MS-103 Quantitative Techniques Notes for GGSIPU MBA Semester 1 run to 83 pages across 4 units, with 64 solved examples, 35 important theory questions and a quick revision table for every unit. They follow the MBA scheme and syllabus that applies from the 2021-22 academic session, and are sold as a printed book.
Written by Prateek Talwar, Founder, Unnati Educations · Checked against GGSIPU MBA Scheme and Syllabus 2021-22 onwards (verified 13 Oct 2026) · Updated 13 October 2026 · Editorial policy
Scheme: MBA Scheme and Syllabus 2021-22 onwards | Applies to: batches from the 2021-22 academic session | Verified against: GGSIPU MBA Scheme and Syllabus 2021-22 onwards (Approved by AC Sub Committee), 13 Oct 2026
MS-103 Quantitative Techniques at a glance: GGSIPU MBA Sem 1 credits, units and exam pattern
MS-103 brings statistics and operations research into the first semester of the GGSIPU MBA. It sits with eight other GGSIPU MBA Semester 1 papers, and its four units move from describing data to probability, linear programming and decisions against a rival.
| Code and title | MS-103 Quantitative Techniques |
|---|---|
| Course and semester | MBA, Semester 1 |
| Category and credits | Core; L-3 (3 lecture hours a week, no T/P); 3 credits |
| Marks | 100: 25 internal (class test 15 + assignments, viva or participation 10) + 75 end-term |
| Applies to | MBA batches from the 2021-22 academic session |
| End-term pattern | 75 marks; question pattern not printed in the official PDF |
| Exam duration | 3 hours |
| Units and teaching hours | 4 units: 12 + 10 + 10 + 10 = 42 hours |
| Pass rules | Not stated in the MBA scheme PDF; see GGSIPU Ordinance 11 (examinations and results) |
| Paired lab | None. The Semester 1 lab, MS-117, is based on MS-109 |
MS-103 Quantitative Techniques Notes unit-wise: 64 solved examples across 4 units
These GGSIPU MBA Sem 1 Quantitative Techniques notes run concept, formula box, solved example, revision table, exam questions in every unit. Notes for the other papers sit under GGSIPU MBA notes. The official PDF numbers the units only, so the titles below are the notes' own. See the sample PDF above.
MS-103 Unit 1: Descriptive Statistics, Correlation & Regression
The largest unit, pages 4 to 33. One data set of 100 retail shops is reused for mean, median, mode, quartiles, mean deviation, SD and skewness, so every measure connects. Then moments, kurtosis, Pearson's and Spearman's correlation, both regression lines, OLS, standardised coefficients and a regression-based budget decision.
Counts: 30 pages, 26 solved examples, 9 theory questions and 7 numerical practice sets.
| Average | Use it for | Business case |
|---|---|---|
| Weighted mean | Items of unequal importance | Supplier rating, WACC |
| Median | Extreme values, open-end classes | Salaries, house prices |
| Geometric mean | Rates and percentage growth | Average sales growth |
| Harmonic mean | Speeds, equal spend per item | Average delivery speed |
Quick look: choosing an average (from page 12)
- Average
- Weighted mean
- Use it for
- Items of unequal importance
- Business case
- Supplier rating, WACC
- Average
- Median
- Use it for
- Extreme values, open-end classes
- Business case
- Salaries, house prices
- Average
- Geometric mean
- Use it for
- Rates and percentage growth
- Business case
- Average sales growth
- Average
- Harmonic mean
- Use it for
- Speeds, equal spend per item
- Business case
- Average delivery speed
MS-103 Unit 2: Probability Analysis & Probability Distributions
Pages 34 to 49. Four approaches to probability, the addition and multiplication theorems, and Bayes' theorem as a prior-likelihood-posterior table. Then expected value, the binomial, Poisson and normal distributions with a Z-area table, and EMV for choosing between two projects.
Counts: 16 pages, 17 solved examples, 8 theory questions and 8 numerical practice sets.
| Basis | Binomial | Poisson |
|---|---|---|
| Number of trials | Fixed and finite (n) | Very large |
| Chance of success | Any constant p | Very small (rare events) |
| Parameters | Two: n and p | One: m |
| Mean and variance | Mean np > variance npq | Mean = variance = m |
Quick look: binomial vs Poisson distribution (from page 44)
- Basis
- Number of trials
- Binomial
- Fixed and finite (n)
- Poisson
- Very large
- Basis
- Chance of success
- Binomial
- Any constant p
- Poisson
- Very small (rare events)
- Basis
- Parameters
- Binomial
- Two: n and p
- Poisson
- One: m
- Basis
- Mean and variance
- Binomial
- Mean np > variance npq
- Poisson
- Mean = variance = m
MS-103 Unit 3: Linear Programming, Transportation & Assignment
Pages 50 to 68. Three LP formulations (product mix, diet, media selection), one product-mix problem solved both graphically and by simplex, and shadow prices read from the final table. Then transportation with NWCR, LCM, VAM and MODI on one problem, the Hungarian method with and without revision, and a four-city travelling salesman route.
Counts: 19 pages, 12 solved examples, 9 theory questions and 6 numerical practice sets.
| Corner | (x1, x2) | Z |
|---|---|---|
| A | (8, 0) | 320 |
| B | (4, 8) | 400 (maximum) |
| C | (0, 12) | 360 |
Quick look: Z = 40x1 + 30x2 at each corner point (from page 54)
- Corner
- A
- (x1, x2)
- (8, 0)
- Z
- 320
- Corner
- B
- (x1, x2)
- (4, 8)
- Z
- 400 (maximum)
- Corner
- C
- (x1, x2)
- (0, 12)
- Z
- 360
MS-103 Unit 4: Decision Theory & Game Theory
Pages 69 to 82. The four decision environments, all five criteria under uncertainty on one investment table, then EMV, EOL and EVPI with a bakery stocking problem. Game theory covers saddle points, dominance, and algebraic, graphical and simplex solutions.
Counts: 14 pages, 9 solved examples, 9 theory questions and 7 numerical practice sets.
Quick look: picking the method for a game (from page 80)
- Saddle point (maximin = minimax)? Pure strategies; done.
- No saddle point: delete dominated rows and columns.
- Left with 2 × 2: use the algebraic formula.
- 2 × n or m × 2: graphical method, then the 2 × 2 formula.
- 3 × 3 or larger: convert to an LP and use simplex.
Decision tree roll-back in decision theory: excerpt from page 73 of the MS-103 notes
Excerpt from page 73
Roll-back (backward induction) method
A decision tree is drawn from left to right, in the order in which decisions are taken and events happen. Probabilities go on the chance branches and the pay-offs, net of costs, go at the end points. The tree is then solved from right to left.
- At every chance node, work out the EMV of the branches leaving it.
- At every decision node, keep the branch with the highest EMV and cut off the others (marked with a double stroke).
- The value left at the first decision node is the optimal EMV, and the uncut branches show the best path.
Worked case: large plant or small plant?
A company can build a large plant for Rs. 200 lakh or a small plant for Rs. 100 lakh. Demand will be high with probability 0.6 or low with probability 0.4. The large plant earns Rs. 500 lakh in high demand and Rs. 150 lakh in low demand. The small plant earns Rs. 150 lakh in low demand. If demand is high, the small plant can be left as it is (Rs. 250 lakh) or expanded for another Rs. 80 lakh (Rs. 450 lakh).
Step 1, the second decision: with a small plant and high demand, expanding gives 450 − 80 = 370 against 250 without expansion, so expansion is chosen and that node carries 370.
Step 2, small-plant chance node: EMV = 0.6 × 370 + 0.4 × 150 = 222 + 60 = 282. Net of the plant cost, 282 − 100 = Rs. 182 lakh.
Step 3, large-plant chance node: EMV = 0.6 × 500 + 0.4 × 150 = 300 + 60 = 360. Net of the plant cost, 360 − 200 = Rs. 160 lakh.
Step 4, first decision: the small plant (182) beats the large plant (160).
What the answer means
Build the small plant and expand it only if demand turns out high. The expected net pay-off is Rs. 182 lakh. The option to expand later is worth more than building big on day one, which is exactly the kind of insight a two-stage tree brings out and a single pay-off table hides.
Trees make a multi-stage problem easy to explain and force you to think through every outcome. They also grow messy when there are many stages, need reliable probabilities, and, because they rest on EMV, ignore how much risk the decision maker is willing to carry.
MS-103 exam corner: 35 theory questions and 64 solved examples by unit
The notes do not tag questions with marks. Each unit ends with important theory questions and a numerical practice list that points back to the solved examples.
| Unit | Theory questions | Numerical practice sets | Solved examples |
|---|---|---|---|
| I: Statistics, correlation, regression | 9 | 7 | 26 |
| II: Probability and distributions | 8 | 8 | 17 |
| III: LP, transportation, assignment | 9 | 6 | 12 |
| IV: Decision theory, game theory | 9 | 7 | 9 |
| Total | 35 | 28 | 64 |
MS-103 exam-practice material per unit in the notes
- Unit
- I: Statistics, correlation, regression
- Theory questions
- 9
- Numerical practice sets
- 7
- Solved examples
- 26
- Unit
- II: Probability and distributions
- Theory questions
- 8
- Numerical practice sets
- 8
- Solved examples
- 17
- Unit
- III: LP, transportation, assignment
- Theory questions
- 9
- Numerical practice sets
- 6
- Solved examples
- 12
- Unit
- IV: Decision theory, game theory
- Theory questions
- 9
- Numerical practice sets
- 7
- Solved examples
- 9
- Unit
- Total
- Theory questions
- 35
- Numerical practice sets
- 28
- Solved examples
- 64
The MS-103 notes list 35 important theory questions and solve 64 examples across 4 units, with a quick revision table at the end of each unit.
One exam-style question per unit, with where the answer sits:
- Unit I: State the assumptions of the classical linear regression (OLS) model. Pointer: page 28 lists nine assumptions, from linearity to normal errors; page 29 explains BLUE.
- Unit II: Explain the properties of the normal distribution. Pointer: page 45, eight properties including the 68.27/95.45/99.73 per cent area rule.
- Unit III: What is degeneracy in a transportation problem? How is it resolved? Pointer: page 61, occupied cells below m + n − 1, fixed with a tiny ε.
- Unit IV: Show that maximum EMV and minimum EOL give the same decision. Pointer: pages 71-72, where Bonds win on both and EVPI is Rs. 7 thousand.
More practice sits in the MS-103 important questions.
Who writes and checks the MS-103 Quant notes?
- Prateek Talwar, author and editor of these notes. 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
The 2026-27 edition of the MS-103 Quantitative Techniques IPU notes follows the GGSIPU MBA Scheme and Syllabus 2021-22 onwards; last updated 13 October 2026. How we check facts is in our editorial policy.
The same team prepares GGSIPU important questions for every course.
Do the MS-103 notes cover the full GGSIPU syllabus?
Yes. Every topic in the four official MS-103 units has a section in the notes. Only course outcome CO3 (spreadsheet skills) is not covered; the notes do not teach Excel.
| Official unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| Unit I (12 hours) | Central tendency, dispersion, skewness, kurtosis; correlation; regression lines; OLS; standardised coefficients; regression-based decisions | Sections 1-7, pages 4-31 | Covered |
| Unit II (10 hours) | Probability; addition, multiplication, Bayes' theorems; binomial, Poisson, normal; probability in decisions | Sections 1-7, pages 34-47 | Covered |
| Unit III (10 hours) | LP formulation; graphical and simplex; Hungarian assignment; travelling salesman; transportation and optimality; trans-shipment | Sections 1-7, pages 50-66 | Covered |
| Unit IV (10 hours) | States of decision making; decision trees; game types; dominance; algebraic, graphical, simplex solutions | Sections 1-2, pages 69-80 | Covered |
MS-103 official syllabus mapped to the notes
- Official unit
- Unit I (12 hours)
- Official topics
- Central tendency, dispersion, skewness, kurtosis; correlation; regression lines; OLS; standardised coefficients; regression-based decisions
- Where in the notes
- Sections 1-7, pages 4-31
- Status
- Covered
- Official unit
- Unit II (10 hours)
- Official topics
- Probability; addition, multiplication, Bayes' theorems; binomial, Poisson, normal; probability in decisions
- Where in the notes
- Sections 1-7, pages 34-47
- Status
- Covered
- Official unit
- Unit III (10 hours)
- Official topics
- LP formulation; graphical and simplex; Hungarian assignment; travelling salesman; transportation and optimality; trans-shipment
- Where in the notes
- Sections 1-7, pages 50-66
- Status
- Covered
- Official unit
- Unit IV (10 hours)
- Official topics
- States of decision making; decision trees; game types; dominance; algebraic, graphical, simplex solutions
- Where in the notes
- Sections 1-2, pages 69-80
- Status
- Covered
Official source: GGSIPU MBA Scheme and Syllabus 2021-22 onwards.
Every course in GGSIPU notes is mapped to its official PDF the same way.
How to write MS-103 answers: step-deviation tables, Bayes revision tables and corner-point solutions
In MS-103 the working matters as much as the answer. The notes' exam tips come down to five habits.
- Show the full table. Use the step-deviation method and write every column (m, d′, fd′, fd′², cf); one table then serves mean, median and SD.
- Use short-cuts that are also checks. Write 1 − P(none) for "at least one"; if two regression coefficients multiply to more than 1, swap the lines.
- Sketch before you calculate. Shade the area on a rough bell curve; add areas on opposite sides of the mean, subtract on the same side.
- Define before you model. State what x1 and x2 mean, with units, before the LP objective.
- Say whether the table is profit or cost. Decision criteria reverse for cost tables; in game graphs, number every line and mark the maximin point.
Worked example (page 27): means and r from two regression lines
Lines: 3X + 2Y = 26 and 6X + Y = 31. Solving them together gives X̄ = 4, Ȳ = 7. Take the first as Y on X (byx = −1.5) and the second as X on Y (bxy = −1/6). The product, 0.25, is below 1, so the guess holds, and r = −√0.25 = −0.5. Write that check line in the answer.
Practise this format on GGSIPU MBA important questions.
MS-103 and the GGSIPU MBA Sem 1 papers it connects to
MS-103 has no lab; the full paper list is on the GGSIPU MBA syllabus page.
- Unit I regression for demand forecasting (page 30) → MS-105 Managerial Economics.
- Unit I cost line Total Cost = a + bQ (page 30) → marginal costing and CVP in MS-107 Accounting for Management.
- CO3 spreadsheet skills → Excel data analysis in MS-109 Information Technology Management and the MS-117 lab.
- Unit I statistics and regression → hypothesis testing and multiple regression in MS-106 Business Research (Semester 2).
The other Semester 1 papers are MS-101 Management Process and Organisational Behaviour, MS-111 Marketing Management, MS-113 Communication in Organizations and MS-115 Legal Aspects of Business.
MS-103 Quant notes FAQs
How do I get the full MS-103 notes?
Message us on WhatsApp at 9899436384 with your course, semester and paper code MS-103. The full MS-103 notes on Quantitative Techniques come only as a printed book of 83 pages; they are not available as a PDF. The team shares the price, payment and delivery details in the same chat. Before ordering, open "View Sample PDF" at the top.
Is MS-103 a core paper, and how many credits does it carry?
Yes. The GGSIPU MBA scheme lists MS-103 Quantitative Techniques as a Core paper in Semester 1, with 3 lecture hours a week, no tutorial or practical hours, and 3 credits. The syllabus spreads 42 teaching hours over four units: 12 for descriptive statistics, correlation and regression, and 10 each for probability, linear programming, and decision and game theory.
What is the passing mark for MS-103?
MS-103 is marked out of 100: 25 internal marks (a 15-mark class test plus 10 for assignments, presentation, viva or class participation) and 75 end-term marks. The MBA scheme of examination does not state a pass percentage. For end-term minimums, pass rules and re-appear, follow GGSIPU Ordinance 11 on ipu.ac.in. The degree needs at least 102 of the programme's 108 credits.
Which MS-103 unit has the most solved examples?
Unit I, on descriptive statistics, correlation and regression. It runs to 30 pages with 26 solved examples, built around one sales data set of 100 shops that is reused from the mean right through to skewness. Unit II has 17 solved examples, Unit III has 12 and Unit IV has 9, which makes 64 in all across the MS-103 notes.
Do the MS-103 notes teach Excel for CO3?
No. The official course outcomes for MS-103 mention spreadsheet skills (CO3), but the four syllabus units list no software of any kind, and the notes solve every problem by hand with full working tables. Spreadsheet work is taught in MS-109 Information Technology Management and practised in the MS-117 lab, both in the same first semester.
Do the MS-103 notes match my MBA batch?
The notes follow the GGSIPU MBA Scheme and Syllabus that applies from the 2021-22 academic session, which is the scheme we checked on 13 October 2026. If your institute has issued a different MS-103 syllabus, compare its four units with the syllabus map on this page before ordering, or send your syllabus on WhatsApp and we will check it for you.
Get the full MS-103 Quantitative Techniques Notes on WhatsApp
The complete MS-103 notes are Quantitative Techniques notes in English, printed as a book; they are not sold as a PDF. Share your course, semester and delivery address on WhatsApp to order.
What is inside the MS-103 notes book
- Part
- Cover and index
- Pages
- 1-3
- Solved examples
- -
- Theory questions
- -
- Part
- Unit I
- Pages
- 4-33 (30)
- Solved examples
- 26
- Theory questions
- 9
- Part
- Unit II
- Pages
- 34-49 (16)
- Solved examples
- 17
- Theory questions
- 8
- Part
- Unit III
- Pages
- 50-68 (19)
- Solved examples
- 12
- Theory questions
- 9
- Part
- Unit IV
- Pages
- 69-82 (14)
- Solved examples
- 9
- Theory questions
- 9
- Part
- Total
- Pages
- 83
- Solved examples
- 64
- Theory questions
- 35
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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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