There are 1000 pupils in a school. Find the probability that exactly 3 of them have their birthdays on 1 January, by using (a) B(1000, 1/365) (b) Po(1000/365) There are 5000 students in a university. Calculate the II probability that exactly 15 of them have their birthdays on 1 January by using (a) suitable binomial distribution approximation III (a) In a certain school, 30% of the students are in the age (b) suitable Poisson group 16-19. i Ten students are chosen at random. What is the probability that fewer than four of them are in the 16-19 age group? ii if the ten students were chosen by picking ten who were sitting together at lunch, explain why a binomial distribution might no longer have been suitable. (b) A factory makes large quantities of coloured sweets and it is known that on average 20% of the sweets are coloured green. A packet contains 20 sweets. Assuming that the packet forms a random sample of the sweets made by the factory, calculate the probability that exactly seven of the sweets are green. If you knew that, in fact, the sweets could have been green, red, orange or brown, would it have invalidated your calculation? IV (a) Find the mean of the random variables X and Y which have the following probability distribution (i) P(X = x) (ii) P(Y = y) (b) The random variable T has the probability distribution given in the following table 1 2 3 4 1/8 3/8 1/8 ¼ 1/8 X y -2 -1 1 2 3 0.15 0.25 0.3 0.05 0.2 0.05 1 3 4 6. 7 0.1 P (T= t) Find E (T) and Var (T) V Given that X - N (44, 25), find s, t, u and v (a) P (X< s) = 0.9808 (b) P(X > t) = 0.7704 (c) P(X > u) = 0.0495 (d) P(X< v) = 0.3336 0.2 0.1 0.2 0.1 0.2 0.1 VI X has normal distribution and P(X>73.05) = 0.0289. Given that the variance of the distribution is 18, find the mean.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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