PRACTICE PROBLEMS - DISTRIBUTION
1. Determine the binomial distribution for which the mean is four and variance three.
Also find its mode?
2. A shipment of 20 tape recorders contains 5 defectives find the standard deviation of
the probability distribution of the number of defectives in a sample of 10 randomly
chosen for inspection?
3. A department has 10 machines which may need adjustment from time to time during
1
the day. Three of these machines are old, each having a probability of
of needing
11
1
adjustment during the day and 7 are new, having corresponding probabilities of
.
21
Assuming that no machine needs adjustments twice on the same day, determine the
probabilities that on a particular day. (i) just 2 old and no new machines need
adjustment.(ii) if just 2 machines need adjustment, they are of the same type.
4
4. The mean and variance of binomial distribution are 4 and
respectively. Find P(X ³
3
1)?
5. If the probability that a new-born child is a male is 0.6, find the probability that in a
family of 5 children there are exactly 3 boys?
6. Out of 800 families with 5 children each, how many would you expect to have (i) 3
boys (ii) 5 girls and
(iii) either 2 or 3 boys? Assuming that equal
probabilities for girls and boys.
7. If the probability of a defective bolt is 0.1, find (i) the mean and (ii) the standard
deviation for the distribution of defective bolts in a total of 400?
8. Find the probability that in five tosses of a fair die a 3 appears (i) at no times (ii) four
times?
9. Find the probability that in a family of 4 children there will be (i) at least 1 boy and
(ii) at least 1 boy and 1 girl?
10. Find the probability of getting at least 4 heads in 6 tosses of a fair coin?
11. In 256 sets of 12 tosses of a coin, in how many cases one can expect eitght heads and
4 tails?
12. The mean and variance of a binomial variate X with parameters “n” and p are 16 and
8. Find (i) p(X = 0) (ii) p(X = 1) and (iii) p(X ³ 2).
13. If 20% of the memory chips made in a certain plant are defective what are the
probabilities that in a lot of 100 randomly chosen for inspection ( i) at most 15 will be
defective ( ii) exactly 15 will be defective.
14. The mean weight of 500 male students at a certain college is 75 kg and the standard
deviation is 7 kg. Assuming that the weights are normally distributed. Find how many
students weigh (i) between 60 and 78 kg (ii ) more than 92 kg.
15. Find the probability of getting 3 and 6 heads inclusive in 10 tosses of a fair coin by
using (i) Binomial distribution (ii) the normal approximation to the binomial
distribution.
16. If the masses of 300 students are normally distributed with mean 68.0 kg and standard
deviation 3.0 kg, how many students have masses:
(i) 72 kgs (ii) £ 64 kgs (iii) 65 £ X £ 71 kg inclusive
17. If the probability that an individual suffers a bad reaction due to a certain injection is
0.001, determine the probability that out of 2000 individuals (i) exactly 3 (ii) more than
2 individuals will suffer a bad reaction?
18. A manufacturer of cotter pins knows that 5% of his product is defective. If he sells
cotter pins in boxes of 100 and guarantees that not more than 10 pins will be defective,
what is the approximate probability that a box will fail to meet the guaranteed quality?
19. 10% of the bolts produced by a certain machine turn out to be defective. Find the
probability that in a sample of 10 tools selected at random exactly two will be defective
using (i) binomial distribution (ii) Poisson distribution and comment upon the result?
20. A hospital switch board receives an average of 4 emergency calls in a 10 min. interval.
What is the probability that (i) there are at the most 2 emergency calls and (ii) there are
exactly 3 emergency calls in a 10 min. interval?
21. A rent a car firm has two cars which it hires from day to day. The number of demands
for a car on each day is distributed as a Poisson variate with mean 1.5. Calculate the
proportion of days on which (i) neither car is used (ii) some demand is refused?
22. In a Poisson distribution (P.D.), P(X = 0) = 2 P(X = 1), then find P(X = 2)?
23. In a factory which turns out razor blades, there is a chance of 0.002 for any blade to be
defective. The blades are supplied in packets of 10 each. Using Poisson distribution,
Calculate the approximate number of packets containing no defective, one defective
and two defective blades if there are 10,000 such packets?
24. The probability of getting no misprint in a page of a book is e-4. Determine the
probability that a page of a book contains more than 2 misprints?
25. If a bank receives on an average 6 bad cheques per day, what are the probabilities that
it will receive (i) four bad cheques on any given day (ii) 10 bad cheques on any two
consecutive days.
26. The incidence of occupational disease in an industry is such that the workmen have a
10% chance of suffering from it. What is probability of 7, five or more will suffer from
it?
27. A car hire firm has two cars which it hires out day by day. The number of demands for
a car on each day is distributed as a Poisson distribution with mean 1.5. calculate the
proportion of days. (i) on which there is no demand (ii) on which demand is refused
(e-5 = 0.2231)?
28. If a random variable has a Poisson distribution such that P(1) = P(2) find (i) mean of
the distribution (ii) P(4) ?
29. If the probability of a bad reaction from a certain injection is 0.001, determine the
chance that out of 2,000 individuals more than two will get a bad reaction?
30.
If 3 % of the electric bulbs manufactured by a company are defective, find the
probability that in a sample of 100 bulbs
(i)
0 (ii) 1 (iii) 4
30a. Ten present of the tools produced in a certain manufacturing process turn out
to be defective. Find the probability that in a sample of 10 tools chosen at
rando.exactly two will be defective by using the Poisson approximation to the
binomial distribution?
31. X is normally distributed with mean 12 and S.D = 4then find (i) P(0£X£12) (ii) P(X £
20) (iii) P(X ³ 20) (iv) if P(X > C) = 0.24.
32. Xis a normal variate with mean 30 and standard deviation 5. Find the probabilities
33.
34.
35.
36.
that (i) 26 £ X £ 40 (ii) X ³ 45.
A random variable has normal distribution with µ = 62.4. find its standard deviation if
the probability is 0.20 that it will take on a value greater than 79.2.
find the probabilities that a random variable having a standard normal distribution will
take on a value (i) between 0.87 and 1.28 (ii) between – 0.34 and 0.62.
In a normal distribution (N.Dn) 31% of the items are under 45 and 8% are over 63.
Find the mean and variance of the distribution
In a normal distribution (N.Dn), 7% of the items are under 35 and 89% are over 64.
Find the mean and variance of the distribution.