The GATE statistics syllabus 2026 will be released on the official website of IIT Guwahati. The GATE Statistics syllabus contains topics like Calculus, Matrix theory, Probability, Standard Discrete and Continuous univariate distributions, Stochastic processes, Estimation, Testing of hypotheses, Non-parametric statistics, Multivariate analysis, and Regression analysis.
The GATE syllabus 2026 is crucial since it gives students an understanding of the topics and subjects covered in the GATE exams. Knowing the GATE exam syllabus PDF will enable students to organize their studies effectively to ace the tests.
The GATE statistics syllabus 2026 will be released on the official website of IIT Guwahati. The GATE Statistics syllabus contains topics like Calculus, Matrix theory, Probability, Standard Discrete and Continuous univariate distributions, Stochastic processes, Estimation, Testing of hypotheses, Non-parametric statistics, Multivariate analysis, and Regression analysis.
Overview of GATE Statistics Paper
The GATE Statistics syllabus will be released by the authorities soon. The overview of the GATE statistics paper has been added below for your reference.
|
Detail |
Information |
|
Paper Code |
ST |
|
Total Marks |
100 |
|
Exam Duration |
3 hours |
|
Number of Questions |
65 |
|
Question Types |
Multiple Choice Questions (MCQs), Multiple Select Questions (MSQs), Numerical Answer Type (NAT) |
|
Sections Covered |
General Aptitude (15%), Statistics Core Subjects (85%) |
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GATE 2026 Statistics Syllabus – Detailed Breakdown
The GATE 2026 statistics syllabus has been divided into different sections. Some important topics are covered in these sections. The GATE statistics sections and their important topics are as follows:
|
Section |
Topics Covered |
|
1. Calculus |
Limits, continuity, and differentiability of functions; Mean Value Theorems (Rolle’s and Lagrange’s); Taylor and Maclaurin expansions; Maxima, minima, and optimization problems; Indeterminate forms and L’Hospital’s rule; Improper integrals and their convergence; Double and triple integrals; Line and surface integrals; Gradient, divergence, curl, and vector calculus |
|
2. Linear Algebra |
Vector spaces, subspaces, linear independence; Basis and dimension of vector spaces; Matrix algebra: addition, multiplication, rank, and determinant; Systems of linear equations and their solutions; Eigenvalues and eigenvectors; Cayley–Hamilton theorem; Inner product spaces, orthogonality, Gram–Schmidt process; Quadratic forms and canonical forms |
|
3. Probability and Stochastic Processes |
Sample space, events, and probability axioms; Conditional probability and independence; Bayes’ theorem; Random variables (discrete and continuous); Probability distributions: Binomial, Poisson, Normal, Exponential, Gamma, Beta; Moment generating functions and characteristic functions; Law of Large Numbers and Central Limit Theorem; Markov chains, stationary distributions; Poisson and birth–death processes; Brownian motion and simple stochastic processes |
|
4. Statistical Inference |
Point estimation: methods of moments, maximum likelihood estimation (MLE); Properties of estimators: unbiasedness, consistency, efficiency, sufficiency; Cramér–Rao inequality and UMVUE; Interval estimation: confidence intervals for means, proportions, and variances; Hypothesis testing: Neyman–Pearson lemma, likelihood ratio tests; Large sample tests (Z, t, Chi-square, F-tests); Non-parametric tests (sign test, Wilcoxon test, Kolmogorov–Smirnov test); Sequential testing and decision theory basics |
|
5. Multivariate Analysis |
Multivariate normal distribution; Estimation of mean vector and covariance matrix; Principal component analysis (PCA); Canonical correlations; Discriminant analysis and classification methods; Factor analysis basics |
|
6. Regression and Design of Experiments |
Simple and multiple linear regression models; Least squares estimation and properties; Residual analysis and goodness of fit; Multicollinearity and heteroscedasticity; Analysis of variance (ANOVA): one-way, two-way classifications; Randomized block design, Latin square design; Factorial designs and confounding |
|
7. General Aptitude (Common for All Papers) |
Verbal Ability: grammar, sentence completion, synonyms, antonyms, comprehension; Numerical Ability: arithmetic, percentages, ratios, averages, profit & loss, simple and compound interest; Logical Reasoning and Data Interpretation |
GATE Statistics Exam Pattern 2026
The GATE statistics syllabus is a total of 100 marks. The general aptitude part is of 15 marks with 15 % weightage, whereas the core topics are of 85 marks, with 85 % weightage. The Statistics GATE exam pattern.
|
Section |
Marks Distribution |
Weightage |
|
General Aptitude |
15 marks |
15% |
|
Core Statistics Topics |
85 marks |
85% |
|
Total |
100 marks |
100% |
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GATE Statistics Topic-Wise Weightage (Based on Previous Trends)
The GATE statistics topic-wise weightage will help you rule out the important and high-weightage topics. This high-weightage topics list will help you cover the major part of the syllabus and help you score well. The List of important topics and their weightage as per the previous year trends is as follows:
|
Topic |
Approximate Weightage (%) |
|
Probability & Stochastic Processes |
20–25% |
|
Statistical Inference |
20–25% |
|
Regression & ANOVA |
15% |
|
Multivariate Analysis |
10–12% |
|
Linear Algebra |
8–10% |
|
Calculus & Real Analysis |
8–10% |
|
General Aptitude |
15% |
How to Prepare for GATE Statistics 2026
The GATE statistics syllabus requires optimum focus and dedication. Since GATE is comparatively tougher than any other exam, the difficulty level is high. Follow these tips to crack the GATE exam.
- Clear your basics: Clearing your basics first will help you know the concepts of the topic. After this, you can move up to complex and more detailed reference books.
- Make a schedule for the GATE Statistic syllabus specifically: The GATE syllabus specifically needs to be covered properly, as it is very vast and important. Try making a detailed schedule to cover all the important and high-weightage topics of the GATE Statistics syllabus.
- Practice mock tests: Practicing mock tests will help you get used to the exam pattern. Practicing mock tests also helps in time management.
- Revise mathematical foundations: Since linear algebra and calculus form the backbone of statistics, ensure clarity in vector spaces, eigenvalues, and multiple integrals.
Best Books for GATE Statistics Preparation
For the preparation of GATE statistics, the best books are required. The list of GATE best books for the preparation of the GATE statistics has been added here:
- A First Course in Probability – Sheldon Ross
- Statistical Inference – Casella and Berger
- Introduction to the Theory of Statistics – Mood, Graybill, and Boes
- Applied Multivariate Statistical Analysis – Johnson and Wichern
- Introduction to Linear Algebra – Gilbert Strang
The GATE Statistics paper is highly conceptual and demands both theoretical clarity and analytical skills. Focus on high-weightage areas like Probability, Inference, and Regression. Regular revision, solving mock tests, and analyzing mistakes will help maximize your score. If you aim to pursue higher studies in statistics, mathematics, or data science, or seek career opportunities in research and analytics, a strong performance in the GATE Statistics exam will open multiple doors for you.
