What is GATE Statistics (ST)?
GATE Statistics (Paper Code: ST) is conducted jointly by IISc and IITs. It opens immense career avenues for Master's degree holders in Statistics, Mathematics, and Data Science.
Qualifying GATE ST unlocks lucrative scientific and executive positions in leading PSUs (ONGC, IOCL, NPCIL, DRDO, RBI DSIM) as well as direct admissions with attractive MHRD fellowships into M.Tech & Ph.D. programs in Data Science, AI, and Quantitative Analytics across IITs.
Alpha Plus provides conceptual rigor, advanced problem-solving modules, past years' paper walkthroughs, and full-length online CBT mock tests to help students secure top percentile scores.
GATE Statistics Exam Pattern
| Section | Topic Scope | Questions | Section Marks |
|---|---|---|---|
| General Aptitude (GA) | Verbal Ability & Numerical Ability | 10 Questions (5 × 1M + 5 × 2M) | 15 Marks |
| Subject Statistics (ST) | Calculus, Matrix Theory, Probability, Stochastic Processes, Inference, Regression, Multivariate | 55 Questions (25 × 1M + 30 × 2M) | 85 Marks |
| Grand Total | 65 Questions | 100 Marks | |
GATE Statistics Syllabus Breakdown
Probability & Distributions
Axiomatic probability, independence, random variables, distribution functions, standard univariate & multivariate distributions, moments, characteristic functions, and probability inequalities.
Stochastic Processes
Markov chains with finite and countable state space, transition probability matrix, classification of states, stationary distributions, Poisson processes, and birth-death processes.
Statistical Inference
Sufficient statistics, complete statistics, Lehmann-Scheffe theorem, UMVUE, Cramer-Rao bound, MLE and its asymptotic properties, uniformly most powerful (UMP) tests, likelihood ratio tests, Bayesian inference.
Regression & Linear Models
Gauss-Markov theorem, multiple linear regression, residual analysis, multicollinearity, model selection, hypothesis testing in linear regression, ANOVA and ANCOVA.
Multivariate Analysis
Multivariate normal distribution, Wishart distribution, Hotelling's T^2, Mahalanobis D^2, principal component analysis (PCA), canonical correlation, discriminant analysis, and factor analysis.
Calculus & Matrix Theory
Functions of several variables, Jacobians, multiple integrals, vector spaces, subspaces, linear independence, eigenvalues, quadratic forms, positive definiteness, and matrix decompositions.
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