# Previous Year Solved Question Papers of GATE ST- Statistics

### GATE Statistics (ST) - Previous year Question papers with Solutions Graduate Aptitude Test in Engineering (GATE) is a national level entrance examination for admissions to Post Graduation programs like M. Tech, M. Arch, M.E., etc.

GATE Qualification is mandatory for seeking admission to:
(i) Master’s programs in Engineering/ Architecture/ Technology
(ii) Doctoral programs in relevant branches of Science.

The scores are accepted by more than 900+ institutes across India and is valid for three years post announcement of results.

The GATE 2020 ST Exam will be  a 3-hour duration examination which is conducted online. The question paper will have 65 questions of 100 marks.

## Pattern of Question Paper – GATE 2020 ST

The exam contains two different types of questions:

### (i)             Multiple Choice Questions (MCQ)

Each question would carry 1 or 2 marks each and These questions are objective in nature i.e. every question will have a choice of four answers, out of which the candidate must select the correct answer. There will be negative marking for a wrongly selected answer. E.g. For 1-mark question, 1/3 mark will be deducted for a wrong answer. Similarly, for 2-mark question, 2/3 mark will be deducted for a wrong answer.

### (ii)Numerical Answer Type (NAT) Questions

Each question carries 1 or 2 marks each and for these questions, the answer is a signed real number, that needs to be entered by the candidate using the virtual numeric keypad on the monitor (keyboard of the computer will be disabled). No choices will be given for these questions and the highlight is that there is no negative marking for a wrong answer in NAT questions.

There are some new changes introduced in GATE 2019. Statistics is a new addition to GATE examination from the year 2019.

## GATE 2020 Statistics Syllabus:

The syllabus of the GATE ST examination 2020 is provided below:

• Calculus: Finite, countable and uncountable sets, Real number system as a complete ordered field, Archimedean property; Riemann integration, Improper integrals; 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; Cayley-Hamilton Theorem, Jordan canonical form, symmetric, skew-symmetric, Hermitian; Finite dimensional inner product spaces, Gram- Schmidt orthonormalization process.
• Probability: Classical, relative frequency and axiomatic definitions of probability, Bayes’ theorem; Random, Probability inequalities, Function of a random variable; Transformations of random variables, sampling distributions, distribution of order statistics and range; Characteristic functions; Modes of convergence;
• Stochastic Processes: Markov chains with finite and countable state space, Poisson and birth-and-death processes.
• Inference: Confidence intervals; Tests of hypotheses, Wilcoxon signed rank test, Mann- Whitney U test, test for independence and Chi-square test for goodness of fit.
• Regression Analysis: Polynomial regression, estimation, confidence intervals and testing for regression coefficients; Partial and multiple correlation coefficients.
• Multivariate Analysis: Basic properties of multivariate normal distribution; Multinomial distribution; Wishart distribution; Discriminant analysis; Clustering.
• Design of Experiments: One and two-way ANOVA, CRD, RBD, LSD, Factorial experiments.