Quantitative Techniques Notes
Subject Overview
Quantitative Techniques equips MBA students with the statistical and operations-research toolkit used throughout the program — central tendency, dispersion, correlation and regression, probability distributions, linear programming and game theory, and transportation, assignment and project scheduling (PERT/
Subject at a glance
4
Units
31
Topics
4
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Meaning
- features
- functions and limitations of statistics
- measures of central tendency (arithmetic/geometric/harmonic mean, mode, median, quartiles/deciles/percentiles)
- measures of variation (range, quartile deviation, mean deviation, standard deviation)
- moments
- skewness and kurtosis
Mean
x̄ = Σfx / Σf
Standard deviation
σ = √[Σf(x − x̄)² / Σf]
Coefficient of variation
(σ / x̄) × 100
Pearson skewness
(Mean − Mode) / σ
Kurtosis
β2 = μ4 / μ2²
3 = normal
Study guide
This unit covers descriptive statistics: averages, positional measures, variation, moments, skewness and kurtosis. Practise interpreting skewness and variation in business data.
- Simple/multiple/partial and linear/non-linear correlation
- Pearson's correlation coefficient
- rank correlation
- simple regression via least squares
- approaches to probability
- addition and multiplication laws
- conditional probability and Bayes' theorem
Pearson's r
Cov(x, y) / (σx σy)
Spearman's rank
1 − 6Σd² / [n(n² − 1)]
Regression line
y = a + bx by least squares
Bayes' theorem
P(A given B) = P(B given A) P(A) / P(B)
Study guide
You study correlation, regression, probability laws, conditional probability and Bayes' theorem. Practise Bayes' theorem with tree diagrams.
- Binomial
- Poisson and Normal distributions
- formulation of linear programming problems
- graphic and Simplex (including Big-M) solution methods
- degeneracy
- duality
- post-optimality analysis
- two-person zero-sum games
- pure and mixed strategies
- dominance rule
Binomial
Successes in n independent trials
np / npq
Poisson
Rare events in a fixed interval
λ / λ
Normal
Continuous, bell-shaped data
μ / σ²
Study guide
You study the binomial, Poisson and normal distributions, linear programming formulation and solution (graphical, simplex, Big-M), duality, post-optimality and game theory. Solve one simplex problem fully each week.
- Transportation problem — North-West Corner
- Least Cost and Vogel's Approximation methods
- optimality testing via MODI method
- assignment problem via the Hungarian method
- PERT/CPM project networks
- scheduling with known activity times
- critical path
North-West Corner
Start top-left, fill in order
Quick, often far from optimal
Least Cost
Fill the cheapest cell first
Better
Vogel's Approximation
Use penalties (difference of two lowest costs)
Usually closest to optimal
Study guide
You study transportation (NWC, Least Cost, Vogel, MODI), assignment (Hungarian) and PERT/CPM scheduling. Set problems up in tables before solving.
Why Quantitative Techniques Matters
Quantitative techniques are the toolkit for operations, finance and analytics decisions and the basis for the quantitative section of MBA entrance exams.
How to Prepare for Quantitative Techniques
- Solve problems daily and keep a formula sheet.
- Practise with Excel Solver for LP and transportation problems.
- Interpret each result in business terms.
Quantitative Techniques: Frequently Asked Questions
What is linear programming used for?
Linear programming finds the best allocation of limited resources, such as maximising profit or minimising cost, subject to constraints.