MGMT90141 Business Analysis and Decision Making
The Complete Exam Bible · S2 2026MGMT90141 Overview
- The University of Melbourne
- Semester 2, 2026
- a graduate coursework management subject
- 12.5 points
- Quantitative business decision science
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.
How MGMT90141 is assessed
| Component | Weight | Format |
|---|---|---|
| Assignment 1 | 15% | Group literature and problem report, 2,000 words |
| Assignment 2 | 30% | Group model and analysis report, 4,000 words |
| Group presentation | 5% | Duration conflict across official sources; check LMS |
| End-of-semester examination | 50% | 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 MGMT90141 dates
| Date | Item | Control |
|---|---|---|
| 24 August 2026 at 9:00 am AEST | Assignment 1 | Group report worth 15%. |
| 12 October 2026 at 9:00 am AEST | Assignment 2 | Group report worth 30%. |
| 2-20 November 2026 | Examination period | Confirm the exact sitting in the official timetable. |
Dates are as published in the current course materials. Confirm exact deadlines and submission settings in the live LMS.
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.
Decision Models, Variables and Objectives
decision variable · objective function · constraint · translate an organisational problem into a dimensional mathematical model before using softwareDefine decision variable
The course material gives this chapter a concrete anchor: The Week 1 guide and assignment Q&A require students to select a real organisational optimisation problem and articulate its requirements.
- decision variable
- objective function
- constraint
- Applying decision variable
- Limits of objective function and constraint
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 validDefine feasible region
The course material gives this chapter a concrete anchor: The LP application weeks and Solver requirement join formulation, solution and interpretation rather than treating optimisation as button pressing.
- feasible region
- binding constraint
- shadow price
- Applying feasible region
- Limits of binding constraint and shadow price
Integer and Logical Business Decisions
binary variable · integer programming · logical constraint · model indivisible, setup and either-or decisions without accepting infeasible fractionsDefine binary variable
The course material gives this chapter a concrete anchor: Week 4 covers integer applications, while the assignment guidance permits LP or IP models for organisational problems.
- binary variable
- integer programming
- logical constraint
- Applying binary variable
- Limits of integer programming and logical constraint
Decision Trees and Expected Outcomes
decision tree · expected monetary value · risk profile · sequence choices and uncertain events before comparing probability-weighted consequencesDefine decision tree
The course material gives this chapter a concrete anchor: The current Week 5 topic introduces decision analysis with perfect information after optimisation applications.
- decision tree
- expected monetary value
- risk profile
- Applying decision tree
- Limits of expected monetary value and risk profile
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 imperfectDefine perfect information
The course material gives this chapter a concrete anchor: Weeks 5 and 6 distinguish perfect from sample information within the same decision-analysis framework.
- perfect information
- sample information
- posterior probability
- Applying perfect information
- Limits of sample information and posterior probability
Descriptive Statistics and Probability Basics
descriptive statistic · probability model · random variable · summarise observed business data and state the probability assumptions used for a decisionDefine descriptive statistic
The course material gives this chapter a concrete anchor: Week 7 introduces descriptive statistics and probability with the Analysis ToolPak as a computational aid.
- descriptive statistic
- probability model
- random variable
- Applying descriptive statistic
- Limits of probability model and random variable
Probability Distributions for Business Risk
probability distribution · expected value · variance · select and interpret a distribution that matches the outcome support and business processDefine probability distribution
The course material gives this chapter a concrete anchor: Week 8 moves from probability basics to distributions before regression begins.
- probability distribution
- expected value
- variance
- Applying probability distribution
- Limits of expected value and variance
Simple Regression and Residual Evidence
linear regression · residual · coefficient · estimate and diagnose a one-predictor relationship before using it for explanation or predictionDefine linear regression
The course material gives this chapter a concrete anchor: Week 9 introduces linear regression after probability, creating a bridge from uncertainty to empirical prediction.
- linear regression
- residual
- coefficient
- Applying linear regression
- Limits of residual and coefficient
Multiple Regression, Confounding and Collinearity
multiple regression · confounding · multicollinearity · interpret a coefficient conditionally while diagnosing omitted structure and unstable predictorsDefine multiple regression
The course material gives this chapter a concrete anchor: Week 10 extends regression to several predictors immediately before revision and the final group submission.
- multiple regression
- confounding
- multicollinearity
- Applying multiple regression
- Limits of confounding and multicollinearity
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 implementationDefine sensitivity analysis
The course material gives this chapter a concrete anchor: The assignments ask teams to connect literature, mathematical model, solution, live demonstration and managerial contribution.
- sensitivity analysis
- model validation
- managerial recommendation
- Applying sensitivity analysis
- Limits of model validation and managerial recommendation
Presentation and Examination Integration
managerial recommendation · decision variable · residual · explain a model to non-experts and retrieve the quantitative workflow under time pressureDefine managerial recommendation
The course material gives this chapter a concrete anchor: The current assessment design asks the same analytical work to travel through reports, a short group presentation and a substantial examination.
- managerial recommendation
- decision variable
- residual
- Applying managerial recommendation
- Limits of decision variable and residual
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
- 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.
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.
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.
Questions students ask in each MGMT90141 chapter
- What is the main task in Decision Models, Variables and Objectives?
- Translate an organisational problem into a dimensional mathematical model before using software. Chapter 01 · Decision Models, Variables and Objectives
- What is the main task in Feasible Regions, Binding Resources and Sensitivity?
- Interpret an optimal solution through resource scarcity and the range over which marginal information remains valid. Chapter 02 · Feasible Regions, Binding Resources and Sensitivity
- What is the main task in Integer and Logical Business Decisions?
- Model indivisible, setup and either-or decisions without accepting infeasible fractions. Chapter 03 · Integer and Logical Business Decisions
- What is the main task in Decision Trees and Expected Outcomes?
- Sequence choices and uncertain events before comparing probability-weighted consequences. Chapter 04 · Decision Trees and Expected Outcomes
- What is the main task in Perfect Information and the Value of Learning?
- Set an upper bound on research value and update decisions when evidence is imperfect. Chapter 05 · Perfect Information and the Value of Learning
- What is the main task in Descriptive Statistics and Probability Basics?
- Summarise observed business data and state the probability assumptions used for a decision. Chapter 06 · Descriptive Statistics and Probability Basics
- What is the main task in Probability Distributions for Business Risk?
- Select and interpret a distribution that matches the outcome support and business process. Chapter 07 · Probability Distributions for Business Risk
- What is the main task in Simple Regression and Residual Evidence?
- Estimate and diagnose a one-predictor relationship before using it for explanation or prediction. Chapter 08 · Simple Regression and Residual Evidence
- What is the main task in Multiple Regression, Confounding and Collinearity?
- Interpret a coefficient conditionally while diagnosing omitted structure and unstable predictors. Chapter 09 · Multiple Regression, Confounding and Collinearity
- What is the main task in Sensitivity, Validation and Managerial Recommendation?
- Stress-test a result and communicate a decision that remains explicit about assumptions and implementation. Chapter 10 · Sensitivity, Validation and Managerial Recommendation
- What is the main task in Presentation and Examination Integration?
- Explain a model to non-experts and retrieve the quantitative workflow under time pressure. Chapter 11 · Presentation and Examination Integration
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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