436 CHAPTER 8 Hypothesis Testing 5.Lightning Deaths The accompanying bar chart shows the numbers of lightning strike deaths broken down by gender for a recent period of eleven years. What is wrong with the graph? 6.Lightning Deaths The graph in Cumulative Review Exercise 5 was created by using data consisting of 242 male deaths from lightning strikes and 64 female deaths from lightning strikes. Assume that these data are randomly selected lightning deaths and proceed to test the claim that the proportion of male deaths is greater than 1>2. Use a 0.01 significance level. Any explanation for the result? 7.Lightning Deaths The graph in Cumulative Review Exercise 5 was created by using data consisting of 242 male deaths from lightning strikes and 64 female deaths from lightning strikes. Assume that these data are randomly selected lightning deaths and proceed to construct a 95% confidence interval estimate of the proportion of males among all lightning deaths. Based on the result, does it seem feasible that males and females have equal chances of being killed by lightning? 8.Lightning Deaths Based on the results given in Cumulative Review Exercise 6, assume that for a randomly selected lightning death, there is a 0.8 probability that the victim is a male. a. Find the probability that three random people killed by lightning strikes are all males. b. Find the probability that three random people killed by lightning strikes are all females. c. Find the probability that among three people killed by lightning strikes, at least one is a male. d. If five people killed by lightning strikes are randomly selected, find the probability that exactly three of them are males. e. A study involves random selection of different groups of 50 people killed by lightning strikes. For those groups, find the mean and standard deviation for the numbers of male victims. f. For the same groups described in part (e), would 46 be a significantly high number of males in a group? Explain. Technology Project Bootstrapping and Robustness Consider the probability distribution defined by the formula P1x2 = 3x2 1000 where x can be any value between 0 and 10 inclusive (not just integers). The graph of this probability distribution on the next page shows that its shape is very far from the bell shape of a normal distribution. This probability distribution has parameters m = 7.5 and s = 1.93649. Listed below is a simple random sample of values from this distribution, and the normal quantile plot for this sample is shown. Given the very non-normal shape of the distribution, it is not surprising to see the normal quantile plot with points that are far from a straight-line pattern, confirming that the sample does not appear to be from a normally distributed population. 8.69 2.03 9.09 7.15 9.05 9.40 6.30 7.89 7.98 7.67 7.77 7.17 8.86 8.29 9.21 7.80 7.70 8.12 9.11 7.64
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