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Whatever may be the parameter of __________ distribution, it has same shape.
The no. of methods for fitting the normal curve is
____________ distribution is symmetrical around t = 0
As the degree of freedom increases, the ________ distribution approaches the Standard Normal distribution
_________ distribution is asymptotic to the horizontal axis.
________ distribution has a greater spread than Normal distribution curve
In Binomial Distribution if n is infinitely large, the probability p of occurrence of event’ is close to _______ and q is close to _________
Poisson distribution approaches a Normal distribution as n
If neither p nor q is very small but n sufficiently large, the Binomial distribution is very closely approximated by _________ distribution
For discrete random variable x, Expected value of x (i.e E(x)) is defined as the sum of products of the different values and the corresponding probabilities.
For a probability distribution, —————— is the expected value of x.
_________ is the expected value of (x – m)2 , where m is the mean.
The probability distribution of x is given below :
Q. Mean is equal to
For n independent trials in Binomial distribution the sum of the powers of p and q is always n , whatever be the no. of success.
In Binomial distribution if n = 4 and p = 1/3 then the value of variance is
In Binomial distribution if mean = 20, S.D.= 4 then q is equal to
If in a Binomial distribution mean = 20 , S.D.= 4 then p is equal to
If is a Binomial distribution mean = 20 , S.D.= 4 then n is equal to
Poisson distribution is a ___________ probability distribution .
No. of radio- active atoms decaying in a given interval of time is an example of
__________ distribution is sometimes known as the “distribution of rare events“.
The probability that x assumes a specified value in continuous probability distribution is
In Normal distribution mean, median and mode are
In Normal distribution the quartiles are equidistant from
In Normal distribution as the distance from the ___________ increases, the curve comes closer and closer to the horizontal axis .
A discrete random variable x follows uniform distribution and takes only the values 6, 8, 11, 12, 17The probability of P( x = 8) is
A discrete random variable x follows uniform distribution and takes the values 6, 9, 10, 11, 13The probability of P( x = 12) is
A discrete random variable x follows uniform distribution and takes the values 6, 8, 11, 12, 17
Q. The probability of P is
A discrete random variable x follows uniform distribution and takes the values 6, 8, 10, 12, 18
Q. The probability of P( x < 12) is
A discrete random variable x follows uniform distribution and takes the values 5, 7, 12, 15, 18
Q. The probability of P( x > 10) is
The probability density function of a continuous random variable is defined as follows :
Q. f(x) = c when , otherwise The value of c is
A continuous random variable x has the probability density fn.f(x) = ½ –ax ,
Q. When ‘a’ is a constant. The value of ‘ a’ is
A continuous random variable x follows uniform distribution with probability density function
An unbiased die is tossed 500 times.The mean of the no. of ‘Sixes’ in these 500 tosses is
An unbiased die is tossed 500 times. The Standard deviation of the no. of ‘sixes’ in these 500 tossed is
A random variable x follows Binomial distribution with mean 2 and variance 1.2.Then the value of n is
A random variable x follows Binomial distribution with mean 2 and variance 1.6 then the value of p is
“The mean of a Binomial distribution is 5 and standard deviation is 3”