Here again is the probability distribution of the number of defective parts in an hour in Yves' production line:
Y. 0 1 2 3 4
P(Y=y) .05 .05 .10
.75 .05
What is the expected amount of defective parts in an hour in Yves'
production line?
2
.2
2.7
.54
Here again is the probability distribution of the number of defective parts in an hour in...
The partial probability distribution of X, the number of defective tires on a randomly selected automobile checked at a certain inspection station, is given below. If one of those automobiled is equally likely to have either 2 or 3 defective tires, what is P[(X = 2) U (X = 4)] = P(2 U 4)? x 0 1 2 3 4 P(x) .54 .12 .20
1. Let the random variable X represent the number of defective parts for a machine when 3 parts are sampled from a production line and tested. The following is the probability distribution of X 0 1 T0.38 2 3 х 0.10 0.01 0.51 (a) Compute expected value of the random variable X c) ) S 0. the l (b) Compute standard deviation of the random variable X (c) If g(X) = 2X +3, what is the expected value of g(X)?...
1. A lightbulb factory produces 567 lightbulbs every hour. Approximately 1.96% of the lightbulbs are defective, and do not work. What is the expected number of defective bulbs produced in an hour? The answer does not need to be an integer. 2. A lightbulb factory produces 956 lightbulbs every hour. Approximately 2.91% of the lightbulbs are defective, and do not work. What is the standard deviation of the number of defective bulbs produced in an hour? 3.A call center receives...
Use the probability distribution to complete parts (a) through (d) below. The probability distribution of number of televisions per household in a small town x 0 1 2 3 P(x) 0.03 0.13 0.31 0.53 (a) Find the probability of randomly selecting a household that has one or two televisions. The probability is nothing.
Use the probability distribution to complete parts (a) through (d) below. The probability distribution of number of televisions per household in a small town x 0 1 2 3 P(x) 0.02 0.19 0.28 0.51 (a) Find the probability of randomly selecting a household that has one or two televisions.
Parts coming off an assembly line have a 1% chance of being defective. If3 parts are randomly chosen from this line and X is the number of defective parts a. Compute the probability function f(x) for X. b. What is the probability that at least one of the three is defective? Parts coming off an assembly line have a 1% chance of being defective. All of the parts coming off the line are inspected. Let X be the number of...
Five percent (0.05) of the parts produced by a machine are defective. Twenty(20) parts are selected at random for study. A. what is the probability that more than 4 parts are defective? B. what is the probability that exactly 3 parts are defective? C. what is the expected number of defective parts in the sample? D. What is the variance and standard deviation of defective parts in the sample?
Approximately 20% of the lightbulbs produced by a company are defective (and the rest are non-defective). Suppose 3 lightbulbs are selected randomly. Let Y be the random variable showing number of defective lightbulbs. a)Complete the following probability distribution given in the following table. (You can use binomial distribution formula) y p(y) 0 0.512 1 2 3 0.008 Find the mean and variance of the above probability distribution
Use the probability distribution to find probabilities in parts (a) through (c). The probability distribution of number of dogs per household in a small town Dogs 0 1 2 3 4 5 Households Use the probability distribution to find probabilities in parts (a) through (c). The probability distribution of number of dogs per household in a small town Dogs 0 1 2 3 4 5 Households 0.6780.678 0.1940.194 0.0790.079 0.0270.027 0.0170.017 0.005
3. A manufactured part is defective with probability 1/6. Assume that n (a large number) parts are produced each day in a factory and X is the number of defective parts. (a) Compute EX. (b) Find the approximate probability that X differs from its expectation by less than 10%, in terms of n and Φ. (c) How large should n be so that the probability in (b) is larger than 0.99? 4. Suppose that it takes an engineer T hours...