Graduate Aptitude Test in Engineering (GATE) is a examination on the comprehensive of the candidates in various undergraduate subjects in Engineering/ Technology/ Architecture and post-graduate level subjects in Science. The GATE 2019 examination will be conducted for 24 subjects and it would be distributed over 2nd, 3rd, 9th and 10th of February 2019. The GATE examination centres are in different cities across India, as well as, in six cities outside india. The examination would be purely a Computer Based Test (CBT). This is a new paper with subject name statistics (ST) were going to introduced. GATE Online Application Processing System (GOAPS) Website Opens from 01.09.2018 onwards and the GATE 2019 Examination Results will be announce on 16.03.2019.
|GATE Online Application Processing System (GOAPS) Website Opens||Saturday||01.09.2018|
|Closing Date for Submission of (Online) Application||Friday||21.09.2018|
|Extended Closing Date for Submission of (Online) Application||Monday||01.10.2018|
|Last Date for Requesting Change of Examination City |
(an additional fee will be applicable)
|Admit Card will be available in the Online Application Portal |
|GATE 2019 Examination |
Forenoon: 9:00 AM to 12:00 Noon (Tentative)
Afternoon: 2:00 PM to 5:00 PM (Tentative)
|Announcement of the Results in the Online Application Portal||Saturday||16.03.2019|
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, method of Lagrange's multipliers; Double and Triple integrals and their applications; Line integrals and Surface integrals, Green's theorem, Stokes' theorem, and Gauss divergence theorem.
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.
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 existence of higher order moments
Markov chains with finite and countable state space, classification of states, limiting behaviour of n-step transition probabilities, stationary distribution, Poisson and birth-and-death processes.
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.
Simple and multiple linear regression, polynomial regression, estimation, confidence intervals and testing for regression coefficients; Partial and multiple correlation coefficients.
Basic properties of multivariate normal distribution; Multinomial distribution; Wishart distribution; Hotellings 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.
For more details : http://gate.iitm.ac.in/