The University of Melbourne · S2 2026 · FACULTY OF MANAGEMENT

MGMT90141 Business Analysis and Decision Making

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The Complete Exam Bible · S2 2026

MGMT90141 Overview

Business Analysis and Decision Making
— A source-grounded MGMT90141 guide to decision variable, objective function, constraint and the complete published assessment structure.
  • The University of Melbourne Faculty of Business and Economics
  • Semester 2, 2026
  • a graduate coursework management subject
  • 12.5 points
  • a quantitative business-analysis and decision-science subject
  • 15% 2,000-word group assignment, 30% 4,000-word group assignment, 5% group presentation and 50% two-hour end-of-semester examination

MGMT90141 Business Analysis and Decision Making develops linear and integer programming, decision analysis, probability and regression for managerial choices. It is taught within The University of Melbourne Faculty of Business and Economics. It is a graduate coursework management subject. It carries 12.5 points.

  • Model before Solver Define variables, objective, constraints and units before opening Solver; a spreadsheet cannot repair a mistaken decision model.
  • Information has a ceiling Perfect-information value is an upper bound on what any sample or research programme should be worth.
  • Regression is conditional A coefficient describes an estimated conditional relationship under the model; it does not by itself prove causation.
  • Timing conflict visible Official sources disagree on presentation duration, so use the current LMS instruction rather than memorising a number from this guide.
MGMT90141 · The University of Melbourne
An independent, AskSia-authored study guide. AskSia is not affiliated with, endorsed by, or sponsored by The University of Melbourne; the course code and name are used for identification only.
Assessment

How MGMT90141 is assessed

ComponentWeightFormat
Assignment 115%Group literature and problem report, 2,000 words
Assignment 230%Group model and analysis report, 4,000 words
Group presentation5%Duration conflict across official sources; check LMS
End-of-semester examination50%Two hours in the examination period

The four Semester 2 components total 100%. Official sources conflict on presentation duration; use the live LMS instruction. The Subject Guide prohibits AI in every assessment.

Current dates · verify in LMS

Current MGMT90141 dates

DateItemControl
24 August 2026 at 9:00 am AESTAssignment 1Group report worth 15%.
12 October 2026 at 9:00 am AESTAssignment 2Group report worth 30%.
2-20 November 2026Examination periodConfirm the exact sitting in the official timetable.

Current-offering dates captured in the course materials. Confirm changes and exact submission settings in the live LMS.

Contents · every chapter, one map

What MGMT90141 covers

Build the course in three arcs: Decision Models, Variables and Objectives establishes the frame, Descriptive Statistics and Probability Basics deepens it, and Presentation and Examination Integration tests the complete method.

01

Decision Models, Variables and Objectives

decision variable · objective function · constraint · translate an organisational problem into a dimensional mathematical model before using software
02

Feasible Regions, Binding Resources and Sensitivity

feasible region · binding constraint · shadow price · interpret an optimal solution through resource scarcity and the range over which marginal information remains valid
03

Integer and Logical Business Decisions

binary variable · integer programming · logical constraint · model indivisible, setup and either-or decisions without accepting infeasible fractions
04

Decision Trees and Expected Outcomes

decision tree · expected monetary value · risk profile · sequence choices and uncertain events before comparing probability-weighted consequences
05

Perfect Information and the Value of Learning

perfect information · sample information · posterior probability · set an upper bound on research value and update decisions when evidence is imperfect
06

Descriptive Statistics and Probability Basics

descriptive statistic · probability model · random variable · summarise observed business data and state the probability assumptions used for a decision
07

Probability Distributions for Business Risk

probability distribution · expected value · variance · select and interpret a distribution that matches the outcome support and business process
08

Simple Regression and Residual Evidence

linear regression · residual · coefficient · estimate and diagnose a one-predictor relationship before using it for explanation or prediction
09

Multiple Regression, Confounding and Collinearity

multiple regression · confounding · multicollinearity · interpret a coefficient conditionally while diagnosing omitted structure and unstable predictors
10

Sensitivity, Validation and Managerial Recommendation

sensitivity analysis · model validation · managerial recommendation · stress-test a result and communicate a decision that remains explicit about assumptions and implementation
11

Presentation and Examination Integration

managerial recommendation · decision variable · residual · explain a model to non-experts and retrieve the quantitative workflow under time pressure

It is positioned as a quantitative business-analysis and decision-science subject.

Students select an organisational optimisation problem, review relevant modelling literature, build and solve a model, communicate it to non-experts and sit a 50% examination.

Assessment in MGMT90141 is distributed as follows: 15% 2,000-word group assignment, 30% 4,000-word group assignment, 5% group presentation and 50% two-hour end-of-semester examination

The operational assessment conditions matter here.

The examination is two hours during the examination period.

The official sources do not publish question count or format, and the current Subject Guide prohibits AI in every assessment.

What makes MGMT90141 demanding is concrete: Translating a messy organisational decision into variables, objective, constraints and data, then interpreting solver or statistical output without treating a mathematically optimal answer as automatically feasible or causal.

The rendered Semester 2 Handbook table contains no hurdle marker or published component pass threshold;

live LMS instructions still control operational requirements.

For enrolment planning, Use the current Handbook eligibility page for enrolment requirements; the build does not infer them from the subject code.

Build the course in three arcs: Decision Models, Variables and Objectives establishes the frame, Descriptive Statistics and Probability Basics deepens it, and Presentation and Examination Integration tests the complete method.

Coverage note: the Handbook says a 10-12 minute presentation while the current Subject Guide specifies 10 minutes and a separate instruction sheet says 8 minutes;

the LMS controls delivery timing.

Worked example · free

Formulate a product-mix decision before solving it

Q [5 marks]. An AskSia-authored workshop makes products A and B. Each A earns $30 and uses two labour hours; each B earns $24 and uses one labour hour. Eight labour hours are available and demand caps B at five units. Formulate the model.
  • 1Define non-negative decision variables A and B as units produced.
  • 1Write the objective: maximise 30A plus 24B.
  • 1Write the labour constraint: 2A plus B is at most 8.
  • 1Write the demand constraint: B is at most 5.
  • 1State whether divisibility is reasonable before selecting linear or integer variables.
The mathematical structure is complete only after units and variable domains are defined. The solution's managerial meaning then depends on whether fractional production is feasible and whether omitted resources matter.
Sia tip — A correct Solver setup begins with a correct verbal model, not a cell range.
Glossary

Key terms

Decision tree
A branching representation of decisions, uncertain events, probabilities and consequences evaluated in sequence.
Multiple regression
A regression model relating a response to two or more predictors while estimating each coefficient conditional on the others.
Decision variable
A controllable quantity whose value is chosen by an optimisation model to represent a managerial action.
Objective function
A mathematical expression of the performance measure that a model seeks to maximise or minimise.
Constraint
A mathematical restriction representing a resource limit, requirement or relationship among decision variables.
Feasible region
The set of all decision-variable values satisfying every constraint in an optimisation model.
Shadow price
The marginal change in the optimal objective value associated with a one-unit increase in a binding constraint's right-hand side within its valid range.
Integer programming
Optimisation in which specified decision variables must take integer or binary values rather than arbitrary fractions.
Expected value
A probability-weighted average of possible outcomes used to compare alternatives under stated beliefs and payoffs.
Bayes theorem
A rule for updating a prior probability with evidence represented by a likelihood to obtain a posterior probability.
Probability distribution
A model assigning probabilities to the possible values or intervals of a random variable.
Linear regression
A model relating a response to a linear function of one predictor plus unexplained variation.
Sensitivity analysis
The systematic examination of how a result changes when inputs, assumptions or constraints are varied.
Managerial recommendation
An action proposal that connects model result, organisational context, assumptions, trade-offs and implementation limits.
FAQ

MGMT90141 FAQ

How is MGMT90141 assessed?

15% 2,000-word group assignment, 30% 4,000-word group assignment, 5% group presentation and 50% two-hour end-of-semester examination

What is the MGMT90141 exam or final-task format?

The examination is two hours during the examination period. The official sources do not publish question count or format, and the current Subject Guide prohibits AI in every assessment.

Where do students usually lose marks in MGMT90141?

Translating a messy organisational decision into variables, objective, constraints and data, then interpreting solver or statistical output without treating a mathematically optimal answer as automatically feasible or causal.

Does MGMT90141 have a hurdle or component-level pass rule?

The rendered Semester 2 Handbook table contains no hurdle marker or published component pass threshold; live LMS instructions still control operational requirements.

Which current MGMT90141 dates are captured?

Assignment 1: 24 August 2026 at 9:00 am AEST; Assignment 2: 12 October 2026 at 9:00 am AEST; Examination period: 2-20 November 2026. Confirm any change and the exact submission setting in the live LMS.

Which offering does this MGMT90141 guide cover?

It is aligned to Semester 2, 2026; confirm your enrolled class and timetable in the current institutional system.

Is this MGMT90141 resource an official university guide?

No. It is an independent MGMT90141 study resource; current institutional instructions remain authoritative for assessment operation.

What prerequisites or restrictions apply to MGMT90141?

Use the current Handbook eligibility page for enrolment requirements; the build does not infer them from the subject code.

Study strategy

How to study for the exam

Retrieve the course map, practise the recurring method—translate the business decision into variables, objectives, constraints or uncertain outcomes, compute with checked units, test assumptions and turn the analytical result into a bounded managerial recommendation—on changed scenarios, and verify every operational assessment detail in the live institutional system.

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