MGMT90280 · Managerial Decision Analytics
The Complete Exam Bible · S2 2026Managerial Decision Analytics
MGMT90280 Managerial Decision Analytics is a postgraduate coursework subject at the University of Melbourne (Faculty of Business & Economics) that teaches you to turn business problems into models a manager can act on. It runs across three strands: prescriptive optimisation (linear, integer and nonlinear programming with Excel Solver and sensitivity analysis), decision-making under uncertainty (probability distributions and Monte Carlo simulation), and data analytics (cluster analysis, association rules, regression, time-series forecasting and classification trees, largely in Excel with the Analysis ToolPak and Analytic Solver Basic). Assessment in Managerial Decision Analytics is a timed Assignment 1 (20%), a group Assignment 2 report (25%) with a short presentation (5%), and a dominant final exam (50%). This free guide mirrors MGMT90280 Managerial Decision Analytics as taught at the University of Melbourne and maps every topic to the 50% open-book final; the full study bible then works each method end to end. Confirm exact dates, weights and rules for your cohort on Moodle / the LMS.
What MGMT90280 covers
The whole subject → one exam-ready map, from optimisation to simulation to forecasting. Each topic links to its free chapter guide and to the recurring exam question it feeds.
How MGMT90280 is assessed
| Component | Weight | Format |
|---|---|---|
| Assignment 1 (timed Respondus quiz, semi-open-book) | 20% | Individual timed quiz |
| Assignment 2 | 25% | Group analytics report/project (LMS submission) |
| Class presentation of Assignment 2 | 5% | Group oral presentation |
| Final exam | 50% | Individual 2-hour written exam (+30 min reading), open-book, Casio FX-82 only; 5 compulsory short/long-answer questions, 100 marks |
Nonlinear programming: two-plant profit maximisation
- +2Write Profit = Revenue − Cost. Z = 400(Q₁+Q₂) − (100Q₁+5Q₁²+200) − (60Q₂+5Q₂²+250) = 300Q₁ − 5Q₁² + 340Q₂ − 5Q₂² − 450. Maximise Z s.t. 30 ≤ Q₁+Q₂ ≤ 50, Q₁,Q₂ ≥ 0 (Solver GRG Nonlinear).
- +2Find the unconstrained stationary point: ∂Z/∂Q₁ = 300 − 10Q₁ = 0 → Q₁ = 30; ∂Z/∂Q₂ = 340 − 10Q₂ = 0 → Q₂ = 34. Total 64 > 50, so the upper bound Q₁+Q₂ = 50 is binding.
- +2Substitute the binding constraint Q₂ = 50 − Q₁ to reduce to one variable: Z(Q₁) = −10Q₁² + 460Q₁ + 4050.
- +2Optimise: Z′ = −20Q₁ + 460 = 0 → Q₁ = 23, so Q₂ = 27 (total 50, feasible).
- +2Revenue = 400·50 = $20,000; Cost₁ = 100·23 + 5·529 + 200 = $5,145; Cost₂ = 60·27 + 5·729 + 250 = $5,515; total cost $10,660.
- +2Profit = 20,000 − 10,660 = $9,340. The objective is concave over a convex region, so this local optimum is the global one — state the plan as the decision.
Key terms
- Decision variable
- An unknown a model solves for — e.g. hours to assign or units to produce. A linear program has a linear objective (Max or Min) in the decision variables, linear constraints (≤, =, ≥) and non-negativity (variables ≥ 0).
- Shadow price (dual value)
- The change in the optimal objective value per one-unit increase in a constraint’s right-hand side, valid only within the allowable increase/decrease range. A binding constraint (slack 0) generally has a non-zero shadow price; a non-binding constraint (slack > 0) has a shadow price of 0.
- Binding vs non-binding constraint
- A constraint is binding when it holds with equality at the optimum (slack/surplus = 0) and non-binding when there is slack left over. Relaxing a non-binding constraint does not change the optimum — the exam’s classic sensitivity read.
- LP relaxation
- Dropping the integer/binary requirement from an integer program. Because it enlarges the feasible region, for a maximisation its optimum is an upper bound on the integer optimum (a lower bound for a minimisation); used to bound and prune the search.
- Monte Carlo simulation
- Automated what-if analysis under uncertainty: each uncertain input is a random draw from its distribution, and many iterations (≥1,000) build an output distribution for risk analysis. It handles many uncertain variables but does not guarantee an optimum.
- Inverse-transform sampling
- Turning a uniform random number RN from RAND() into a sample: Uniform(a,b) via a+(b−a)·RN, Normal via NORM.INV(RN,μ,σ), and a discrete distribution by mapping RN through the cumulative-probability cutoffs.
- R² and adjusted R²
- R² = SSR/SST is the fraction of variation in y a regression explains (0–1). R² only rises as predictors are added, so adjusted R² = 1 − (1−R²)(n−1)/(n−p−1) penalises weak predictors and is the fair yardstick when comparing models.
- Overall F-test vs coefficient t-test
- The overall F = MSR/MSE (upper-tailed, df p and n−p−1) tests H₀: all slopes = 0 — is the model any good? Each coefficient t = bₖ/SE(bₖ) (two-tailed, df n−p−1) tests H₀: βₖ = 0 — does that predictor matter? Regression uses t/F, not z, because σ is estimated by MSE.
- p-value reject-rule
- Reject H₀ when the p-value < α (typically 0.05); otherwise fail to reject. “Fail to reject” means insufficient evidence, not proof the effect is zero. A 95% confidence interval for a coefficient that excludes 0 is equivalent to rejecting H₀ at 5%.
- Confidence and lift (association rules)
- For a rule “if A then C”: confidence = support(A∪C)/support(A) = P(C∣A); lift = confidence ÷ (support(C)/N). Lift > 1 means the rule beats random (useful), = 1 means independent, < 1 worse than random — so always check lift, not confidence alone.
- Euclidean distance & standardisation
- The straight-line distance d = √Σ(uⱼ−vⱼ)² between two observations, used to assign a point to its nearest cluster centroid. Because it is scale-sensitive, standardise each variable to a z-score first so no single unit dominates.
- Confusion matrix metrics
- From counts of TN, FP, FN, TP: accuracy = 1 − overall error; sensitivity (recall) = TP/(TP+FN); specificity = TN/(TN+FP); precision = TP/(TP+FP); F1 = 2·TP/(2·TP+FP+FN). ROC plots sensitivity vs 1−specificity; a larger AUC means a better classifier.
- Overfitting
- When a model fits the training data far better than it generalises — the symptom is training error much lower than validation error. Data is partitioned into training, validation and test sets, and a classification tree is pruned on the validation set to reduce it.
MGMT90280 FAQ
Can AI help me study MGMT90280?
Yes — as a study aid, not an answer service. Sia explains Managerial Decision Analytics step by step: it can walk you through linearising a percentage constraint, reading a Solver sensitivity report, sampling in a Monte Carlo model, or deciding an F-test vs a t-test, and check your own working line by line. It will not sit an assessment for you or hand over answers to a graded assignment or exam, and it cannot promise a grade — Assignment 1 is a timed Respondus quiz that forbids generative AI, so use Sia to learn the method beforehand and confirm the rules on Moodle.
Where can I find past exam papers/practice for MGMT90280?
The University of Melbourne library keeps a past-examination repository, and your subject Moodle/LMS page and seminar slides carry practice problems with worked solutions — always the most reliable source. This free guide adds a mini practice exam re-authored with fresh numbers that mirrors the five-question, 100-mark format so you can rehearse the exact skills. Ask Sia to explain any step you get stuck on, but note it will not reproduce a restricted paper or supply answers to graded work.
What can Sia do that a textbook can't?
A textbook shows one worked example; Sia adapts to your numbers and your working. Ask it to re-solve an optimisation with different constraints, explain why a shadow price is zero, generate a fresh Monte Carlo or regression problem, or pinpoint where your F-test/t-test reasoning went wrong — interactively, one step at a time. It explains any step but never promises exam answers or a specific grade; the understanding is what carries into the open-book final.
Is MGMT90280 hard?
It is broad rather than deep: the challenge is covering optimisation (LP/ILP/NLP), probability and Monte Carlo simulation, and data analytics (clustering, association rules, regression, forecasting and classification) in one semester. Most exam marks reward choosing the right method, doing clean by-hand or Excel working, and stating the decision — not heavy proofs. Students who keep up week to week and practise the recurring question types tend to find the paper predictable; use SWOTVAC to drill each thread.
Is the MGMT90280 final open-book, and is there a hurdle?
Per the subject guide and past papers, the final is an open-book written exam — student notes, seminar slides and the textbook are permitted, with a Casio FX-82 calculator only — of 2 hours writing plus 30 minutes reading time. The guide states no single-component hurdle, so the University’s standard pass rules apply; confirm the exact permitted materials and any hurdle for your cohort on Moodle before the exam.
What is examined in the MGMT90280 final exam?
Across past papers the final is five compulsory questions worth 20 marks each (100 marks), and the topic spine is stable: LP formulation with sensitivity-report reading plus a nonlinear profit model; Monte Carlo simulation; descriptive data mining (matching/Jaccard coefficients and clustering); time-series regression with a trend and seasonal dummies; and predictive classification (confusion matrix and lift/ROC). The final is worth 50%. Confirm your paper’s structure on Moodle.
How to study for the exam
Treat MGMT90280 Managerial Decision Analytics as a method-selection subject: the University of Melbourne final is open-book, so marks come from naming the right tool, doing one clean calculation and writing the explicit decision — not from memorising formulas. Because the exam is 50% and spans the whole unit, keep up week to week and build a one-page menu that links each cue to its method: percentage rules → linearise the LP and read the shadow price/slack; quadratic profit → set ∂Z/∂Q = 0 then check the binding bound; “probability that” → the right distribution and Excel function; “simulate” → inverse-transform sampling; “if…then” → confidence and lift; “significant?” → F-test then t-tests with the reject rule p < α; “forecast” → regression with trend and seasonal dummies, compared on adjusted R²/MSE. Pace by the marks at about 1.2 minutes per mark (a 20-mark question ≈ 24 minutes) and never leave a question blank — bank the part-marks for a model, a labelled table or a stated hypothesis. Rehearse with the re-authored practice set, then use SWOTVAC to close gaps; a strong final protects your WAM because it is half the subject. Confirm the exam date, permitted materials and any hurdle on Moodle.
Your AI Statistics tutor for MGMT90280
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