Chapter 6 Discrete Probability Distributions Ch 6.1 Discrete Random Variables Objective A: Discrete Probability Distribution A1. Distinguish between Discrete and Continuous Random Variables Example 1: Determine whether the random variable is discrete or continuous.State the possible values of the random variable. (a) The number of fish caught during the fishing tournament. (b) The distance of a baseball travels in the air after being hit. A2. Discrete Probability Distributions 1 Example 1: Determine whether the distribution is a discrete probability distribution. If not, state why. Example 2: (a) Determine the required value of the missing probability to make the distribution a discrete probability distribution. (b) Draw a probability histogram. 2 Objective B: The Mean and Standard Deviation of a Discrete Random Variable Example 1: Find the mean, variance, and standard deviation of the discrete random variable x . (a) Mean x [ x P( x)] P( x) 0.073 0.117 0.258 0.322 0.230 x 0 1 2 3 4 (b) Variance 0 P( x) 0.073 1 0.117 2 0.258 3 4 0.322 0.230 x ---> x P( x) Use the definition formula x 2 [( x x )2 P( x)] Formula (2a) in the textbook 3 Chapter 6.2 The Binomial Probability Distribution Objective A : Criteria for a Binomial Probability Experiment The binomial probability distribution is a discrete probability distribution that obtained from a binomial experiment. Example 1: Determine which of the following probability experiments represents a binomial experiment.If the probability experiment is not a binomial experiment, state why. (a) A random sample of 30 cars in a used car lot is obtained, and their mileages recorded. (b) A poll of 1,200 registered voters is conducted in which the repondents are asked whether they believe Congress should reform Social Security. 4 Objective B : Binomial Formula Let the random variable x be the number of successes in n trials of a binomial experiment. Example 1: A binomial probability experiment is conducted with the given parameters. Compute theprobability of x successes in the n independent trials of the experiment. n 15, p 0.85, x 12 (Round to four decimal places as needed) 5 Example 2: (a) Use StatCrunch to compute a Binomial table of n 4 and p 0.65 . First state the possible values of the random variable x then select Stat Calculators Binomial and input n 4 and p 0.65 then each x value. x P( x) (b) Use the Binomial table from part (a), find P ( x 2) . (c) Use the Binomial table from part (a), find P(0 x 3) . Objective C : Binomial Table by StatCunch Example 1: Use StatCrunch with Binomial Distribution to find P ( x 6) with n 12 and p 0.4 . Example 2: According to the American Lung Association, 90% of adult smokers started smoking before turning 21 years old. Ten smokers 21 years old or older are randomly selected, and the number of smokers who started smoking before 21 is recorded. (a) Explain why this is a binomial experiment. (b) Use StatCrunch to find the probability that exactly 8 of them started smoking before 21 years of age. 6 (c) Use StatCrunch to find the probability that at least 8 of them started smoking before 21 years of age. (d) Use StatCrunch to find the probability that between 7 and 9 of them, inclusive, started smoking before 21 years of age. Objective D : Mean and Standard Deviation of a Binomial Random Variable Example 1: A binomial probability experiment is conducted with the given parameters. Compute the mean and standard deviation of the random variable x . n 9 p 0.8 Example 2: According to the 2005 American Community Survey, 43% of women aged 18 to 24 were enrolled in college in 2005. (a) For 500 randomly selected women ages 18 to 24 in 2005, compute the mean and standarddeviation of the random variable x , the number of women who were enrolled in college. (b) Interpret the mean. 7 What role do p and n play in the shape of a binomial distribution? Study the textbook pg. 318-319. In Other Words Provided that np(1 p) 10, the interval 2 to 2 represent the “usual” observations. Observations outside this interval may be considered unusual. 8