BUSINFO 705 Decision Analytics
The Complete Study & Assessment Guide · Quarter 4, 2026BUSINFO 705 Overview
- Quarter 4, 2026
- Postgraduate decision analytics
- Microsoft Excel modelling
- Individual and group assessment
BUSINFO 705 Decision Analytics is a postgraduate University of Auckland course about using quantitative models to improve business processes and decisions. The captured Quarter 4 offering begins with spreadsheet modelling, moves through probability and probability distributions, and then uses Monte Carlo simulation to explore risk.
- Assessed by An individual case, an interactive task, two quizzes and a group project report.
- Hardest step Matching the business process to a defensible probability model and parameter set.
- How to prepare Rebuild formulas without notes, check units, then explain what would reverse the decision.
What you get in the full BUSINFO 705 Study & Assessment Guide
- 5 chapters, 25 pages — every assessed topic, week by week
- 13 original practice questions, each with a model answer
- 5 worked examples, solved the way BUSINFO 705 marks
- 10 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Quiz * 2 (30%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How BUSINFO 705 is assessed
| Component | Weight | Format |
|---|---|---|
| Individual Case Study | 25% | Individual test; the captured Canvas summary dates the Quarter 4 task on 17 November 2026 |
| Interactive Assessment (Individual) | 15% | Coursework completed individually |
| Quiz * 2 | 30% | Coursework completed individually; Quiz 1 covers Weeks 1 to 3 without notes or online resources |
| Group Project Report | 30% | Group coursework; the captured Canvas summary dates submission on 25 November 2026 |
The four weights come from the University lecture assessment slide and total 100%. The Canvas course summary supplies the current Quarter 4 dates. No hurdle is stated for an individual component in the captured course materials.
What other students are asking about BUSINFO 705
- Where can I find lecture notes or study guides for BUSINFO 705 at auckland?
- The BUSINFO 705 study guide in AskSia Library covers Decision Analytics in 5 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in BUSINFO 705?
- No final exam is listed in the offering this AskSia Library guide is built from; BUSINFO 705 is assessed by Individual Case Study 25%, Interactive Assessment (Individual) 15%, Quiz * 2 30%, Group Project Report 30%.
- What does BUSINFO 705 cover on probability evidence and distribution summaries?
- In AskSia Library's BUSINFO 705 guide, the Probability Evidence and Distribution Summaries chapter explains empirical probability, valid mass functions, expected value, variance and standard deviation.
- What does BUSINFO 705 cover on discrete and continuous probability models?
- In AskSia Library's BUSINFO 705 guide, the Discrete and Continuous Probability Models chapter explains discrete uniform, binomial, Poisson, continuous uniform and normal models.
- What does BUSINFO 705 cover on monte carlo simulation for risk?
- In AskSia Library's BUSINFO 705 guide, the Monte Carlo Simulation for Risk chapter explains input distributions, random trials, output histograms, loss probability and interval interpretation.
Current BUSINFO 705 dates
| Date | Item | Control |
|---|---|---|
| October 22, 2026 | Quiz 1 | Use the time for your Canvas stream. |
| November 5, 2026 | Quiz 2 | Use the time for your Canvas stream. |
| November 17, 2026 | Individual Case Study | Canvas summary lists 20:30. |
| November 25, 2026 | Group Project | Canvas summary lists 23:59. |
Dates are as published in the Quarter 4, 2026 Canvas course summary. Confirm exact deadlines and submission settings in the live LMS.
What BUSINFO 705 covers
Five evidence-led chapters move from spreadsheet architecture through probability models to Monte Carlo simulation and paired policy comparisons.
Decision Models and Spreadsheet Architecture
Analytics types, decision variables, transparent formulas, what-if tools and cost crossover logicThis chapter turns a business question into a spreadsheet that another person can audit.
- Descriptive, predictive and prescriptive analytics
- Problem definition, criteria and alternatives
- Decision variables and model parameters
- Input, formula and output separation
- One-way and two-way data tables
- Goal Seek and cost crossovers
Probability Evidence and Distribution Summaries
Empirical probability, valid mass functions, expected value, variance and standard deviationThis chapter builds probability evidence from experiments, outcomes and observed frequencies. It distinguishes discrete counts from continuous measurements and checks that every discrete probability is nonnegative and that total mass equals one.
- Experiments, outcomes and sample spaces
- Classical, empirical and subjective probability
- Discrete and continuous random variables
- Valid probability mass functions
- Expected value as a weighted mean
- Variance and standard deviation
Discrete and Continuous Probability Models
Discrete uniform, binomial, Poisson, continuous uniform and normal modelsThis chapter chooses probability models from the data-generating process. It compares discrete uniform, binomial and Poisson counts with continuous uniform and normal measurements.
- Discrete uniform outcomes
- Binomial trials and combinations
- Poisson counts and interval scaling
- Continuous uniform interval probability
- Normal location and spread
- Z-scores and tail direction
Monte Carlo Simulation for Risk
Input distributions, random trials, output histograms, loss probability and interval interpretationThis chapter replaces a single forecast with repeated scenarios. It assigns distributions to uncertain inputs, generates a coherent set of draws for each trial and sends them through a verified spreadsheet relationship.
- Input distributions and parameter evidence
- Random draws in Excel
- Coherent simulation trials
- Output histograms and summary statistics
- Loss probability at a threshold
- Probability and confidence intervals
Comparing Policies with Simulation Evidence
Paired scenarios, profit differences, confidence intervals, robustness and operating recommendationsThis chapter compares operating policies under identical uncertain scenarios. Each trial sends the same demand draw through both policy formulas and records a difference in a fixed direction.
- Piecewise inventory profit logic
- Common random numbers
- Paired policy differences
- Confidence intervals around mean gain
- Win frequency and downside risk
- Sensitivity and implementation triggers
Its wider published course plan continues into linear and integer optimisation, decision analysis, and data visualisation and management. This guide concentrates on the captured teaching body, where lecture notes, lab questions and answer guides provide enough evidence to teach the method accurately. The course repeatedly connects a numerical model with a qualitative decision process.
Students must identify the problem, criteria and alternatives; distinguish decision variables from parameters; build formulas whose inputs remain visible; represent uncertainty with an appropriate distribution; and explain how the output changes a recommendation. Excel is the working environment, with data tables, Goal Seek, SUMPRODUCT and random-number functions appearing as tools rather than ends in themselves.
The key intellectual move is auditability: another reader should be able to trace a recommendation from the stated assumptions through the model relationship to the output, challenge an assumption and see the result update. Assessment combines an individual case study, an individual interactive assessment, two quizzes and a group project report.
Quiz 1 prohibits notes and online resources, runs for 45 minutes and covers Weeks 1 to 3 through conceptual and short numerical questions. The later case and project work reward a transparent chain from assumptions to evidence and a recommendation whose limits are stated plainly.
Worked example · free
Find a make-or-buy crossover and test the decision
- 1Write total make cost as 48,000 + 7.60q and total buy cost as 10.00q.
- 2Set the expressions equal: 48,000 + 7.60q = 10.00q, so 2.40q = 48,000.
- 1The crossover is q = 20,000 units. Below it buying is cheaper; above it making is cheaper.
- 2At 26,000 units, make cost is $245,600 and buy cost is $260,000, so making saves $14,400.
Key terms
- Decision variable
- A quantity controlled by the decision maker and changed to compare alternatives.
- Model parameter
- An input supplied by evidence or assumption that shapes the model relationship.
- What-if model
- A spreadsheet structure that recalculates outputs when one or more inputs change.
- Goal Seek
- An Excel tool that finds the single input needed to reach a target output.
- Random variable
- A numerical representation of the outcome of an uncertain process.
- Probability mass
- Probability assigned to a particular value of a discrete random variable.
- Expected value
- The probability-weighted long-run centre of a random variable.
- Standard deviation
- A measure of the typical scale of variation around a mean in original units.
- Binomial distribution
- A model for success counts in fixed independent trials with constant success probability.
- Poisson distribution
- A model for event totals during a stated span when average arrival intensity stays stable.
- Monte Carlo simulation
- Repeated random sampling from input models to create a distribution of outputs.
- Paired difference
- The output under one policy minus another under the same simulated scenario.
- Probability interval
- An interval describing the central spread of individual simulated outcomes.
- Confidence interval
- An interval expressing sampling uncertainty around an estimated parameter such as a mean.
BUSINFO 705 FAQ
How is the course assessed?
The published assessment slide assigns 25% to the Individual Case Study, 15% to the Interactive Assessment, 30% to two quizzes and 30% to the Group Project Report. These four components total 100%. The captured materials do not state a hurdle for an individual component.
What is the first quiz format?
Quiz 1 covers Weeks 1 to 3, lasts 45 minutes and permits one attempt. It contains 20 multiple-choice questions, including conceptual and short numerical or application items. Students must complete it individually in Canvas without notes, generative AI or other unauthorised help.
Which probability distributions should I know first?
Start with discrete uniform, binomial and Poisson models, then continuous uniform and normal models. Learn the support, process and parameters for each. The Quiz 1 guide explicitly excludes exponential and triangular distributions from that quiz, even though the lecture later introduces them.
What makes a spreadsheet model defensible?
Separate inputs from formulas, label units, keep decision variables distinct from parameters, and make outputs traceable. Test a simple case by hand and state the assumption most likely to reverse the recommendation. Clear formatting helps, but visible logic and directional checks make the model auditable.
How should I interpret a simulation result?
Report the output centre, the probability of crossing the relevant decision threshold and an interval with its correct meaning. A probability interval describes individual outcomes, while a confidence interval for the mean describes uncertainty in that estimated mean. Neither validates the input assumptions by itself.
How do paired policy simulations work?
Feed the same uncertain draw through both policy formulas in each trial, then subtract the outputs in a fixed direction. The paired differences isolate the policy effect from scenario noise. Report their mean, confidence interval, win frequency and downside before recommending an operating policy.
How should a group report present the model?
Keep the input table, policy formulas, output summary and recommendation consistent. Use one subtraction direction, label every chart with units and thresholds, and include a hand-checked scenario. The report should state the preferred action, quantify uncertainty and name the assumption that would trigger a review.
Questions students ask in each BUSINFO 705 chapter
- When should I use Goal Seek?
- Use Goal Seek when one input may change and you know the target value of one output. Define the output formula first, then set that cell to the target by changing the true input cell. Check the returned value in the original relationship. Chapter 01 · Decision Models and Spreadsheet Architecture
- What makes a discrete distribution valid?
- Every assigned probability must be at least zero, no probability may exceed one, and all probabilities over the complete support must sum to one. Also check that the listed support contains every value treated as possible by the model. Chapter 02 · Probability Evidence and Distribution Summaries
- How do binomial and Poisson models differ?
- Binomial models successes among a fixed number of opportunities and needs n and p. Poisson models event counts over a fixed interval and needs the interval mean. Both produce counts, but their process assumptions and supports are different. Chapter 03 · Discrete and Continuous Probability Models
- Why use simulation instead of three scenarios?
- Three scenarios show selected possibilities but not their relative likelihood. Simulation draws from input distributions many times and creates an output distribution, letting you estimate threshold probabilities, variability and intervals under the stated uncertainty model. Chapter 04 · Monte Carlo Simulation for Risk
- Why should policies share the same demand draw?
- A shared scenario keeps the uncertain environment constant while the policy changes. This isolates the policy effect and reduces noise in the paired difference. Separate random draws mix the decision effect with two different operating conditions. Chapter 05 · Comparing Policies with Simulation Evidence
How to prepare for the assessments
Study the course as a chain of decisions rather than a catalogue of Excel functions. First rebuild the Week 1 cost and what-if relationships without notes, keeping inputs, formulas and outputs separate. Then practise probability from the variable outward: state the support, choose the distribution, name each parameter and sketch the event before calculating.
For Quiz 1, work in short sessions without reference materials because the official format allows one 45-minute attempt and mixes concepts with numerical items. Check units, parameters and rounding on every answer. Before simulation work, verify the deterministic formula with a hand-calculated scenario. In a simulation, inspect several trial rows and distinguish an output distribution from uncertainty about its mean.
For policy comparisons, use the same random scenario for both alternatives and keep the subtraction direction fixed. Finish every case or project recommendation with the decision metric, uncertainty, sensitive assumption and review trigger. This routine protects both the arithmetic and the qualitative judgement the course expects.
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