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GATE 2026 Statistics Syllabus: IIT Guwahati has released the GATE Statistics syllabus 2026 on the official website, gate2026.iitg.ac.in. Candidates can access the GATE Statistics 2026 syllabus pdf on this page. The authorities has released the GATE syllabus to provide candidates with the complete list of topics that must be prepared for the Graduate Aptitude Test in Engineering. The GATE statistics syllabus comprises ten chapters. Candidates must refer to the GATE 2026 ST syllabus and paper pattern to plan their preparation strategy. The GATE 2026 exam will be conducted on February 7, 8, 14 and 15, 2026 online.
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Moreover, the GATE Statistics question paper is based on topics mentioned in the syllabus. Read the complete article to know more about the GATE 2026 syllabus for statistics
GATE 2026 Statistics Syllabus
The Indian Institute of Technology Guwahati has released the syllabus for GATE ST 2026 online. The GATE 2026 syllabus includes topics such as Calculus, Matrix Theory, Probability, Standard discrete and continuous univariate distributions, Stochastic Processes, Estimation, Testing of Hypotheses, Non-parametric Statistics, Multivariate Analysis, Regression Analysis and more. Candidates can follow the syllabus to prepare for the entrance test.
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Topics | Sub Topics |
Calculus | Finite, countable and uncountable sets, Real number system as a complete ordered field, Archimedean property, Sequences of real numbers, convergence of sequences, bounded sequences, monotonic sequences, Cauchy criterion for convergence, Series of real numbers, convergence, tests of convergence, alternating series, absolute and conditional convergence, Power series and radius of convergence, Functions of a real variable: Limit, continuity, monotone functions, uniform continuity, differentiability, Rolle’s theorem, mean value theorems, Taylor’s theorem, L’ Hospital rules, maxima and minima, Riemann integration and its properties, improper integrals, Functions of several real variables: Limit, continuity, partial derivatives, directional derivatives, gradient, Taylor’s theorem, total derivative, maxima and minima, saddle point, method of Lagrange multipliers, double and triple integrals and their applications. |
Matrix Theory | Subspaces of span, linear independence, basis and dimension, row space and column space of a matrix, rank and nullity, row reduced echelon form, trace and determinant, inverse of a matrix, systems of linear equations; Inner products in and, Gram-Schmidt orthonormalization, Eigenvalues and eigenvectors, characteristic polynomial, Cayley-Hamilton theorem, symmetric, skew-symmetric, Hermitian, skew-Hermitian, orthogonal, unitary matrices and their eigenvalues, change of basis matrix, equivalence and similarity, diagonalizability, positive definite and positive semi-definite matrices and their properties, quadratic forms, singular value decomposition. |
Probability | Axiomatic definition of probability, properties of probability function, conditional probability, Bayes’ theorem, independence of events, Random variables and their distributions, distribution function, probability mass function, probability density function and their properties, expectation, moments and moment generating function, quantiles, distribution of functions of a random variable, Chebyshev, Markov and Jensen inequalities. |
Standard discrete and continuous univariate distributions | Bernoulli, binomial, geometric, negative binomial, hypergeometric, discrete uniform, Poisson, continuous uniform, exponential, gamma, beta, Weibull, normal. Jointly distributed random variables and their distribution functions, probability mass function, probability density function and their properties, marginal and conditional distributions, conditional expectation and moments, product moments, simple correlation coefficient, joint moment generating function, independence of random variables, functions of random vector and their distributions, distributions of order statistics, joint and marginal distributions of order statistics, Multinomial distribution, bivariate normal distribution, sampling distributions: central, chi-square, central t, and central F distributions. Convergence in distribution, convergence in probability, convergence almost surely, convergence in r-th mean and their inter-relations, Slutsky’s lemma, Borel-Cantelli lemma, Weak and strong laws of large numbers; central limit theorem for i.i.d. random variables, delta method. |
Stochastic Processes | Markov chains with finite and countable state space, classification of states, limiting behaviour of n-step transition probabilities, stationary distribution, Poisson process, birth and death process, pure-birth process, pure-death process, Brownian motion and its basic properties. |
Estimation | Sufficiency, minimal sufficiency, factorization theorem, completeness, completeness of exponential families, ancillary statistic, Basu’s theorem and its applications, unbiased estimation, uniformly minimum variance unbiased estimation, Rao-Blackwell theorem, Lehmann-Scheffe theorem, Cramer-Rao inequality, consistent estimators, method of moments estimators, method of maximum likelihood estimators and their properties Interval estimation: pivotal quantities and confidence intervals based on them, coverage probability. |
Testing of Hypotheses | Neyman-Pearson lemma, most powerful tests, monotone likelihood ratio (MLR) property, uniformly most powerful tests, uniformly most powerful tests for families having MLR property, uniformly most powerful unbiased tests, uniformly most powerful unbiased tests for exponential families, likelihood ratio tests, large sample tests. |
Non-parametric Statistics | Empirical distribution function and its properties, goodness of fit tests, chisquare test, Kolmogorov-Smirnov test, sign test, Wilcoxon signed rank test, Mann-Whitney U-test, rank correlation coefficients of Spearman and Kendall. |
Multivariate Analysis | Multivariate normal distribution: properties, conditional and marginal distributions, maximum likelihood estimation of mean vector and dispersion matrix, Hotelling’s T2 test, Wishart distribution and its basic properties, multiple and partial correlation coefficients and their basic properties. |
Regression Analysis | Simple and multiple linear regression, R2 and adjusted R2 and their applications, distributions of quadratic forms of random vectors: Fisher-Cochran theorem, GaussMarkov theorem, tests for regression coefficients, confidence intervals. |
GATE books are the best resources for the preparation of entrance tests. Candidates must be aware of the GATE Statistics syllabus to select the best book for GATE 2026 ST. Below is the list of the best books for GATE 2026 Statistics.
Book Name | Author Name |
The Foundations of Statistics | Leonard J. Savage |
Probability and Statistics | Murray R Spiegel, John J Schiller, and R Alu Srinivasan |
Miller and Freund’s Probability and Statistics For Engineers | Pearson |
GATE Statistics Practice Question Bank with Topic wise | Rajendra Dubey and Dr. Puneet Pasricha |
Matrix Theory | Joel Nick Franklin |
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Being aware of the GATE statistics syllabus with topic-wise weightage helps to secure a good rank in the exam. Knowing the syllabus weightage, candidates can prioritize the topics and manage their preparation strategy. While the topics with high weightage should be given priority, candidates must not neglect the remaining topics. GATE Statistics syllabus with topic weightage GATE exam analysis. Check the table for the GATE ST syllabus with topics-wise weightage.
Topics | Weightage |
Probability | 20% |
Calculus | 15% |
Multivariate Analysis | 10% |
Matrix Theory | 8% |
Stochastic Processes | 8% |
Estimation | 8% |
Testing of Hypotheses | 8% |
Regression Analysis | 8% |
There are a few topics that hold more weight as compared to other topics. So, here we have given the list of important topics of the GATE Statistics 2026 syllabus. The GATE Statistics syllabus important topics 2026 has more questions in the exam. Check the list of GATE Statistics syllabus 2026 important topics here.
S.No. | Calculus |
1. | Probability |
2. | Regression Analysis |
3. | Linear Equations |
4. | Estimation |
Aspirants can practice the GATE Statistics sample paper 2026 for quick revision and self-assessment. The questions in the sample paper of GATE ST are based on the exam syllabus. Candidates get familiar with the exam difficulty level by solving the GATE 2026 statistics sample paper. Here we have provided the previous year GATE Statistics sample paper.
GATE ST year-wise Sample Paper | GATE ST sample paper link |
IIT Guwahati will activate the GATE Statistics mock test link on the official website. No login credentials are required to attempt the GATE mock test for Statistics. The mock test is a replica of the actual exam. The questions in the GATE Statistics mock test 2026 are based on the syllabus of GATE 2026. Hence, candidates should attempt this without fail.
Candidates appearing for the exam can check the GATE previous year's cutoff on this page. Knowing the cutoff of GATE Statistics helps students have a rough idea of what marks are required to clear the exam.
GATE Subject | Qualifying Marks | Qualifying Score | ||||
Statistics (ST) | GEN | EWS/OBC | SC/ST/PwD | GEN | EWS/OBC | SC/ST/PwD |
25 | 22.5 | 16.6 | 350 | 268 | 57 |
Frequently Asked Questions (FAQs)
Yes, the GATE 2026 Syllabus has been released on the official website, gate2026.iitg.ac.in.
IIT Guwahati will release the mock test of GATE 2026 for Statistics on the official website, gate2026.iitg.ac.in.
The GATE 2026 syllabus includes topics such as Calculus, Matrix Theory, Probability, Standard discrete and continuous univariate distributions, Stochastic Processes, Estimation, Testing of Hypotheses, Non-parametric Statistics, Multivariate Analysis, Regression Analysis and more.
Start your preparation the moment you decide to get admission in an IIT or get a job at any PSU. As this exam is a gateway for your choices. So, start your preparation as early as possible.
On Question asked by student community
To take GATE mock tests, first choose a reliable source like GATE official portal, NPTEL, Made Easy, or ACE Engineering. Sign up or register online to access the full-length tests. Simulate the actual 3-hour exam with proper timing and no distractions. Attempt complete tests to practice time management and exam strategy. After each test, analyze your performance to identify weak areas and revise. Regularly taking 1–2 mocks per week helps improve speed, accuracy, and confidence.
For GATE Life Sciences question banks, you can go for GATE past year's papers to understand the exam style, and you can buy online test series from a coaching site for lots of new practice questions.
you can get question book one from publishers like IFAS or GKP. Also use free online resources like NPTEL and EasyBiologyClass for extra practice and study materials. Good luck!
Here are GATE exam aptitude topics from high to low weightage:
1. English Comprehension & Vocabulary
2.Numerical Ability and Arithmetic
3.Logical Reasoning & Analytical Ability
4.Data Interpretation
5.Geometry & Mensuration basic
6.Algebra basic
7.Probability and Statistics
8.General Knowledge and Miscellaneous Verbal like odd one out, analogy, word usage.
Hello,
Here is the link where you can get required question paper : GATE 2024 EE Question Paper
Hope it helps !
If you are needed to prepare for gate exam from ece batch there 65 questions in exam pattern total marks is 100 here 15% is aptitude general questions then 15% mathematics engineering questions , 70% core subjects you need to concentrate on more core related subject like controls system , signals and system , digital circut these topics covers most of the basics but you need to prepare more topics in core subjects and create a structure plan for yourself dialy attend mock test with time duration these steps will help to prepare very easily.
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