GATE 2020 is going to be organized very soon by IIT Delhi. Soon after the GATE notification is out, exam conducting authority will give the syllabus for all the papers. Statistics is one of the papers among the GATE 2020 exam papers. Since last year this paper has been introduced or added along with 23 other papers in the entrance exam. **GATE Syllabus for Statistics (ST)** will also be available for the applicants as this is one of the papers of this entrance exam. Candidates can check the complete details of the Statistics syllabus for the test here.

## GATE Statistics Syllabus

Statistics is one of the GATE 2020 papers in which the questions from **GATE 2020 syllabus for statistics** are asked. Candidates appearing in the test this year for the statistics paper can go through the entire syllabus and the topics that are covered before the start of the preparation. By knowing the syllabus, candidates can prepare better for the test.

Various topics that are listed under GATE statistics syllabus include, Calculus, Linear Algebra, Probability, Stochastic Processes, Inference, Regression Analysis, Multivariate Analysis, and Design of Experiments. Statistics paper will be common as other papers of the GATE. So, it does also includes the General Aptitude section from which 10 questions are asked in the ST paper too. As the GATE 2020 will be held in the February month, applicants can prepare the subject covering all the topics well and be ready for the exam. The higher the marks you get in the GATE, the better chances you will have for getting seat or dream job you are looking for. Check the detailed GATE Syllabus for Statistics below.

SI. No. |
Subject |
Topics |

1. |
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. |

2. |
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. |

3. |
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 existence of higher order moments. |

4. |
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. |

5. |
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. |

6. |
Regression Analysis | Simple and multiple linear regression, polynomial regression, estimation, confidence intervals and testing for regression coefficients; Partial and multiple correlation coefficients. |

7. |
Multivariate Analysis | Basic properties of multivariate normal distribution; Multinomial distribution; Wishart distribution; Hotellings T2 and related tests; Principal component analysis; Discriminant analysis; Clustering. |

8. |
Design of Experiments | One and two-way ANOVA, CRD, RBD, LSD, 22 and 23 Factorial experiments. |

9. |
General Aptitude | Syllabus Link |

Along with the **GATE Syllabus for Statistics**, also check the GATE General Aptitude syllabus 2020 to know the syllabus and prepare for the test. Further information GATE 2020 is available in various articles of ePostbag. Do follow us for regular updates on education.