GATE 2024 Statistics Syllabus (Available) – Get (ST) Syllabus PDF Here

GATE 2024 Syllabus of Statistics has been Released. GATE 2024 is a national-level exam organized by IISc Banglore.

The Engineering Graduation Skill Test (GATE 2024) is organized for admission to PG courses in engineering and technology, specifically ME / M.Tech.

The Exam will be held on 3rd, 4th, 10th & 11th February 2024. See here for complete information about the GATE 2024 Syllabus.

GATE 2024 Statistics Syllabus – PDF Released

GATE 2024 Statistics Syllabus

Calculus: Finite, countable and uncountable sets, Real number system as a complete ordered field, Archimedean property; Sequences and series, convergence; Limits, continuity, uniform continuity, differentiability, mean value theorems; Riemann integration, Improper integrals; Functions of two or three variables, continuity, directional derivatives, partial derivatives, total derivative,

maxima and minima, saddle point, a method of Lagrange’s multipliers; Double and Triple integrals and their applications; Line integrals and Surface
integrals, Green’s theorem, Stokes’, and Gauss divergence theorem.

Linear Algebra: Finite dimensional vector spaces over real or complex fields; Linear transformations and their matrix representations, rank; systems of linear equations, eigenvalues and eigenvectors, minimal polynomial, Cayley-Hamilton Theorem, diagonalization, Jordan canonical form,

symmetric, skew-symmetric, Hermitian, skew-Hermitian, orthogonal and unitary matrices; Finite dimensional inner product spaces, Gram- Schmidt orthonormalization process, definite forms.

Probability: Classical, relative frequency and axiomatic definitions of probability, conditional probability, Bayes’ theorem, independent events; Random variables and probability distributions, moments and moment generating functions, quantiles; Standard discrete and continuous univariate distributions;

Probability inequalities (Chebyshev, Markov, Jensen); Function of a random variable; Jointly distributed random variables, marginal and conditional distributions, product moments, joint moment generating
functions, independence of random variables; Transformations of random variables, sampling distributions, distribution of order statistics and range; Characteristic functions; Modes of convergence; Weak and strong laws of large numbers; Central limit theorem for I .i.d. random variables with the existence of higher-order moments.

Stochastic Processes: Markov chains with finite and countable state space, classification of states, limiting behavior of n-step transition probabilities, stationary distribution, Poisson, and birth-and-death processes.

Inference: Unbiasedness, consistency, sufficiency, completeness, uniformly minimum variance unbiased estimation, method of moments and maximum likelihood estimations; Confidence intervals; Tests of hypotheses, most powerful and uniformly most powerful tests, likelihood ratio tests, large sample test, Sign test, Wilcoxon signed-rank test, Mann- Whitney U test, test for independence and Chi-square test for goodness of fit.

Regression Analysis: Simple and multiple linear regression, polynomial regression, estimation, confidence intervals, and testing for regression coefficients; Partial and multiple correlation coefficients.

Multivariate Analysis: Basic properties of the multivariate normal distribution; Multinomial distribution; Wishart distribution; Hotelling’s T2 and related tests; Principal component analysis; Discriminant analysis; Clustering.

Design of Experiments: One and two-way ANOVA, CRD, RBD, LSD, 22, and 23 Factorial Experiments.

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Linear Algebra: Matrices and Determinants, Systems of linear equations, Eigenvalues and eigenvectors.

Calculus: Limit, continuity, and differentiability; Partial Derivatives; Maxima and minima; Sequences and series; Test for convergence; Fourier series.

Vector Calculus: Gradient; Divergence and Curl; Line; surface and volume integrals; Stokes, Gauss and Green’s theorems.

Differential Equations: Linear and non-linear first order ODEs; Higher order linear ODEs with constant coefficients; Cauchy’s and Euler’s equations; Laplace transforms; PDEs -Laplace, heat and wave equations.

Probability and Statistics: Mean, median, mode and standard deviation; Random variables; Poisson, normal and binomial distributions; Correlation and regression analysis.

Numerical Methods: Solutions of linear and non-linear algebraic equations; integration of trapezoidal and Simpson’s rule; single and multi-step methods for differential equations.

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Sunil Kushwaha

As a dedicated Biology Science graduate, I’m passionate about sharing the latest updates in national and state entrance exams through my blog. I aim to keep aspiring students informed about exam trends, important dates, and changes in syllabi. With a keen interest in education, I strive to offer valuable insights for students navigating the competitive landscape of entrance examinations and admission tests. Stay updated with me.