M.SC.(Statistics) (Semester (CBCS) Examination, 2017
Statistical Computing
Day Date: Thursday, 27-04-2017 Max. Marks: 70
Time: 10.30 AM to 01.00 PM
N.B. Attempt five questions
Q.No.(1) and Q.No.(2) are compulsory.
Attempt any three from Q.No.(3) to Q.No.(7)
Figures to the right indicate full mark
Q.1 Choose the correct alternatives: 05
Jack Knife estimator reduces
Variance Bias
Bias and variance None of the above
Regula-Falsi Method is used to
Find roots of the equation 0
Maximize a function
Minimize a function
Optimize a function.
The operator is used as assignment operator in R.

None of these
In is used to extract data from a specified location.

Data.extract( All of the above
In the command arranges the data in order
by default
Decreasing Increasing
Both None of these
Q.1 Fill in the blanks 05
Random numbers generated by some algorithm are called
To obtain density of at point 0.34, we use
command in R
Using CLT, the minimum number of iid uniform random
variates required to obtain a single normal variate is
Binomial variate can be generated by adding variates.
To obtain the largest integer not greater than the argument,
the command is used in MS Excel.
Page 2 of 2
Q.1. State whether following statements are true or false 04
R-Software uses only Excel workbook as an external file to
import.
To assign many values to a single variable, combine function
is used in R-software
Jack-knife is a re-sampling technique
In the matrix data can be entered row-wise only.
Q.2 Discuss matrix operations in MS-Excel.
ii) Write algorithm to estimate e.
06
Write short notes on the following
Estimation of using Monte Carlo technique
ii) Regula-Falsi Method
08
Q.3 State and prove the result to obtain Poisson random variates
using iid variates.
07
Explain the method of generating random numbers from

using normal variates.
07
Q.4 Explain the method of obtaining random numbers from
using congruential random number generator.
07
Explain any two methods to check uniformity of random
numbers.
07
Q.5 Explain the method of generating random numbers from
bivariate normal distribution.
07
Explain how to obtain frequency table, proportions and marginal
frequencies using R-software.
07
Q.6 Discuss Jack-knife technique. Obtain Jack-knife estimator of
if X1, X2,….,Xn) are iid
07
Discuss Bootstrap method of bias reduction. State its
assumptions.
07
Q.7 Describe Newton-Raphson method to find root of the equation

07
Discuss the following methods of numerical integration
Simpson's 1/3rd rule
ii) Trapezoidal rule