ETF1100 Business Statistics
The Complete Exam Bible · S2 2026ETF1100 Overview
- Semester 2 2026
- Monash University
- Level 1
- Excel-based statistics
- On-campus eExam
Business Statistics develops a practical chain of reasoning for decisions made from data. The unit begins with data types, tidy spreadsheet structure, frequency tables and visualisation, then moves through categorical comparisons, probability, numerical summaries and the normal distribution.
- Assessed by Weekly exercises, case work, group analysis and a supervised final
- Hardest step Choosing the correct denominator, tail or controlled comparison
- How to prepare Recompute original problems and explain each result in business language
How ETF1100 is assessed
| Component | Weight | Format |
|---|---|---|
| Tutorial submissions | 10% | Across Weeks 2 through 11, the strongest eight tutorial results count |
| Post-seminar quizzes | 5% | Weeks 2 to 10, completed before the published weekly deadline |
| Individual Case Study | 20% | One-hour individual quiz in a three-day Week 6 window |
| Group assignment | 15% | Group report with Excel workings and individual peer evaluation in Week 11 |
| Final exam | 50% | Two-hour supervised on-campus eExam with automatically marked question types |
Current ETF1100 dates
| Date | Item | Control |
|---|---|---|
| 6 September 2026 | Individual Case Study closes | The one-hour quiz closes at 11:55 pm. |
| 16 October 2026 | Group assignment due | The report, Excel workings and peer evaluation are due at 11:55 pm. |
Dates are as published in Dates are from the Semester 2 2026 Moodle assessment page.. Confirm exact deadlines and submission settings in the live LMS.
What ETF1100 covers
Follow the published sequence from data types and visualisation through uncertainty, testing, regression and Excel optimisation. Each chapter connects a statistical method to an original business decision.
Data Foundations and Excel
observational units, variable types, clean data and defensible spreadsheet structureData Foundations and Excel establishes the decisions that come before a formula: identify what one row represents, classify each variable, separate identifiers from measurements, define the target population and keep a reproducible spreadsheet structure.
- Observational units and rows
- Categorical and numerical variables
- Nominal and ordinal categories
- Discrete and continuous measurements
- Identifiers, dates and missing values
- Tidy Excel structure and audit checks
Data Visualisation
frequency tables, chart selection, honest scales and normalised comparisonsData Visualisation turns tables into defensible comparisons. The chapter selects bars for categories, histograms for numerical distributions, lines for ordered time and scatterplots for two numerical variables.
- Frequency and relative-frequency tables
- Bar charts for categories
- Histograms for distributions
- Line charts for time
- Scatterplots for relationships
- Normalised rates and honest scales
Categorical Data and Probability
contingency tables, conditional proportions, probability and associationCategorical Data and Probability builds contingency tables and reads joint, marginal and conditional information without losing the denominator.
- Joint and marginal counts
- Conditional proportions
- Contingency-table association
- Complement probabilities
- Union and overlap
- Independence and causal limits
Numerical Summaries and Distributions
centre, spread, shape, outliers, z-scores and the normal distributionNumerical Summaries and Distributions treats centre, spread and shape as one description.
- Mean and median
- Range and interquartile range
- Variance and standard deviation
- Skewness and outliers
- Z-scores and standardisation
- Normal distribution and model checks
Sampling and Confidence Intervals
random samples, sampling distributions, standard errors and interval interpretationSampling and Confidence Intervals explains why a statistic changes across random samples and how standard error quantifies that fluctuation.
- Population parameters and sample estimates
- Random sampling and generalisation
- Sampling distributions
- Standard errors
- Confidence level and margin
- Coverage interpretation and bias
Hypothesis Tests for Business Decisions
null and alternative claims, p-values, one-sided tests and decision errorsHypothesis Tests for Business Decisions turns a population claim into null and alternative hypotheses, selects a one-sided or two-sided direction, computes a standardised statistic and interprets the p-value under the null model.
- Null and alternative hypotheses
- One-sided and two-sided tests
- Test statistics and reference models
- P-values and significance levels
- Type I and Type II errors
- Population conclusions and effect size
Inference for Proportions
binary outcomes, proportion intervals, null standard errors and percentage-point effectsInference for Proportions focuses on binary business outcomes and the sample share p-hat.
- Binary outcomes and success definitions
- Sample and population proportions
- Proportion confidence intervals
- Null standard errors
- One-proportion z tests
- Percentage-point effects and conditions
Correlation and Simple Regression
scatterplots, correlation, fitted lines, residuals, prediction and causal limitsCorrelation and Simple Regression begins with a scatterplot, then quantifies linear association and fits a prediction line.
- Scatterplot direction and form
- Correlation strength and outliers
- Slope and intercept interpretation
- Predictions and residuals
- R-squared and model fit
- Extrapolation and causal limits
Multiple Regression
controlled comparisons, categorical indicators, goodness of fit and model limitationsMultiple Regression extends the fitted line to several predictors and interprets each slope as a comparison holding the other included predictors constant.
- Controlled coefficient comparisons
- Numerical predictor units
- Indicator variables and references
- R-squared and adjusted fit
- Omitted-variable risk
- Residual checks and validation
Business Modelling and Optimisation
decision variables, objective functions, constraints, feasible solutions and Solver checksBusiness Modelling and Optimisation translates a decision into variables, an objective and constraints before Excel Solver is used.
- Decision variables with units
- Objective functions
- Resource and demand constraints
- Feasible regions
- Binding constraints and slack
- Solver verification and sensitivity
The second half explains why samples vary, how confidence intervals describe uncertainty, how hypothesis tests assess business claims, and how correlation and regression model relationships. The final teaching topic translates decision variables, objectives and constraints into an Excel optimisation model.
The assessment design makes regular practice important.
Tutorial submissions contribute 10%, post-seminar quizzes contribute 5%, an Individual Case Study contributes 20%, a Group assignment contributes 15%, and the final exam contributes 50%. The final is a supervised on-campus eExam covering Weeks 1 to 10. It uses automatically marked multiple-choice, matching, dropdown, numerical-entry and select-all-that-apply questions rather than essays.
Numeric answers must follow the units and precision printed in the question.
Excel is the calculation surface, not the statistical argument. Strong work defines the observational unit and population, chooses a method that fits the variable type, shows the relevant calculation and interprets the result in business language.
The course rubric rewards explicit hypotheses, assumptions, justified methods, correct p-values and population conclusions, plus well-chosen visualisations, regression interpretation, recommendations and limitations. This guide therefore uses original datasets and scenarios to practise the complete reasoning chain without reproducing assessment or textbook questions.
Worked example · free
Assess a delivery-time claim with a one-sided test
- 1State H0: mu = 6 and H1: mu greater than 6.
- 1Compute SE = 2.1 / sqrt(49) = 0.30 hour.
- 1Compute z = (6.8 - 6.0) / 0.30 = 2.67.
- 1Use the right-tail p-value, approximately 0.0038.
- 1Reject H0 because the p-value is below 0.05.
- 1Conclude that the sample supports a population mean above the target, then note the sampling conditions.
Key terms
- Observational unit
- The person, transaction, product or event represented by one row of a dataset.
- Conditional proportion
- A category share calculated within a stated group, using that group's total as the denominator.
- Standard deviation
- A measure of typical distance from the mean in the original measurement unit.
- Standard error
- The typical sample-to-sample variation of an estimator under a specified sampling process.
- Confidence interval
- A range built from an estimate and its standard error to express plausible population parameter values.
- Null hypothesis
- The benchmark population claim used to calibrate a hypothesis test.
- P-value
- The probability, under the null model, of a result at least as extreme as the observation.
- Type I error
- Rejecting the null hypothesis when that benchmark is true.
- Correlation
- A unit-free measure of the direction and strength of a linear association between two numerical variables.
- Residual
- The observed outcome minus its fitted or predicted outcome.
- Indicator variable
- A zero-one variable that compares a named category with a reference category in a regression.
- Feasible solution
- A set of decision-variable values that satisfies every constraint in an optimisation model.
ETF1100 FAQ
How is the unit assessed?
The published structure is 10% tutorial submissions, 5% post-seminar quizzes, 20% Individual Case Study, 15% Group assignment and 50% final exam. The weekly tasks build speed with data and Excel, while the case work and report reward interpretation, justified methods, clear figures, recommendations and limitations.
What format does the final exam use?
The final is a supervised on-campus eExam designed for two hours. Questions are automatically marked and use multiple choice, matching, dropdown selections, numerical-entry boxes and select-all-that-apply formats. There are no essay questions, and all teaching content from Weeks 1 to 10 is testable.
What materials are permitted in the final?
The exam is closed book, but you may bring up to five blank working pages and one double-sided A4 paper notes sheet. The platform supplies a blank Monash-watermarked Excel workbook as the only calculation tool. A separate calculator and electronic notes are not permitted.
How hard is the statistics in this unit?
The published topics move from data types and charts into sampling uncertainty, hypothesis tests, regression and optimisation. The demanding part is usually method choice and interpretation: selecting the right denominator or test direction, checking assumptions, entering the requested precision and turning output into a population or business conclusion without causal overclaiming.
Do I need to use Excel for every calculation?
Excel is used throughout the unit and is the calculation tool supplied in the final. You still need to know what each input means and why the method applies. Short arithmetic can be clearer by hand, while probability areas, regression output and optimisation benefit from Excel once the statistical setup is correct.
Are past papers or solutions available in these materials?
The Week 12 page links a practice exam and says the tutorial pack includes a prior final, but those files are not contained in the available teaching materials for this guide. It also states that solutions are not released. The questions here are newly written from the published formats and topic sequence.
Questions students ask in each ETF1100 chapter
- Why is a postcode categorical if it contains numbers?
- The digits identify an area rather than measure an amount. Arithmetic on postcodes has no useful meaning, so the variable is nominal categorical. Storage format does not determine statistical type; the role of the values in the business question does. Chapter 01 · Data Foundations and Excel
- When should I use percentages instead of counts?
- Use percentages or rates when group sizes or exposure totals differ and the question concerns relative performance or risk. Keep counts when workload or absolute volume matters. Many business decisions need both views, because a small rate can still generate many cases in a large group. Chapter 02 · Data Visualisation
- What is the difference between joint and conditional probability?
- Joint probability uses the whole sample as its denominator and asks whether two events occur together. Conditional probability restricts the denominator to the group named after the condition. The same cell count can therefore produce different probabilities depending on the question. Chapter 03 · Categorical Data and Probability
- When should I use the median instead of the mean?
- Use the median as the typical centre when the distribution is strongly skewed or contains influential outliers. Pair it with the interquartile range. The mean may still matter for totals or resource planning, so explain the business purpose instead of applying a mechanical rule. Chapter 04 · Numerical Summaries and Distributions
- What does 95% confidence mean?
- It describes a method that captures the true population parameter in about 95% of repeated random samples under its assumptions. For one realised interval, describe a range of plausible parameter values rather than assigning a 95% probability to the fixed parameter. Chapter 05 · Sampling and Confidence Intervals
- What does a small p-value mean?
- It means the observed result, or something more extreme, would be unusual under the null model. When the p-value is at most the chosen significance level, reject the null in favour of the stated alternative. It is not the probability that the null is true. Chapter 06 · Hypothesis Tests for Business Decisions
- Why do tests and intervals use different proportion inputs?
- A test asks what sample proportions would look like if the null benchmark were true, so its standard error uses p0. An interval estimates around the observed proportion, so its usual standard error uses p-hat. Mixing them answers the wrong sampling question. Chapter 07 · Inference for Proportions
- What should I describe in a scatterplot?
- Describe direction, form, strength and unusual observations before quoting correlation. A relationship can be positive or negative, linear or curved, tight or weak, and influenced by outliers. Correlation alone can miss a strong nonlinear pattern. Chapter 08 · Correlation and Simple Regression
- What does holding other variables constant mean?
- It means comparing fitted cases with the same values of the other predictors actually included in the model. It does not control omitted factors. Name the included controls rather than claiming that every other difference has been removed. Chapter 09 · Multiple Regression
- What should I define before opening Solver?
- Write the decision variables with units, the objective and every constraint in words and symbols. Include non-negativity, integer, demand and capacity rules when relevant. Solver cannot discover a business restriction that was never entered into the model. Chapter 10 · Business Modelling and Optimisation
How to study for the exam
Build revision around decisions rather than isolated functions. First, make a one-page route from variable type to graph or summary, with the denominator written beside every categorical percentage. Second, keep confidence intervals and hypothesis tests in separate boxes: an interval estimates plausible parameter values, while a test compares evidence with a benchmark.
For proportion tests, mark that the null value enters the test standard error. Third, practise coefficient sentences for simple and multiple regression, always including units, reference categories and the exact predictors held constant. Fourth, formulate optimisation problems in words before building Solver cells, then verify every returned constraint.
Use the weekly quizzes to diagnose reading and precision errors, and use longer original problems to practise the full chain from setup to business conclusion. On the permitted A4 sheet, record routing cues and conditions instead of unexplained formulas.
During the final, read the requested quantity and entry convention before calculating, keep full precision until the last line, then check sign, unit, decimal-versus-percentage format and rounding. Finish conceptual questions by separating association from causation, statistical evidence from practical importance, and sample results from population claims.
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