population. A population summary is a parameter, fixed and almost never known.
- Parameter against statistic, and why only one of them varies
- Selection, survivor and non-response bias, and why size cannot repair them
- The law of large numbers and the single thing it promises
- The sampling distribution of the mean and the standard error
- Confidence intervals, and stating the level as a property of the procedure
- Why the unit works with the t-distribution rather than the normal
Models 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.The scatter is where the tools are licensed
Before any coefficient is computed, the scatter plot settles four things that nothing else shows: the direction of the pattern, its form, whether a straight line is even the right shape, its strength, and whether one point sitting away from the rest is going to pull the line towards itself.
Covariance carries direction, correlation carries size
Covariance averages the product of the two variables' deviations, so it is positive when the deviations usually share a sign.
- Reading a scatter for direction, form, strength and exceptions
- Covariance, its sign rule, and why its magnitude cannot be compared
- The correlation coefficient and what a given value looks like
- Fitting the least squares line and interpreting both coefficients
- Residual patterns and what each of the three shapes warns about
- The coefficient of determination and the three things it does not certify
Hypothesis 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.A rule that can be stated before the data is seen
Hypothesis testing assumes for the sake of argument that nothing is going on, works out how surprising the observed result would be under that assumption, and rejects the assumption when the result is surprising enough.
- Where a test sits in the four step inference framework the unit teaches
- Writing the null and the alternative about the population, not the sample
- One sided against two sided, and why the question decides it
- The test statistic, the p-value, and the sentence each one licenses
- Type I and Type II errors, and what changing the level actually trades
- Testing a regression slope against a null of zero
Multiple 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.Every coefficient carries a clause
A multiple regression coefficient answers a narrower question than a simple one.
- Reading a coefficient with its holding fixed clause attached
- Why adding a variable changes the coefficient on the first one
- Dummy variables, the omitted baseline, and the count of dummies you need
- Interaction terms and the slope difference identity
- Main effects that stop being main effects once an interaction is present
- The adjusted fit measure and what a high value does not certify
Introduction 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.From pointing to naming
Weeks 1 to 6 run in a spreadsheet, where you point at a cell and the result appears beside it. R replaces pointing with naming: data is bound to a name, a function is applied to that name, and something comes back.
- Objects, functions, arguments and packages, and the error each one produces
- Reading the first error rather than the last
- Data frames, column types, and why one stray entry changes a whole column
- Missing values as values that propagate rather than gaps that vanish
- Planning the join, the missing value investigation and the summary the project requires
Data 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.A small number of verbs, each doing one thing
Data rarely arrives in the shape a question needs, and wrangling is the work of getting it there.
- Which verbs change rows, which change columns, and which changes the grain
- Filtering before ranking, and mutating to a rate before comparing
- Grouping as a label that changes nothing until the next operation
- The six statistics the project brief asks for, per council
- Why an average of group averages is not the overall average
Using 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.The outcome becomes a category
Every model so far predicted a quantity and measured its errors as residuals in the units of that quantity.
- Why residuals stop being defined once the outcome is a category
- The threshold as a business decision rather than a model output
- The four counts and the four rates built from them
- Sensitivity against precision, and the denominator that separates them
- The base rate, and why accuracy alone flatters a useless model
- Scoring on data the model has not seen
Logistic 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.Why the obvious approach fails
Running an ordinary regression on a zero one outcome half works.
- Why a straight line predicts probabilities outside zero and one
- The logistic curve, its steep middle and its flat ends
- Probability, odds and log odds, and what a coefficient does to each
- Turning a coefficient into a defensible sentence
- The tipping point where the predicted probability is one half
- Judging the model with the week 9 classifier rates
The 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.The question and the freedom
The project sets one question about Melbourne housing: how do prices for houses and for units vary from suburb to suburb, and which characteristics of a suburb account for that variation.
- The four data sources and why the suburb name key does not line up cleanly
- Reducing four quarterly medians to one price per suburb, and defending the choice
- Using the socio economic index as a value or as a decile category
- The four tasks and which teaching week each one draws on
- The recorded presentation, its time limit and its recording rules
- The two published due dates, the late penalty and the group extension rule
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.
Worked example · free
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.
Questions students ask in each ETC1000 chapter
- Can a variable stored as numbers still be categorical?
- Yes, and it often is. A postcode, a customer identifier and a store number are all written with digits and none of them supports arithmetic, so they are nominal. The working test is to ask what an average of the values would mean; if the answer is nothing, the variable is categorical however it is stored. Chapter 01 · Analysing Categorical Data
- When should I report the median rather than the mean?
- Whenever a few extreme values are pulling the mean away from the bulk of the data, which is the normal situation for prices, incomes, waiting times and order values. The test is to compute both: if they differ noticeably the distribution is skewed and the median is the honest summary of a typical case. The mean is still correct if the question is about a total, because the total is what it is built from. Chapter 02 · Analysing Numerical Data
- What does 95 per cent confidence actually mean?
- It describes the procedure, not the interval you are holding. Building 95 per cent intervals from repeated samples produces intervals that capture the parameter in about 95 per cent of those repetitions. Your particular interval either contains the parameter or it does not, and no calculation tells you which, which is why the defensible sentence is written about the method rather than about the number. Chapter 03 · Using Data in an Uncertain World
- Does a strong correlation show that one variable causes the other?
- No, and nothing in the calculation could. The coefficient is symmetric in the two variables, so it cannot distinguish advertising driving sales from busy stores being given larger budgets, and a third variable such as store size can produce both. The only defence available in this unit is to name the plausible third variable out loud whenever the coefficient is reported. Chapter 04 · Models of Relationships Between Data
- Does a p-value of 0.03 mean the null has a 3 per cent chance of being true?
- No. It means that if the null were true, results at least this extreme would occur in about 3 per cent of samples. It is the probability of the data given the hypothesis, and the reversed reading is a different quantity that is not close to it. Writing the correct version takes one extra clause and is worth a mark on most interpretive questions. Chapter 05 · Hypothesis Testing and Statistical Evidence
- Why did my coefficient change when I added another variable?
- Because it now answers a different question. In the simple model it measured everything that travelled with that variable, including whatever the second variable represents. In the larger model that share has been given to the variable it belongs to. Neither estimate is wrong; the larger one answers the question a reader usually means, which is a comparison between cases that are alike in other respects. Chapter 06 · Multiple Regression, Dummies and Interactions
- Do I need R for the whole unit?
- No. Tutorials from week 1 run on a full installation of Excel, and the group project brief states that tasks may be done in either Excel or R according to the group's preference, with one specified part that must be done in R. R is introduced in week 7 and the tidyverse in week 8, so there are several weeks between the introduction and the requirement. Chapter 07 · Introduction to R and RStudio
- Which verb changes the meaning of a row?
- Only summarising. Filtering removes rows, selecting removes columns and mutating adds one, but in each case a row still represents what it represented before. Summarising collapses many rows into one, so afterwards a row is one group rather than one case, and every sentence written about the table has to change accordingly. Chapter 08 · Data Wrangling with the tidyverse
- Why is accuracy a poor headline number?
- Because it is dominated by whichever class is larger. On a portfolio with a 3 per cent default rate, a model that predicts no defaults at all scores 0.97, catches nothing and has no value for the decision it was built for. Accuracy belongs in the report, but never on its own and never before the base rate it has to beat. Chapter 09 · Using Models for Classification and Prediction
- Why not just run an ordinary regression on a zero one outcome?
- Because a straight line is unbounded. Far enough along the predictor it returns values above one or below zero, and no reading of those numbers is a probability. Near the middle of the range the two models agree closely, which is why the ordinary version looks acceptable until it is used at the ends, where decisions about the clearest cases actually live. Chapter 10 · Logistic Regression for Binary Outcomes
- What exactly is the group project asking?
- It sets one question about Melbourne housing: how prices for houses and for units vary from suburb to suburb, and which suburb characteristics account for that variation. Groups build one analysis table from quarterly price data, a socio economic index and a list of distances from the city, then 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. Chapter 11 · The Group Project on Melbourne Suburb Prices
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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