The GATE 2020 conducting body, IIT Delhi has released GATE 2020 Exam Dates, Eligibility Criteria, Registration Details, Subject wise Syllabus, Exam Pattern among other information. Statistics Subject (ST) is added as a new subject from GATE 2019 exam. Thus, you can view the GATE 2020 Statistics Syllabus properly with the exam pattern explained below and prepare accordingly.
Statistics Subject (ST) is one of the various branches for which the Graduate Aptitude Test in Engineering examination is conducted. Check out the AfterGraduation articles to know more about the Exam Pattern, Preparation Tips, Preparation Timetable, Scope, Study Materials etc for better GATE Score.
GATE 2020 Statistics Exam Pattern
|Section||Marks||Marks in Total||Question type|
|General Aptitude||5 questions for 1 mark each
5 questions for 2 marks each
|Core Section||25 questions for 1 mark each
30 questions for 2 marks each
|85 marks||MCQs and NATs|
Note: There is no negative marking for Numerical Type. However for MCQ, each correct answer 1 mark will be added and for each incorrect answer 0.33 mark will be deducted and for each 2 marker question correct answer gives 2 marks and incorrect answer deducts 0.66 marks.
GATE 2020 Statistics Syllabus
The syllabus for GATE 2020 Statistics subject core part is :
|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, 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.|
|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 behaviour 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, 2 power 2 and 2 power 3 Factorial experiments|
The syllabus for General Aptitude is for GATE 2020 Statistics Subject:
Verbal Ability: Critical Reasoning and Verbal Deduction, English grammar, Sentence Completion, Verbal Analogies, Word Groups, instructions.
Numerical Ability: Numerical computation, Numerical Estimation, Numerical Reasoning and Data Interpretation.
GATE (Graduate Aptitude Test in Engineering) is a national level examination held all over India for the aspirants seeking admissions in various Post Graduate Engineering/Science Courses. The GATE score is also used for the various PSU’s recruitment.
All the Best, Aspirants!!!
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