M.C.A. (Semester III) (CBCS) Examination Nov/Dec-2018
Science
COMPUTER ORIENTED STATISTICS
Time: 2½ Hours Max. Marks: 70
Instructions: Q. No. and Q. No.(2) are compulsory.
Attempt any three from Q. No. to Q. No.
Figures to the right indicate full marks.
Q.1 Choose the correct alternative. 05
The most frequent value in the data is called as
mean mode
median none of these
If intersection of two sets is empty, they the sets are called as
exclusive exhaustive
simple complex
The mean of binomial random variable is
np n
p
If a r.v. X is symmetric about 0.5, then the median of X is
1 0
0.5 None of these
If is a probability mass function, then
0 for all x 1
Both a and b None of the above
Fill in the blanks. 05
Variance of binomial distribution is
The square root of variance is called as
In case of tossing a fair coin, the probability of head is
The distribution is a generalization of Bernoulli distribution.
If mean of Bernoulli distribution is then its variance is
State whether the following statement are True or False. 04
For normal distribution, mean is always same as variance.
Variance of the data is always non-negative.
Negative binomial distribution is a particular case of binomial distribution.
If X is number of successes out of n trials, then X is a binomial random
variable.
Q.2 Answer the following. 06
Describe frequency distribution.
State Baye's theorem.
Write short notes on the following. 08
Conditional Probability
Regression
Q.3 Define a random variable. Also describe Geometric and hyper geometric
random variables.
07
Define Poisson distribution. Also find its mean. 07
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Q.4 Define probability density function (pdf). Also give pdf of exponential and
normal random variables.
07
Find mean and variance of exponential distribution. 07
Q.5 Describe the technique of obtaining random numbers from 07
Describe the fitting of exponential curve to the given data. 07
Q.6 Define the following. 07
Skewness
Kurtosis
Sample Space
What do you mean by central tendency? Describe various measures of central
tendency.
07
Q.7 State and explain addition and multiplication theorems of probability. 07
Define inverse of c.d.f. Also state its relation with uniform variate. 07