IT JAM 2026 Mathematical Statistics exam held today, February 11, 2026 at more than 100 exam centres across the nation. JAM 2026 Mathematical Statistics subject exam aws scheduled for the first shift i.e., from 9:30 to 12:30 PM. Test takers can read out this article to get update on post-IIT JAM 2026 Mathematical Statistics subjects exam analysis, student reactions, and based on the exam analysis, check for the IIT JAM expected cut-off for Mathematical Statistics subject. IIT JAM 2026 exam is scheduled to be held on February 11, 2026 in around 100 Cities in India. IIT Madras will conduct the JAM 2026 for 8 courses in predetermined IIT JAM 2026 shift timings. The IIT JAM morning shift will start at 9:30 am and conclude at 12:30 pm for Chemistry, Geology and Mathematical Statistics subjects at the IIT JAM 2026 exam centres. There will be around 70 to 80 thousand students will appear in the exam this year as per previous year trends.
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Sequences and Series of Real Numbers: convergence of
sequences, bounded and monotone sequences, Cauchy
sequences, Bolzano–Weierstrass theorem, absolute
convergence; tests of convergence for series
(comparison, ratio, root tests). Power series of one
real variable, radius and interval of convergence,
term-wise differentiation and integration.
Functions of One Real Variable: limits, continuity,
intermediate value property, differentiation, Rolle’s
theorem, mean value theorem, L’Hospital’s rule,
Taylor’s theorem and Taylor series, maxima and minima,
Riemann integration, fundamental theorem of calculus.
Functions of Two or Three Real Variables: limits,
continuity, partial derivatives, total derivative,
maxima and minima.
Integral Calculus: double and triple integrals, change
of order of integration, applications to surface areas
and volumes.
Differential Equations: Bernoulli’s equation, exact
differential equations, integrating factors,
orthogonal trajectories, homogeneous differential
equations, method of separation of variables, linear
differential equations of second order with constant
coefficients, Cauchy–Euler equations, method of
variation of parameters.
Linear Algebra: systems of linear equations, rank,
nullity, rank–nullity theorem, inverse of matrices,
determinants, eigenvalues and eigenvectors.
Probability Theory: random experiments, sample space,
events, axioms of probability, conditional probability,
Bayes’ theorem, independence.
Random Variables and Distributions: discrete and
continuous random variables, probability mass
function, probability density function, cumulative
distribution function, expectation, variance,
covariance and correlation.
Below is the official exam pattern for IIT-JAM Mathematical Statistics (MS)
| Particulars | Details |
|---|---|
| Exam mode | Online Computer-Based Test (CBT) |
| Exam duration | 3 Hours |
| Total number of questions | 60 |
| Total marks | 100 |
| Section A | 30 MCQs (10 × 1 mark, 20 × 2 marks) |
| Section B | 10 MSQs (2 marks each) |
| Section C | 20 NAT (10 × 1 mark, 10 × 2 marks) |
| Negative marking | Only for MCQs |
IIT-JAM Mathematical Statistics (MS) Recommended Books
| Subject Area | Book Name | Author |
|---|---|---|
| Mathematics | Calculus & Real Analysis (limits, series, multivariable calculus) | Standard Real Analysis & Calculus Texts |
| Linear Algebra (matrices, eigenvalues, vector spaces) | Standard Linear Algebra Texts | |
| Statistics & Probability | Fundamentals of Mathematical Statistics | S. C. Gupta & V. K. Kapoor |
| An Introduction to Probability and Statistics | V. K. Rohatgi & A. K. Md. Ehsanes Saleh | |
| Probability and Statistical Inference | Additional reference for deeper concepts |
Tip: Choose one good book per topic and focus on problem-solving. Excessive book-hopping should be avoided.
Important areas for scoring well in IIT-JAM Mathematical Statistics (MS)
| Topic Area | What to Focus On |
|---|---|
| Probability Theory | Probability distributions and their properties, conditional probability, independence |
| Limit Theorems | Convergence concepts, Law of Large Numbers, Central Limit Theorem |
| Sampling Distributions | Sampling theory and distributions of estimators |
| Estimation | Maximum Likelihood Estimation (MLE), unbiased estimators, consistency |
| Hypothesis Testing | Type I & Type II errors, Neyman–Pearson framework, likelihood ratio tests |
| Multivariate Statistics | Joint distributions, covariance, correlation, multivariate random variables |
| Linear Algebra | Matrix algebra required for statistical computations |
Note: IIT-JAM does not specify topic-wise weightage. Candidates should prepare all core topics thoroughly.