# ISS Syllabus Statistics Paper I (Objective Type) Revised 2016

## (i) Probability:

• Classical and axiomatic definitions of Probability and consequences. Law of total probability. Conditional probability. Bayes’ theorem and applications. Discrete and continuous random variables. Distribution functions and their properties.

• Standard discrete and continuous probability distributions — Bernoulli, Uniform, Binomial, Poisson, Geometric, Rectangular. Exponential, Normal, Cauchy, Hyper, geometric, Multinomial, Laplace, Negative binomial, Beta, Gamma, Lognormal.

• Random vectors, Joint and marginal distributions, conditional distributions, Distributions of functions of random variables. Modes of convergences of sequences of random variables — in distribution, in probability, with probability one and in mean square. Mathematical expectation and conditional expectation. Characteristic function, moment and probability generating functions. Inversion, uniqueness and continuity theorems. Borel O—1 law, Kolmogorov’s O—1 law. Tchebycheff’s and

• Kolmogorov’s inequalities. Laws of large numbers and central limit theorems for independent variables.

## (ii) Statistical Methods:

• Collection, compilation and presentation of data. Charts, diagrams and histogram. Frequency distribution. Measures of location, dispersion. Skewness and kurtosis, Bivariate and multivariate data, Association and contingency, Curve fitting and orthogonal polynomials. Bivariate normal distribution. Regression—linear, polynomial, Distribution of the correlation coefficient, Partial and multiple correlations. Infraclass correlation, Correlation ratio.

• Standard errors and large sample test. Sampling distributions of sample mean. Sample variance, t. chi—square and F tests of significance based on them, small sample tests.

• None—parametric tests—Goodness of fit, sign, median, run. Wilcoxon. Mann—Whitney, Wald—Wolfowitz and Kolmogorov—Smirnov. Order statistic s—minimum, maximum, range and median. Concept of Asymptotic relative efficiency.

## (iii) Numerical Analysis

• Finite differences of different orders: I E and D operators, factorial representation a polynomial, separation of symbols, sub—division of intervals, differences of zero.

• Concept of interpolation and extrapolation: Newton Gregory’s forward and backward interpolation formulae for equal intervals, divided differences and their properties. Newton’s formula for divided difference, Lagrange? Formula for unequal intervals. Central difference formula due to Gauss, Sterling and Bessel, concept of error terms in interpolation formula.

• Inverse interpolation: Different methods of inverse interpolation.

• Numerical differentiation: Trapezoidal. Simpson one—third and three—eight rule and Waddles rule.

Summation of Series: Whose general term

(i) Is the first difference of a function.

(ii) Is in geometric progression.

• Numerical solutions of differential equations: Euler’s Method. Milne’ Method. Picard’ Method and Runge—Kutta Method.

## (iv) Computer application and Data Processing:

• Basics of Computer: Operations of a computer. Different units of a computer system like central processing unit, memory unit, arithmetic and logical unit, input unit, output unit etc., Hardware including different types of input, output and peripheral devices. Software, system and application software, number systems. Operating systems, packages and utilities, Low and High level languages. Compiler, Assembler, Memory: RAM, ROM, unit of computer memory (bits, bytes etc.), Network: LAN, WAN, internet, intranet, basics of computer security, virus, antivirus, firewall, spyware, malware etc.

• Basics of Programming: Algorithm, Flowchart, Data. Information. Database, overview of different programming languages, frontend and backend of a project, variables, control structures, arrays and their usages, functions, modules, loops, conditional statements, exceptions, debugging and related concepts.