MFM 103 Quantitative Techniques Notes from Unnati Educations cover all four units of the GGSIPU MBA (Financial Management) Semester 1 paper in 54 pages, with 32 solved numericals, 40 exam-style questions and 13 figures. They follow the scheme for batches from 2021-22 onwards and come as a printed book, not a PDF.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Prateek Talwar, Associate Company Secretary (ACS), ICSI · Checked against MBA (Financial Management) Scheme and Syllabus, 2021-22 onwards · Updated 14 October 2026 · Editorial policy
Scheme: MBA (FM) 2021-22 onwards | Applies to: batches from 2021-22 onwards | Verified against: MBA-Financial Management.pdf, GGSIPU official scheme and syllabus
MFM 103 Quantitative Techniques at a glance: GGSIPU MBA FM Sem 1 credits, units and exam pattern
MFM 103 is one of six Core papers in MBA (Financial Management) Semester 1. It sits alongside eight other Semester 1 papers, for 25 credits in all.
| Code and title | MFM 103 Quantitative Techniques |
|---|---|
| Course and semester | MBA (Financial Management), First Semester |
| Category and credits | Core · 3 credits · L 3 (three lecture hours a week) |
| Marks | 100: 25 internal (class test 15 + assignment, viva or participation 10) + 75 end-term |
| Pass mark | Not stated in the MBA (FM) scheme PDF; check the examination rules applicable to your batch and GGSIPU Ordinance 11 |
| Applies to | Batches from 2021-22 onwards |
| End-term pattern | 75 marks; question pattern not printed in the official PDF |
| Units and hours | 4 units: 12 + 10 + 10 + 10 = 42 hours |
| Paired lab | None (MFM 117 Information Technology Management Lab is associated with MFM 109) |
The official scheme sets the assessment split, but does not specify the subject's pass percentage. The relevant examination and re-appear provisions should be checked against GGSIPU Ordinance 11.
MFM 103 Quantitative Techniques Notes unit-wise: 32 solved numericals across 4 units
These GGSIPU MBA FM Sem 1 Quantitative Techniques notes run to 54 printed pages: a cover, a two-page index and 51 pages of units. Each unit ends with a Quick Revision Table and exam questions. Every other paper is listed under GGSIPU MBA FM notes.
MFM 103 Unit 1: Descriptive Statistics
Unit 1 (pages 4 to 20) covers averages (AM, GM, HM, median and mode), dispersion, skewness and kurtosis, correlation using Karl Pearson's r and Spearman's R, two regression lines, OLS assumptions, standardised coefficients and regression-based decisions. It is the longest unit at 17 pages, with 9 solved numericals, 4 figures and 10 exam-style questions.
| Fund | Mean return | SD | CV = SD ÷ mean × 100 |
|---|---|---|---|
| Fund A | 50% | 8% | 16% (more consistent) |
| Fund B | 60% | 12% | 20% |
Quick look: comparing two funds by coefficient of variation (from page 9)
- Fund
- Fund A
- Mean return
- 50%
- SD
- 8%
- CV = SD ÷ mean × 100
- 16% (more consistent)
- Fund
- Fund B
- Mean return
- 60%
- SD
- 12%
- CV = SD ÷ mean × 100
- 20%
MFM 103 Unit 2: Probability Analysis
Unit 2 (pages 21 to 31) covers approaches to probability, addition and multiplication theorems, Bayes' theorem, random variables, binomial, Poisson and normal distributions, and expected monetary value. It has 9 solved numericals, 5 figures and 10 exam-style questions.
| Event in one minute | Working | Probability |
|---|---|---|
| No customer | e−2 | 0.1353 |
| Exactly 3 | e−2 × 8 ÷ 6 | 0.1804 |
| At most 2 | e−2 × 5 | 0.6767 |
| More than 2 | 1 − 0.6767 | 0.3233 |
Quick look: Poisson arrivals at a bank counter, λ = 2 per minute (from page 26)
- Event in one minute
- No customer
- Working
- e−2
- Probability
- 0.1353
- Event in one minute
- Exactly 3
- Working
- e−2 × 8 ÷ 6
- Probability
- 0.1804
- Event in one minute
- At most 2
- Working
- e−2 × 5
- Probability
- 0.6767
- Event in one minute
- More than 2
- Working
- 1 − 0.6767
- Probability
- 0.3233
MFM 103 Unit 3: Linear Programming
Unit 3 (pages 32 to 43) covers the formulation and graphical and simplex solutions of linear programming problems, special cases, the Hungarian assignment method, the travelling salesman model, transportation and trans-shipment. The notes describe a chairs-and-tables example that reaches 24 chairs, 8 tables and ₹1,360 profit by both graphical and simplex methods. It has 7 solved numericals, 2 figures and 11 exam-style questions.
| Method | Initial cost | Rule |
|---|---|---|
| North-West Corner | ₹885 | Ignores costs |
| Least Cost | ₹615 | Cheapest cell first |
| Vogel's Approximation | ₹505 | Penalties; MODI (page 42) identifies this solution as optimal |
Quick look: one transportation problem, three starting methods (from page 41)
- Method
- North-West Corner
- Initial cost
- ₹885
- Rule
- Ignores costs
- Method
- Least Cost
- Initial cost
- ₹615
- Rule
- Cheapest cell first
- Method
- Vogel's Approximation
- Initial cost
- ₹505
- Rule
- Penalties; MODI (page 42) identifies this solution as optimal
MFM 103 Unit 4: Decision Theory
Unit 4 (pages 44 to 54) is titled "Decision Theory and Game Theory" in the notes. It covers decision environments, five criteria under uncertainty, expected monetary value (EMV), expected opportunity loss (EOL), expected value of perfect information (EVPI), decision trees, saddle points, dominance and three methods of solving games. It has 7 solved numericals, 2 figures and 9 exam-style questions.
| Act | EMV (₹ lakh) | EOL (₹ lakh) |
|---|---|---|
| Large plant | 100 | 32 |
| Medium plant | 109 (best) | 23 (lowest) |
| Small plant | 71 | 61 |
Quick look: plant-size decision with probabilities 0.3, 0.5, 0.2 (from page 47)
- Act
- Large plant
- EMV (₹ lakh)
- 100
- EOL (₹ lakh)
- 32
- Act
- Medium plant
- EMV (₹ lakh)
- 109 (best)
- EOL (₹ lakh)
- 23 (lowest)
- Act
- Small plant
- EMV (₹ lakh)
- 71
- EOL (₹ lakh)
- 61
The lowest expected opportunity loss is ₹23 lakh, so the reported EVPI is ₹23 lakh. The original payoff matrix is needed to independently verify the EMV and EOL values.
Bayes' theorem and posterior probability in business statistics: excerpt from page 23 of the MFM 103 notes
Excerpt from page 23
What Bayes' theorem does
Bayes' theorem, named after Rev. Thomas Bayes (1763), revises probabilities when new information arrives. The starting probabilities are called prior probabilities and the revised ones are called posterior probabilities. It answers a reverse question: once an effect has been observed, how likely is it that a particular cause produced it?
The formula
If A1, A2, …, An are mutually exclusive and exhaustive events and B is an event that has occurred with nonzero probability, then P(Ai | B) = P(Ai) × P(B | Ai) ÷ Σ[P(Aj) × P(B | Aj)]. The denominator is the total probability of B, which is the sum of all the joint probabilities.
The notes set the working out in four columns: the prior P(Ai) is the initial belief, the likelihood P(B | Ai) is the conditional information, the joint is prior multiplied by likelihood, and the posterior P(Ai | B) is the joint divided by the total.
Worked example: which machine made the defective item?
A factory has three machines, M1, M2 and M3, producing 50%, 30% and 20% of its output. Their defective rates are 3%, 4% and 5%. An item picked at random turns out to be defective. Find the probability that it came from each machine.
| Machine | Prior P(Mi) | P(D | Mi) | Joint | Posterior P(Mi | D) |
|---|---|---|---|---|
| M1 | 0.50 | 0.03 | 0.015 | 0.015 ÷ 0.037 = 0.4054 |
| M2 | 0.30 | 0.04 | 0.012 | 0.012 ÷ 0.037 = 0.3243 |
| M3 | 0.20 | 0.05 | 0.010 | 0.010 ÷ 0.037 = 0.2703 |
| Total | 1.00 | — | P(D) = 0.037 | 1.0000 |
Bayes' theorem table for the three-machine problem (excerpt, page 23)
- Machine
- M1
- Prior P(Mi)
- 0.50
- P(D | Mi)
- 0.03
- Joint
- 0.015
- Posterior P(Mi | D)
- 0.015 ÷ 0.037 = 0.4054
- Machine
- M2
- Prior P(Mi)
- 0.30
- P(D | Mi)
- 0.04
- Joint
- 0.012
- Posterior P(Mi | D)
- 0.012 ÷ 0.037 = 0.3243
- Machine
- M3
- Prior P(Mi)
- 0.20
- P(D | Mi)
- 0.05
- Joint
- 0.010
- Posterior P(Mi | D)
- 0.010 ÷ 0.037 = 0.2703
- Machine
- Total
- Prior P(Mi)
- 1.00
- P(D | Mi)
- —
- Joint
- P(D) = 0.037
- Posterior P(Mi | D)
- 1.0000
The joint column adds up to 0.037, the overall probability that a randomly selected item is defective, and the three posterior probabilities add up to 1. M1 has the lowest defect rate of the three, yet it has the highest posterior probability because it makes half of all output. The same page draws this as a probability tree (Fig 2.2), with one branch per machine and a second split into defective and good items.
MFM 103 exam corner: 40 exam-style questions and 32 solved numericals by unit
Each unit ends with an Important Exam Questions list: theory first, then a bundled "Numericals" item. They carry no exam-year tags, so treat them as exam-style practice.
| Unit | Exam-style questions | Solved numericals |
|---|---|---|
| 1: Descriptive Statistics | 10 | 9 |
| 2: Probability Analysis | 10 | 9 |
| 3: Linear Programming | 11 | 7 |
| 4: Decision Theory | 9 | 7 |
| Total | 40 | 32 |
MFM 103 notes: exam questions and solved numericals per unit
- Unit
- 1: Descriptive Statistics
- Exam-style questions
- 10
- Solved numericals
- 9
- Unit
- 2: Probability Analysis
- Exam-style questions
- 10
- Solved numericals
- 9
- Unit
- 3: Linear Programming
- Exam-style questions
- 11
- Solved numericals
- 7
- Unit
- 4: Decision Theory
- Exam-style questions
- 9
- Solved numericals
- 7
- Unit
- Total
- Exam-style questions
- 40
- Solved numericals
- 32
The MFM 103 notes list 40 exam-style questions and 32 solved numericals across 4 units. The question lists are practice material, not verified previous-year papers.
One question per unit, with answer pointers:
- Unit 1: Distinguish between correlation and regression. Why are there two regression lines? Pointer: correlation measures association, while regression estimates relationships for prediction. The Y-on-X and X-on-Y regression coefficients generally differ; both regression lines pass through the point of sample means (X̄, Ȳ).
- Unit 2: Compare the binomial, Poisson and normal distributions. Pointer: compare their types, parameters, means, variances, shapes and one business application of each.
- Unit 3: Distinguish between transportation and assignment problems. Pointer: a standard one-to-one assignment model uses binary allocation variables and can be represented by a square matrix after adding dummy rows or columns where needed. A non-degenerate basic feasible transportation solution has m + n − 1 basic allocations.
- Unit 4: Explain the principles of dominance with an example. Pointer: in a two-person zero-sum payoff matrix for maximizing player A, remove a row dominated by another row with equal or higher payoffs, or a column dominated by another column with equal or lower payoffs for minimizing player B.
More practice is planned on the MFM 103 important questions page.
Who writes and checks the MFM 103 QT notes?
- Prateek Talwar, author: Founder, Unnati Educations; Associate Company Secretary (ACS), ICSI, membership no. 58704, since 15 May 2019; B.Com, University of Delhi (School of Open Learning), 2012. Author profile
Prateek Talwar also reviewed these MFM 103 Quantitative Techniques notes against the four official units. This 2026-27 edition follows the 2021-22 onwards MBA (FM) scheme and was updated on 14 October 2026. It includes Excel boxes such as =CORREL() and Solver for CO3.
Our editorial policy explains how pages are checked. The same team prepares GGSIPU important questions for every course.
Do the MFM 103 notes cover the full GGSIPU syllabus?
The supplied unit-to-page map identifies coverage for the listed topics across all four official units.
| Official unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| I: Descriptive Statistics | Central tendency; dispersion; skewness; kurtosis; correlation; regression lines; OLS assumptions and coefficients; standardized and unstandardised coefficients; regression decisions | Sections 1.2 to 1.12, pages 4 to 19 | Mapped |
| II: Probability Analysis | Probability; addition, multiplication and Bayes' theorems; binomial, Poisson and normal distributions; probability in decision making | Sections 2.1 to 2.8, pages 21 to 30 | Mapped |
| III: Linear Programming | Assumptions, formulation; graphical and simplex methods; assignment (Hungarian); travelling salesman; transportation, initial solution and optimality; trans-shipment | Sections 3.1 to 3.8, pages 32 to 43 | Mapped |
| IV: Decision Theory | States of decision making; decision tree; game theory types; dominance; algebraic, graphical and simplex solutions | Sections 4.1 to 4.9, pages 44 to 54 | Mapped |
MFM 103: official syllabus topics mapped to the notes
- Official unit
- I: Descriptive Statistics
- Official topics
- Central tendency; dispersion; skewness; kurtosis; correlation; regression lines; OLS assumptions and coefficients; standardized and unstandardised coefficients; regression decisions
- Where in the notes
- Sections 1.2 to 1.12, pages 4 to 19
- Status
- Mapped
- Official unit
- II: Probability Analysis
- Official topics
- Probability; addition, multiplication and Bayes' theorems; binomial, Poisson and normal distributions; probability in decision making
- Where in the notes
- Sections 2.1 to 2.8, pages 21 to 30
- Status
- Mapped
- Official unit
- III: Linear Programming
- Official topics
- Assumptions, formulation; graphical and simplex methods; assignment (Hungarian); travelling salesman; transportation, initial solution and optimality; trans-shipment
- Where in the notes
- Sections 3.1 to 3.8, pages 32 to 43
- Status
- Mapped
- Official unit
- IV: Decision Theory
- Official topics
- States of decision making; decision tree; game theory types; dominance; algebraic, graphical and simplex solutions
- Where in the notes
- Sections 4.1 to 4.9, pages 44 to 54
- Status
- Mapped
Verify the mapping against the MBA (Financial Management) scheme and syllabus, 2021-22 onwards. The source identifies MFM 103 on PDF page 12 and the Semester 1 scheme on page 5.
Each course in GGSIPU notes is mapped this way.
How to write MFM 103 answers: step-wise numericals, interpretation lines and theory distinctions
MFM 103 papers mix theory with numericals, so marks depend on both working and explanation. The notes' Exam Tip and Key Point boxes build three answer habits that suit both kinds of question.
1. Show every step, then state the meaning. Each solved example in the book closes with an "Interpretation" or "Decision use" line. A correlation of 0.966 gives r² ≈ 0.933, meaning approximately 93.3% of the sample variation in the dependent variable is explained by the fitted simple linear regression model with an intercept. This is an association measure, not proof of causation.
2. Follow the method order. Use the Y-on-X line to predict Y and the X-on-Y line to predict X, not the reverse. Draw and shade the normal curve before using the Z table. In simplex, show the ratio column and mark the pivot element. For games, check for a saddle point first, then dominance, followed by algebraic methods for suitable 2 × 2 games, graphical methods for suitable 2 × n games and linear programming for larger games.
3. Write theory as a distinction table. "Correlation versus regression" or "transportation versus assignment" reads best as a basis-wise table, the way the notes set them out.
Worked example: Spearman's rank correlation (page 14)
Two panels rank six trainee candidates. Panel I: 3, 1, 4, 2, 6, 5. Panel II: 2, 3, 4, 1, 5, 6.
- Calculate rank differences: D = 1, −2, 0, 1, 1, −1.
- Square and sum: ΣD² = 1 + 4 + 0 + 1 + 1 + 1 = 8.
- Apply the no-ties formula: R = 1 − 6ΣD² ÷ [N(N² − 1)].
- Substitute N = 6: R = 1 − (6 × 8) ÷ [6 × (36 − 1)] = 1 − 48 ÷ 210 = 0.7714.
- Interpret: the panels show a positive, fairly strong rank association, indicating substantial agreement in their rankings.
For tied ranks, the calculation needs tie-aware treatment, such as assigning average ranks and calculating Pearson correlation on those ranks. Practise this format on GGSIPU MBA FM important questions.
MFM 103 and the GGSIPU MBA FM Sem 1 papers it connects to
Paired lab: none. MFM 103 has no dedicated lab; the full paper list is on the GGSIPU MBA FM syllabus page.
- Unit 1 regression → MFM 107 and MFM 105: the fixed and variable cost split feeds CVP analysis; demand estimation feeds forecasting.
- Excel boxes → MFM 109 and MFM 117: the Solver tip connects with the lab's Solver exercises.
- Units 1 and 2 → Semester 2: correlation, covariance and beta return in MFM 110 Investment Analysis and Portfolio Management; regression appears in MFM 106 Business Research.
| Group | Papers |
|---|---|
| Core | MFM 101 Management Process and Organizational Behaviour; MFM 105 Managerial Economics; MFM 107 Accounting for Management; MFM 111 Marketing Management; MFM 115 Legal Aspects of Business |
| Ability Enhancement | MFM 113 Communication in Organizations |
| Skill Enhancement | MFM 109 Information Technology Management; MFM 117 Information Technology Management Lab |
Other MBA (FM) Semester 1 papers (2021-22 onwards scheme)
- Group
- Core
- Papers
- MFM 101 Management Process and Organizational Behaviour; MFM 105 Managerial Economics; MFM 107 Accounting for Management; MFM 111 Marketing Management; MFM 115 Legal Aspects of Business
- Group
- Ability Enhancement
- Papers
- MFM 113 Communication in Organizations
- Group
- Skill Enhancement
- Papers
- MFM 109 Information Technology Management; MFM 117 Information Technology Management Lab
MFM 103 QT notes FAQs
How do I get the full MFM 103 notes?
Message 9899436384 on WhatsApp using the "Get Full Notes" button on this page. The full MFM 103 Quantitative Techniques notes come only as a printed book and are not available as a PDF. Delivery takes a maximum of 5 to 7 days, and the exact time depends on your location. Our team shares all order details on WhatsApp.
What is the passing mark for MFM 103 Quantitative Techniques?
MFM 103 is marked out of 100: 25 internal and 75 end-term. The MBA (FM) scheme and syllabus does not print a pass percentage for MFM 103, so we do not quote one. Check the relevant GGSIPU Ordinance 11 provisions for examination and re-appear requirements applicable to your batch.
Which MFM 103 unit has the most solved numericals?
Units 1 and 2 tie with 9 solved numericals each. Unit 1, Descriptive Statistics, is also the longest at 17 pages and the only unit given 12 teaching hours in the official syllabus; the others get 10 each. Units 3 and 4 have 7 each, including longer examples such as simplex and game theory.
Do the MFM 103 notes follow the 2021-22 onwards MBA (FM) scheme?
Yes. The notes are mapped against the GGSIPU MBA (Financial Management) scheme and syllabus for 2021-22 onwards, where MFM 103 is a 3-credit Core paper with four units. The coverage table above gives the source's page mapping for each unit. Students should check for any revised scheme applicable to their batch.
Do I need Excel for MFM 103?
Course outcome CO3 includes computational and spreadsheet skills for business analysis. The notes show hand-worked calculations and Excel boxes on pages 11, 29 and 37, including =CORREL(), =NORM.INV() and Solver. The official assessment instructions should be followed for any examination-specific requirements.
Does MFM 103 include game theory solved by the simplex method?
Yes. Unit IV covers algebraic, graphical and simplex approaches to games. The notes describe a 2 × 2 game after dominance, a 2 × 4 game graphically and a 3 × 3 game through linear programming after adding 5 to each payoff. The reported value of the original 3 × 3 game is −29/45, unfavourable to player A; its full payoff matrix is needed to independently verify that result.
Get the full MFM 103 Quantitative Techniques Notes on WhatsApp
The full notes are a 54-page printed book with 32 solved numericals, 40 exam-style questions and 13 figures. They are sold only in printed form, not as a PDF.
| Part | Pages | Solved numericals |
|---|---|---|
| Cover and index | 1-3 (3) | None |
| Unit 1: Descriptive Statistics | 4-20 (17) | 9 |
| Unit 2: Probability Analysis | 21-31 (11) | 9 |
| Unit 3: Linear Programming | 32-43 (12) | 7 |
| Unit 4: Decision Theory | 44-54 (11) | 7 |
| Total | 1-54 (54) | 32 |
MFM 103 printed notes book: pages and numericals per unit
- Part
- Cover and index
- Pages
- 1-3 (3)
- Solved numericals
- None
- Pages
- 4-20 (17)
- Solved numericals
- 9
- Pages
- 21-31 (11)
- Solved numericals
- 9
- Pages
- 32-43 (12)
- Solved numericals
- 7
- Pages
- 44-54 (11)
- Solved numericals
- 7
- Part
- Total
- Pages
- 1-54 (54)
- Solved numericals
- 32
Delivery: within 5 to 7 days at most; the exact time may vary with your location.
Order the MFM 103 printed notes on WhatsApp (4 units, 54 pages)
Unnati Educations is an independent education provider based in Vikaspuri, Delhi. We are not affiliated with, authorised by, or endorsed by Guru Gobind Singh Indraprastha University (GGSIPU). 'GGSIPU' and 'IPU' are used only to describe the syllabus our notes follow. For official information, visit ipu.ac.in.
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